WO2016043007A1 - Simple method for predicting tree growth - Google Patents
Simple method for predicting tree growth Download PDFInfo
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- WO2016043007A1 WO2016043007A1 PCT/JP2015/074165 JP2015074165W WO2016043007A1 WO 2016043007 A1 WO2016043007 A1 WO 2016043007A1 JP 2015074165 W JP2015074165 W JP 2015074165W WO 2016043007 A1 WO2016043007 A1 WO 2016043007A1
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- soil
- growth
- tree
- color
- analyzing
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- 238000000034 method Methods 0.000 title claims abstract description 105
- 230000012010 growth Effects 0.000 title claims abstract description 78
- 239000002689 soil Substances 0.000 claims abstract description 119
- 235000015097 nutrients Nutrition 0.000 claims abstract description 47
- 238000004458 analytical method Methods 0.000 claims abstract description 30
- 238000002329 infrared spectrum Methods 0.000 claims description 14
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 10
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 8
- 229910052799 carbon Inorganic materials 0.000 claims description 8
- 229910052757 nitrogen Inorganic materials 0.000 claims description 5
- 238000004566 IR spectroscopy Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 description 15
- 238000011088 calibration curve Methods 0.000 description 10
- 238000012360 testing method Methods 0.000 description 8
- 238000005070 sampling Methods 0.000 description 6
- 238000009499 grossing Methods 0.000 description 5
- 238000001228 spectrum Methods 0.000 description 5
- 244000166124 Eucalyptus globulus Species 0.000 description 4
- 241001124569 Lycaenidae Species 0.000 description 4
- 230000003993 interaction Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 241000894007 species Species 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000000491 multivariate analysis Methods 0.000 description 2
- 235000016709 nutrition Nutrition 0.000 description 2
- 230000035764 nutrition Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010998 test method Methods 0.000 description 2
- 229910021654 trace metal Inorganic materials 0.000 description 2
- USFZMSVCRYTOJT-UHFFFAOYSA-N Ammonium acetate Chemical compound N.CC(O)=O USFZMSVCRYTOJT-UHFFFAOYSA-N 0.000 description 1
- 240000007472 Leucaena leucocephala Species 0.000 description 1
- 235000010643 Leucaena leucocephala Nutrition 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004720 fertilization Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 238000010238 partial least squares regression Methods 0.000 description 1
- 230000008635 plant growth Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000004856 soil analysis Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
- 229910052725 zinc Inorganic materials 0.000 description 1
Images
Classifications
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G7/00—Botany in general
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G23/00—Forestry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/22—Improving land use; Improving water use or availability; Controlling erosion
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
- Y02P60/40—Afforestation or reforestation
Definitions
- the present invention relates to a simple method for predicting tree growth.
- Non-Patent Document 1 A method for evaluating the growth amount of a tree itself (Patent Document 1), a method for evaluating the soundness of a tree (Patent Document 2), and a method for quantifying a wood component of a tree (Patent Document 3) have been reported. Yes. Moreover, paying attention to soil, a method for predicting soil properties and components from a light spectrum (near infrared spectrum) as a soil analysis method has also been reported (Patent Document 4). Further, Non-Patent Document 1 describes a method for estimating a past growth amount in order to predict a stand growth amount.
- JP 2008-79549 A JP 2007-166967 A JP-A-11-258161 JP 2006-38511 A
- Patent Documents 1 to 4 enable prediction of tree growth before planting.
- the method described in Patent Document 4 is merely a method for predicting soil characteristics and components, and the document does not describe the growth of trees or the prediction thereof.
- Non-Patent Document 1 describes a method for estimating a past growth amount, and does not provide a method for predicting the growth of trees. In other words, no technology has been known so far that enables tree growth prediction before planting.
- an object of the present invention is to provide a method for predicting the growth of trees before planting.
- the present inventors searched for an index that contributes to tree growth prediction, and found that the above-mentioned problem may be solved by a specific index that has not been used for the growth prediction so far.
- the present invention was completed. That is, the present invention relates to at least the following inventions: [1] A method for predicting the degree of growth of a tree when planted on the target land, including the following steps: (A) The process of acquiring the data regarding the growth about the several trees planted in the vicinity of the said land (b) About the soil under each of the said trees, soil color, soil characteristics, and / or soil nutrients Step of analyzing (c) Step of obtaining an explanatory variable from the result of the analysis (d) Step of obtaining a calibration formula with data relating to the growth of the tree using the explanatory variable (e) Analyzing the color, soil characteristics and / or soil nutrients, applying the analysis results to the calibration formula, and calculating and predicting the degree of growth of the trees when the trees are planted on the land.
- [6] The method according to [5], further comprising analyzing the color of the soil.
- An afforestation method comprising predicting the degree of growth of a tree in a planned plantation site before and / or after planting a tree for planting by the method according to any one of [1] to [6] above .
- the system according to [8] further including equipment having a function of predicting a degree of tree growth by converting a measurement value obtained using the measurement device into a tree height.
- the system according to [8] or [9] wherein the mobile device is a ground mobile device or an airborne mobile device.
- FIG. 1 is a scheme showing an outline of the present invention. It is a figure which shows the correlation with the predicted value of tree growth obtained in the method of this invention using the color of soil, and an actual value.
- FIG. 2A shows a case where a linear expression is created by a generalized linear model using only L *, a * and b * without considering pH and EC, and FIG. The correlation between the predicted height obtained from the prediction formula created including the action and the actually measured height is shown.
- FIG. 2C further shows the results considering the measured values of pH and EC. It is a figure which shows the correlation with the predicted value of tree growth obtained in the method of this invention using the characteristic of soil, and an actual value. It is a figure which shows infrared spectrum data (FIG.
- FIG. 4A shows the result by the nutrient 1 method (FIG. 5A) and the nutrient 2 method (FIG. 5B).
- FIG. 5B shows the result of the prediction of the tree growth by the method of this invention using the characteristic (infrared spectroscopy spectrum) of a soil about eucalyptus.
- the vertical axis represents the actual value of the position index (an index representing the average tree height), and the horizontal axis represents the predicted value of the position index.
- analyze (perform) the soil means to obtain information on the color of the target soil, the characteristics of the soil, and / or nutrients contained in the soil (soil nutrients). Also.
- growth of (tree) means growth quantified by the tree height of the tree, for example, but may be other quantitative items (for example, height under branch, thickness (chest height diameter)).
- At least the present invention relates to a method for predicting the degree of growth of a tree when the tree is planted on the target land, including the following steps: (A) The process of acquiring the data regarding the growth about the several trees planted in the vicinity of the said land (b) About the soil under each of the said trees, soil color, soil characteristics, and / or soil nutrients Step of analyzing (c) Step of obtaining an explanatory variable from the result of the analysis (d) Step of obtaining a calibration formula with data relating to the growth of the tree using the explanatory variable (e) Analyzing the color, soil characteristics and / or soil nutrients, applying the analysis results to the calibration formula, and calculating and predicting the degree of growth of the trees when the trees are planted on the land.
- the procedure for the example of the present invention is conceptually shown in FIG. That is, the present invention analyzes soil color, soil characteristics, soil nutrients, etc., creates a calibration curve model and / or calibration formula from the analysis results, and uses the calibration curve model and / or calibration formula. Therefore, it is based on estimation (prediction) of tree growth.
- the method of the present invention includes (1) a step of acquiring data relating to tree growth such as the height of the tree, and (2) the soil under the tree is collected and used for analysis, or on the spot.
- the process for obtaining the analysis data is performed in common.
- the method of the present invention includes at least the following steps: (A1) An image of soil is acquired by a color information acquisition device such as a scanner, a digital camera, or a soil color meter, and average color information (RGB, etc.) of the sample is obtained by image analysis software. (B1) The above color information is converted into CIE Lab color space information established by CIE (Commission Internationale de l'Eclairage) (Conversion formulas between each color information such as RGB to CIE Lab are publicly available) Http://www.cs.rit.edu/ ⁇ ncs/color/t_convert.html).
- CIE Commission Internationale de l'Eclairage
- (C1) A calibration curve model and / or calibration formula is created by a generalized linear model using L *, a *, and b *, which are variables for the obtained color information, as explanatory variables and tree growth data as an objective variable.
- (D1) Tree growth is estimated (predicted) using the calibration curve model and / or calibration equation.
- the number of variables is not limited, but more accurate prediction may be possible by increasing the number. For example, the method using 3 or 4 variables is preferable, and the method using 6 or more variables is more preferable.
- the variables used are typically the above L *, a * and b *, where the calibration equation is a linear equation. A method of further using La, Lb and ab in addition to these three variables is preferable.
- the color information acquisition device such as the scanner, the digital camera, or the earth color meter is not limited as long as it provides the color information of the soil.
- the portable small color information acquisition device is mounted on a mobile device so as to acquire color information about the soil of the entire plantation when preparing the ground.
- a system including a mobile device further having a function of converting / analyzing the obtained color information, converting the analysis result into a tree height, and estimating / predicting the growth degree of the tree More preferably used for carrying out the method of the invention.
- a system that can modify the conversion formula used when converting the analysis result into tree height according to the environmental conditions is even more preferable.
- Environmental conditions include location conditions such as soil type, climate, season, topography, and microtopography along with conditions such as weather.
- ground mobile devices such as tractors and forestry machines (harvesters, skidder, etc.), and aerial mobile devices, such as manned aircraft and unmanned aircraft (drone, radio controlled helicopter, etc.) Is exemplified.
- a tractor and a harvester having a regular movement pattern are preferable, and an unmanned aircraft is also preferable because the operation is simple.
- Soil nutrients are thought to affect tree growth, but there are many types of nutrients necessary for tree growth, and no method has yet been established to clarify the relationship with growth. Rather, measuring all nutrients is often time-constrained. In addition, it is not always easy to predict the growth of trees by nutrients because various nutrients are related to the growth of trees, and in addition to the required nutrients, it may become a growth limiting factor if it is excessive. There are circumstances. On the other hand, tree growth is estimated by obtaining soil infrared spectrum information reflecting soil characteristics (total carbon content, EC (electrical conductivity), exchangeable Ca content, exchangeable Mg content, exchangeable K content, etc.). Therefore, it is possible to estimate (predict) tree growth with high accuracy.
- the method of the present invention comprises at least the following steps: (A2) The infrared spectroscopic spectrum data of the soil sample is obtained by the infrared spectroscopic measurement device. (B2) The infrared spectrum data is standardized. (C2) Using the tree height as an explanatory variable, multivariate analysis PLS analysis is performed, and a calibration curve model and / or calibration formula is created. (D2) Tree growth is estimated (predicted) by the calibration curve model and / or calibration equation.
- infrared spectrum data of 350 to 7800 cm ⁇ 1 of a plurality of standard soil samples is acquired using an infrared spectroscopic device, and 800 to 1200 cm ⁇ 1 is selected as an optimal wave number range, and 4 cm ⁇
- a smoothing process may be performed for each and a standardization process of spectrum data may be performed.
- a calibration curve model is created by PLS regression using tree growth data results such as tree height. Using the calibration curve model, tree height growth is estimated (predicted) when trees are planted in a place where tree growth is unknown. Note that by performing the smoothing process every 4 cm ⁇ 1 , the most accurate prediction among the smoothing processes in the range of 2 cm ⁇ 1 to 16 cm ⁇ 1 is made.
- the soil characteristics reflected in the method those containing the total amount of carbon are preferable, and those containing the amount of trace components such as exchangeable Ca, Mg and K are more preferable. Moreover, you may reflect all Fe and / or all Si.
- a portable type including a digital camera and an infrared spectrum camera can be used. In the case of an infrared spectroscopic measurement device including a small portable device, the infrared spectroscopic measurement device is mounted on a mobile device so that color information about the soil of the entire plantation can be acquired when preparing the ground. What is done is preferable.
- the use of mobile equipment not only enables accurate and efficient acquisition of infrared spectral data information over a wider area, but in the case of ground mobile equipment, redness of soil that exists below the surface. This is because the outside spectrum data information can be acquired.
- equipment that has the function to convert and analyze the obtained infrared spectrum data information, convert the analysis results into tree heights, and estimate and predict the degree of tree growth
- a system including the mobile equipment provided is more preferably used for carrying out the method of the invention.
- a system that can modify the conversion formula used when converting the analysis result into tree height according to the environmental conditions is even more preferable.
- the equipment may constitute a part of the measuring apparatus or may be a separate equipment independent of the measuring apparatus.
- ground mobile equipment such as tractors and forestry machines (harvesters, skidder, etc.), and aeronautical mobile type such as manned aircraft and unmanned aircraft (drones, radio controlled helicopters, etc.)
- tractors and forestry machines harvesters, skidder, etc.
- aeronautical mobile type such as manned aircraft and unmanned aircraft (drones, radio controlled helicopters, etc.)
- manned aircraft and unmanned aircraft drones, radio controlled helicopters, etc.
- an unmanned aircraft is also preferable because the operation is simple.
- a color information acquisition device is mounted in addition to the infrared spectroscopic measurement device, it is preferable because tree growth can be predicted more precisely.
- the method of the present invention comprising analyzing soil nutrients includes at least the following steps: (A3) The amount (ratio) of exchangeable trace metal elements (Ca, K, Mg) by extraction of total carbon, total nitrogen, and ammonia acetate is measured, and the soil pH and electrical conductivity (EC) are also measured. (B3) Using the measurement values of nutrients of these soils as explanatory variables, and using the measurement results of tree growth such as tree height, a standardized linear model and / or a calibration equation for estimating tree growth using a generalized linear model create. In addition to the above-mentioned Ca, K, and Mg as exchangeable trace metal elements, it is preferable to take into account the analysis results of components such as Al and Na, since prediction with higher accuracy becomes possible. In view of the fact that components such as Al and Na have a relatively small influence on tree growth, it is unexpected that such an embodiment is preferable.
- the method of the invention which includes both analyzing soil properties and soil nutrients. Furthermore, it may be possible to predict with higher accuracy by further including analyzing the color of the soil. You may investigate the correlation about each explanatory variable about the color of a soil, a characteristic, and a nutrient, and various characteristic items (pH, EC, element amount), and you may use it for preparation of a calibration formula / calibration curve using the said item suitably.
- the area to which the method of the present invention is applied is not particularly limited, and can be applied from several hundred to several thousand ha to several ha or less, which is an area corresponding to one plantation.
- the sampling method / number may be changed according to the area.
- the number of samples of soil, the number of places (density) where sampling is performed, and the amount of soil to be sampled may be appropriately determined according to the accuracy of the land application and survey to be surveyed.
- the number of trees for which a growth survey is performed in advance as a sample may be determined according to the accuracy required for the surface survey and survey for the land to be surveyed.
- sampling In order to reduce the influence of the terrain, it is preferable to perform the sampling evenly from the parts of each shape. Furthermore, in order to eliminate the influence of the region / terrain, tree species, season, period (years), etc., sampling or the like may be performed with a relatively narrow plot. When forecasting in a relatively large area, it is possible to make predictions that incorporate these fluctuation factors by including data on these potential fluctuation factors in the analysis performed using the calibration curve. is there.
- the present invention also relates to an afforestation method including predicting the growth degree of a tree in a planned plantation site before and / or after planting a tree for planting by any one of the methods described above.
- the prediction method of this invention used for the said prediction is not limited, You may determine suitably considering time, a place, cost, etc. From the viewpoint of simplicity, a method using soil color is advantageous, and from a viewpoint of accuracy of prediction, a method using analysis results of soil nutrients is advantageous. In addition, the method using the characteristics of the soil has an advantage that it has a considerable combination of the advantages of both the method using the color of the soil and the method using the analysis result of the nutrients of the soil.
- the equipment used in performing the method of the present invention is not limited, but a system for performing the method of the present invention is preferred, including: (A) a measuring device for measuring soil color and / or soil properties; and (a) a mobile device equipped with the device.
- a system further comprising a device having a function of predicting the growth degree of a tree by converting a measurement value obtained using a measuring device into a tree height is more preferable, and the mobile device is a ground mobile device or A system that is an airborne instrument is even more preferred.
- a mobile device equipped with a color information acquisition device and / or an infrared spectroscopic measurement device as a moving means.
- Examples of such mobile equipment include ground mobile equipment such as tractors and forestry machines (harvesters and skidder), and airborne mobile equipment such as manned aircraft and unmanned aircraft (drone and radio control helicopters). Tractors and harvesters are preferred, and unmanned Aircraft are also preferred.
- ground mobile equipment such as tractors and forestry machines (harvesters and skidder)
- airborne mobile equipment such as manned aircraft and unmanned aircraft (drone and radio control helicopters). Tractors and harvesters are preferred, and unmanned Aircraft are also preferred.
- Example 1 Test for predicting tree growth (tree height) (1)
- Test site First-year acacia hybrid plantation in Long An province, Vietnam. -There was variation in the growth of trees (30 cm to 430 cm) within the same plot. The difference in soil composition was thought to be the cause, but it was not clearly understood.
- Method A the color of the soil
- Method B the characteristics of the soil
- Methodhod C the nutrient of the soil
- Example 2 Test (2) for predicting the growth (tree height) of trees A test similar to that of Example 1 was conducted on Papua New Guinea Eucalyptus (7th to 20th grade). [Test method] ⁇ Test site: 7th to 20th year eucalyptus plantation in Papua New Guinea. ⁇ The average height of the main forest tree at the 15th year is averaged with a position index of 4 and divided into 7 levels from 1 to 7 every 2 m, and the measured height of the tree is compared with the predicted value by infrared spectroscopy. It was decided. “Position index” is an index adopted in this test in order to more easily express the average tree height of a main forest tree in a certain plot.
- the present invention greatly contributes to the development of afforestation industry and related industries.
Abstract
The present invention provides a method for predicting the degree of growth of a tree when the tree is planted on land in question, the method including the following steps: (a) a step for acquiring data pertaining to growth of a plurality of trees planted in the vicinity of the land; (b) a step for analyzing soil color, soil characteristics, and/or soil nutrients with regards to the soil under each of the trees; (c) a step for obtaining an explanatory variable from the result of the analysis; (d) a step for using the explanatory variable to obtain a calibration formula with respect to data pertaining to growth of the trees; and (e) a step for analyzing the soil color, soil characteristics, and/or soil nutrients with regards to the soil on the land, fitting the analysis result into the calibration formula, and calculating and predicting the degree of growth of a tree if the tree is planted on the land.
Description
本発明は、樹木成長の簡易予測法に関する。
The present invention relates to a simple method for predicting tree growth.
ある土地への植林前に、植栽が予定される樹木について植栽後の成長予測を行うことは、植林の効率を確保するうえで必要である。しかしながら、これまでかかる成長の予測が行われることは実質的になかった。予測のための方法が存在しないことが、その一因である。林業は農業とは異なり、施肥が難しいため、土壌環境が重要であるにもかかわらず、適切な土壌の管理の方法さえ明らかではないといった背景もある。
It is necessary to ensure the efficiency of afforestation by predicting the growth after planting trees that are planned to be planted before planting on a certain land. However, there has been virtually no prediction of such growth so far. One of the reasons is that there is no method for prediction. Forestry, unlike agriculture, is difficult to fertilize, and even though the soil environment is important, there is a background that even the proper soil management method is not clear.
樹木の成長量自体を評価する方法（特許文献１）、樹木の健全度を評価する方法（特許文献２）や、樹木の木材成分を定量する方法（特許文献３）については、報告がなされている。
また土壌に着目し、土壌分析方法として光スペクトル（近赤外分光スペクトル）から土壌の特性及び成分等を予測する方法についても報告がなされている（特許文献４）。
さらに非特許文献１には林分成長量の予測のために、過去の成長量を推定するための方法について記載されている。 A method for evaluating the growth amount of a tree itself (Patent Document 1), a method for evaluating the soundness of a tree (Patent Document 2), and a method for quantifying a wood component of a tree (Patent Document 3) have been reported. Yes.
Moreover, paying attention to soil, a method for predicting soil properties and components from a light spectrum (near infrared spectrum) as a soil analysis method has also been reported (Patent Document 4).
Further, Non-PatentDocument 1 describes a method for estimating a past growth amount in order to predict a stand growth amount.
また土壌に着目し、土壌分析方法として光スペクトル（近赤外分光スペクトル）から土壌の特性及び成分等を予測する方法についても報告がなされている（特許文献４）。
さらに非特許文献１には林分成長量の予測のために、過去の成長量を推定するための方法について記載されている。 A method for evaluating the growth amount of a tree itself (Patent Document 1), a method for evaluating the soundness of a tree (Patent Document 2), and a method for quantifying a wood component of a tree (Patent Document 3) have been reported. Yes.
Moreover, paying attention to soil, a method for predicting soil properties and components from a light spectrum (near infrared spectrum) as a soil analysis method has also been reported (Patent Document 4).
Further, Non-Patent
しかしながら、特許文献１～４に開示されている技術的事項は、いずれも植林前に樹木の成長予測を可能とするものではない。特許文献４に記載の方法は土壌の特性及び成分等を予測する方法に留まり、同文献にも樹木の成長あるいはその予測については記載されていない。また非特許文献１は過去の成長量を推定するための方法について記載するものであって、樹木の成長予測の方法を与えるものではない。
すなわち、植林前に樹木の成長予測を可能とする技術はこれまで知られていない。
上記背景に鑑み、本発明は、植林前に樹木の成長を予測する方法を提供することを目的とした。 However, none of the technical matters disclosed inPatent Documents 1 to 4 enable prediction of tree growth before planting. The method described in Patent Document 4 is merely a method for predicting soil characteristics and components, and the document does not describe the growth of trees or the prediction thereof. Non-Patent Document 1 describes a method for estimating a past growth amount, and does not provide a method for predicting the growth of trees.
In other words, no technology has been known so far that enables tree growth prediction before planting.
In view of the above background, an object of the present invention is to provide a method for predicting the growth of trees before planting.
すなわち、植林前に樹木の成長予測を可能とする技術はこれまで知られていない。
上記背景に鑑み、本発明は、植林前に樹木の成長を予測する方法を提供することを目的とした。 However, none of the technical matters disclosed in
In other words, no technology has been known so far that enables tree growth prediction before planting.
In view of the above background, an object of the present invention is to provide a method for predicting the growth of trees before planting.
そこで本発明者らは樹木の成長予測に資する指標を探索したところ、これまで前記成長予測に用いられていなかった特定の指標により上記課題が解決される可能性があることを見出し、さらに鋭意研究を進めた結果本発明を完成するに至った。
すなわち、本発明は、少なくとも以下の発明に関する：
［１］
以下の工程を含む、対象である土地に樹木を植えた際の該樹木の成長度合いを予測する方法：
（ａ）上記土地の近傍に栽植された複数の樹木についての成長に関するデータを取得する工程
（ｂ）前記樹木のそれぞれの下の土壌について、土壌の色、土壌の特性及び／又は土壌の養分を分析する工程
（ｃ）該分析の結果から説明変数を得る工程
（ｄ）該説明変数を用いて、前記樹木についての成長に関するデータとの検量式を得る工程
（ｅ）上記土地の土壌について土壌の色、土壌の特性及び／又は土壌の養分を分析し、当該分析結果を前記検量式に当てはめて、上記土地に樹木を植えた際の該樹木の成長度合いを計算し、予測する工程。
［２］
土壌の色を分析することを含み、分析の結果得られる説明変数として少なくともＬ*、ａ*、ｂ*を含む上記［１］に記載の方法。
［３］
土壌の特性を赤外分光法により分析することを含み、分析の結果得られる説明変数として少なくとも赤外分光スペクトルを含む上記［１］に記載の方法。
［４］
土壌の養分を分析することを含み、分析の結果得られる説明変数として少なくとも全炭素、全窒素、交換性Ｃａ、交換性Ｋ及び交換性Ｍｇの量を含む上記［１］に記載の方法。
［５］
土壌の特性及び土壌の養分を分析することを含む上記［１］に記載の方法。
［６］
土壌の色を分析することをさらに含む上記［５］に記載の方法。
［７］
上記［１］～上記［６］のいずれかに記載の方法により、植林予定地における樹木の成長度合いを植林のための樹木の植栽前及び／又は植栽後に予測することを含む、植林方法。
［８］
以下を含む、上記［１］～［６］のいずれかに記載の方法を行うためのシステム：
（ア）土壌の色及び／又は土壌の特性を測定する測定装置
（イ）前記装置を搭載した移動機具。
［９］
測定装置を用いて得られた測定値を樹高に換算して樹木の成長度合いを予測する機能を備えた機材をさらに具備する、［８］に記載のシステム。
［１０］
移動機具が地上移動型機具又は空中移動型機具である、［８］又は［９］に記載のシステム。 Therefore, the present inventors searched for an index that contributes to tree growth prediction, and found that the above-mentioned problem may be solved by a specific index that has not been used for the growth prediction so far. As a result, the present invention was completed.
That is, the present invention relates to at least the following inventions:
[1]
A method for predicting the degree of growth of a tree when planted on the target land, including the following steps:
(A) The process of acquiring the data regarding the growth about the several trees planted in the vicinity of the said land (b) About the soil under each of the said trees, soil color, soil characteristics, and / or soil nutrients Step of analyzing (c) Step of obtaining an explanatory variable from the result of the analysis (d) Step of obtaining a calibration formula with data relating to the growth of the tree using the explanatory variable (e) Analyzing the color, soil characteristics and / or soil nutrients, applying the analysis results to the calibration formula, and calculating and predicting the degree of growth of the trees when the trees are planted on the land.
[2]
The method according to [1] above, comprising analyzing the color of the soil, and including at least L *, a *, b * as explanatory variables obtained as a result of the analysis.
[3]
The method according to [1] above, comprising analyzing soil characteristics by infrared spectroscopy, and including at least an infrared spectrum as an explanatory variable obtained as a result of the analysis.
[4]
The method according to [1] above, comprising analyzing soil nutrients, and comprising at least total carbon, total nitrogen, exchangeable Ca, exchangeable K, and exchangeable Mg as explanatory variables obtained as a result of the analysis.
[5]
The method according to [1] above, comprising analyzing soil characteristics and soil nutrients.
[6]
The method according to [5], further comprising analyzing the color of the soil.
[7]
An afforestation method comprising predicting the degree of growth of a tree in a planned plantation site before and / or after planting a tree for planting by the method according to any one of [1] to [6] above .
[8]
A system for performing the method according to any one of [1] to [6], including:
(A) Measuring device for measuring soil color and / or soil properties (A) A mobile device equipped with the device.
[9]
The system according to [8], further including equipment having a function of predicting a degree of tree growth by converting a measurement value obtained using the measurement device into a tree height.
[10]
The system according to [8] or [9], wherein the mobile device is a ground mobile device or an airborne mobile device.
すなわち、本発明は、少なくとも以下の発明に関する：
［１］
以下の工程を含む、対象である土地に樹木を植えた際の該樹木の成長度合いを予測する方法：
（ａ）上記土地の近傍に栽植された複数の樹木についての成長に関するデータを取得する工程
（ｂ）前記樹木のそれぞれの下の土壌について、土壌の色、土壌の特性及び／又は土壌の養分を分析する工程
（ｃ）該分析の結果から説明変数を得る工程
（ｄ）該説明変数を用いて、前記樹木についての成長に関するデータとの検量式を得る工程
（ｅ）上記土地の土壌について土壌の色、土壌の特性及び／又は土壌の養分を分析し、当該分析結果を前記検量式に当てはめて、上記土地に樹木を植えた際の該樹木の成長度合いを計算し、予測する工程。
［２］
土壌の色を分析することを含み、分析の結果得られる説明変数として少なくともＬ*、ａ*、ｂ*を含む上記［１］に記載の方法。
［３］
土壌の特性を赤外分光法により分析することを含み、分析の結果得られる説明変数として少なくとも赤外分光スペクトルを含む上記［１］に記載の方法。
［４］
土壌の養分を分析することを含み、分析の結果得られる説明変数として少なくとも全炭素、全窒素、交換性Ｃａ、交換性Ｋ及び交換性Ｍｇの量を含む上記［１］に記載の方法。
［５］
土壌の特性及び土壌の養分を分析することを含む上記［１］に記載の方法。
［６］
土壌の色を分析することをさらに含む上記［５］に記載の方法。
［７］
上記［１］～上記［６］のいずれかに記載の方法により、植林予定地における樹木の成長度合いを植林のための樹木の植栽前及び／又は植栽後に予測することを含む、植林方法。
［８］
以下を含む、上記［１］～［６］のいずれかに記載の方法を行うためのシステム：
（ア）土壌の色及び／又は土壌の特性を測定する測定装置
（イ）前記装置を搭載した移動機具。
［９］
測定装置を用いて得られた測定値を樹高に換算して樹木の成長度合いを予測する機能を備えた機材をさらに具備する、［８］に記載のシステム。
［１０］
移動機具が地上移動型機具又は空中移動型機具である、［８］又は［９］に記載のシステム。 Therefore, the present inventors searched for an index that contributes to tree growth prediction, and found that the above-mentioned problem may be solved by a specific index that has not been used for the growth prediction so far. As a result, the present invention was completed.
That is, the present invention relates to at least the following inventions:
[1]
A method for predicting the degree of growth of a tree when planted on the target land, including the following steps:
(A) The process of acquiring the data regarding the growth about the several trees planted in the vicinity of the said land (b) About the soil under each of the said trees, soil color, soil characteristics, and / or soil nutrients Step of analyzing (c) Step of obtaining an explanatory variable from the result of the analysis (d) Step of obtaining a calibration formula with data relating to the growth of the tree using the explanatory variable (e) Analyzing the color, soil characteristics and / or soil nutrients, applying the analysis results to the calibration formula, and calculating and predicting the degree of growth of the trees when the trees are planted on the land.
[2]
The method according to [1] above, comprising analyzing the color of the soil, and including at least L *, a *, b * as explanatory variables obtained as a result of the analysis.
[3]
The method according to [1] above, comprising analyzing soil characteristics by infrared spectroscopy, and including at least an infrared spectrum as an explanatory variable obtained as a result of the analysis.
[4]
The method according to [1] above, comprising analyzing soil nutrients, and comprising at least total carbon, total nitrogen, exchangeable Ca, exchangeable K, and exchangeable Mg as explanatory variables obtained as a result of the analysis.
[5]
The method according to [1] above, comprising analyzing soil characteristics and soil nutrients.
[6]
The method according to [5], further comprising analyzing the color of the soil.
[7]
An afforestation method comprising predicting the degree of growth of a tree in a planned plantation site before and / or after planting a tree for planting by the method according to any one of [1] to [6] above .
[8]
A system for performing the method according to any one of [1] to [6], including:
(A) Measuring device for measuring soil color and / or soil properties (A) A mobile device equipped with the device.
[9]
The system according to [8], further including equipment having a function of predicting a degree of tree growth by converting a measurement value obtained using the measurement device into a tree height.
[10]
The system according to [8] or [9], wherein the mobile device is a ground mobile device or an airborne mobile device.
本発明によれば、少なくとも以下の効果が奏される：
（ア）土壌色や赤外分光スペクトル、土壌養分から成長予測を行うことが可能となる。
（イ）土壌において不足する養分が明らかになるため、施肥などによる土壌改良の際の指針を提供することができる。 According to the present invention, at least the following effects are exhibited:
(A) Growth prediction can be performed from soil color, infrared spectrum, and soil nutrients.
(I) Since nutrients that are insufficient in the soil are clarified, it is possible to provide a guideline for soil improvement by fertilization or the like.
（ア）土壌色や赤外分光スペクトル、土壌養分から成長予測を行うことが可能となる。
（イ）土壌において不足する養分が明らかになるため、施肥などによる土壌改良の際の指針を提供することができる。 According to the present invention, at least the following effects are exhibited:
(A) Growth prediction can be performed from soil color, infrared spectrum, and soil nutrients.
(I) Since nutrients that are insufficient in the soil are clarified, it is possible to provide a guideline for soil improvement by fertilization or the like.
以下に本発明をより詳細に説明する。
なお、本明細書において「土壌を分析（する）」とは、対象の土壌の色、土壌の特性及び／又は土壌に含まれる養分（土壌の養分）についての情報を得ることを意味する。
また。本明細書において「（樹木の）成長」とは、例えば樹木の樹高により定量される成長を意味するが、他の定量項目（例えば枝下高、太さ（胸高直径）であってもよい。 Hereinafter, the present invention will be described in more detail.
In the present specification, “analyze (perform) the soil” means to obtain information on the color of the target soil, the characteristics of the soil, and / or nutrients contained in the soil (soil nutrients).
Also. In this specification, “growth of (tree)” means growth quantified by the tree height of the tree, for example, but may be other quantitative items (for example, height under branch, thickness (chest height diameter)).
なお、本明細書において「土壌を分析（する）」とは、対象の土壌の色、土壌の特性及び／又は土壌に含まれる養分（土壌の養分）についての情報を得ることを意味する。
また。本明細書において「（樹木の）成長」とは、例えば樹木の樹高により定量される成長を意味するが、他の定量項目（例えば枝下高、太さ（胸高直径）であってもよい。 Hereinafter, the present invention will be described in more detail.
In the present specification, “analyze (perform) the soil” means to obtain information on the color of the target soil, the characteristics of the soil, and / or nutrients contained in the soil (soil nutrients).
Also. In this specification, “growth of (tree)” means growth quantified by the tree height of the tree, for example, but may be other quantitative items (for example, height under branch, thickness (chest height diameter)).
上記のとおり、少なくとも本発明は以下の工程を含む、対象である土地に樹木を植えた際の該樹木の成長度合いを予測する方法に関する：
（ａ）上記土地の近傍に栽植された複数の樹木についての成長に関するデータを取得する工程
（ｂ）前記樹木のそれぞれの下の土壌について、土壌の色、土壌の特性及び／又は土壌の養分を分析する工程
（ｃ）該分析の結果から説明変数を得る工程
（ｄ）該説明変数を用いて、前記樹木についての成長に関するデータとの検量式を得る工程
（ｅ）上記土地の土壌について土壌の色、土壌の特性及び／又は土壌の養分を分析し、当該分析結果を前記検量式に当てはめて、上記土地に樹木を植えた際の該樹木の成長度合いを計算し、予測する工程。
本発明の例についての手順を概念的に図１に示した。すなわち、本発明は土壌の色、土壌の特性及び土壌の養分等についての分析を行い、当該分析結果から検量線モデル及び／又は検量式を作製し、該検量線モデル及び／又は検量式を用いて、樹木成長の推定（予測）を行うことに基礎を置くものである。 As described above, at least the present invention relates to a method for predicting the degree of growth of a tree when the tree is planted on the target land, including the following steps:
(A) The process of acquiring the data regarding the growth about the several trees planted in the vicinity of the said land (b) About the soil under each of the said trees, soil color, soil characteristics, and / or soil nutrients Step of analyzing (c) Step of obtaining an explanatory variable from the result of the analysis (d) Step of obtaining a calibration formula with data relating to the growth of the tree using the explanatory variable (e) Analyzing the color, soil characteristics and / or soil nutrients, applying the analysis results to the calibration formula, and calculating and predicting the degree of growth of the trees when the trees are planted on the land.
The procedure for the example of the present invention is conceptually shown in FIG. That is, the present invention analyzes soil color, soil characteristics, soil nutrients, etc., creates a calibration curve model and / or calibration formula from the analysis results, and uses the calibration curve model and / or calibration formula. Therefore, it is based on estimation (prediction) of tree growth.
（ａ）上記土地の近傍に栽植された複数の樹木についての成長に関するデータを取得する工程
（ｂ）前記樹木のそれぞれの下の土壌について、土壌の色、土壌の特性及び／又は土壌の養分を分析する工程
（ｃ）該分析の結果から説明変数を得る工程
（ｄ）該説明変数を用いて、前記樹木についての成長に関するデータとの検量式を得る工程
（ｅ）上記土地の土壌について土壌の色、土壌の特性及び／又は土壌の養分を分析し、当該分析結果を前記検量式に当てはめて、上記土地に樹木を植えた際の該樹木の成長度合いを計算し、予測する工程。
本発明の例についての手順を概念的に図１に示した。すなわち、本発明は土壌の色、土壌の特性及び土壌の養分等についての分析を行い、当該分析結果から検量線モデル及び／又は検量式を作製し、該検量線モデル及び／又は検量式を用いて、樹木成長の推定（予測）を行うことに基礎を置くものである。 As described above, at least the present invention relates to a method for predicting the degree of growth of a tree when the tree is planted on the target land, including the following steps:
(A) The process of acquiring the data regarding the growth about the several trees planted in the vicinity of the said land (b) About the soil under each of the said trees, soil color, soil characteristics, and / or soil nutrients Step of analyzing (c) Step of obtaining an explanatory variable from the result of the analysis (d) Step of obtaining a calibration formula with data relating to the growth of the tree using the explanatory variable (e) Analyzing the color, soil characteristics and / or soil nutrients, applying the analysis results to the calibration formula, and calculating and predicting the degree of growth of the trees when the trees are planted on the land.
The procedure for the example of the present invention is conceptually shown in FIG. That is, the present invention analyzes soil color, soil characteristics, soil nutrients, etc., creates a calibration curve model and / or calibration formula from the analysis results, and uses the calibration curve model and / or calibration formula. Therefore, it is based on estimation (prediction) of tree growth.
本発明の方法について、以下により詳細に説明する。
なお本発明の方法は、いずれの方法においても
（１）当該樹木の樹高など樹木の成長に関するデータを取得する工程、及び
（２）当該樹木下の土壌を採取し分析に供するか、又はその場で分析データを得る工程は共通して行われる。 The method of the present invention is described in more detail below.
In any of the methods, the method of the present invention includes (1) a step of acquiring data relating to tree growth such as the height of the tree, and (2) the soil under the tree is collected and used for analysis, or on the spot. The process for obtaining the analysis data is performed in common.
なお本発明の方法は、いずれの方法においても
（１）当該樹木の樹高など樹木の成長に関するデータを取得する工程、及び
（２）当該樹木下の土壌を採取し分析に供するか、又はその場で分析データを得る工程は共通して行われる。 The method of the present invention is described in more detail below.
In any of the methods, the method of the present invention includes (1) a step of acquiring data relating to tree growth such as the height of the tree, and (2) the soil under the tree is collected and used for analysis, or on the spot. The process for obtaining the analysis data is performed in common.
土壌の色、土壌の特性及び／又は土壌の養分を用いる本発明の方法のそれぞれについて、以下に説明する。
Ａ．土壌の色を用いる方法
土壌の色の分析は比較的容易に行うことができるため、当該分析結果を用いる方法は簡便さの点において好ましい。
土壌の色を用いる場合、Ｌａｂの情報を入手し、高さおよび養分との相関を解析する。
ＣＩＥＬＡＢの３つの座標は、色の明度（Ｌ*＝０は黒、Ｌ*＝１００は白の拡散色で、白の反射色はさらに高い）、赤／マゼンタと緑の間の位置（ａ*、負の値は緑寄りで、正の値はマゼンタ寄り）、黄色と青の間の位置（ｂ*、負の値は青寄り、正の値は黄色寄り）に対応している。
土壌の色を用いる場合、本発明の方法は少なくとも以下の各ステップを含む：
（ａ１）スキャナー、デジタルカメラや土色計などの色情報取得装置により土壌の画像を取得し、画像解析ソフトによりサンプルの平均的な色情報（ＲＧＢなど）を得る。
（ｂ１）上記色情報をＣＩＥ（Commission Internationale de l’Eclairage：国際照明委員会）により策定されたＣＩＥ Ｌａｂ色空間情報に変換する（ＲＧＢからＣＩＥ Ｌａｂなど各色情報間の変換式は公開されている。http://www.cs.rit.edu/~ncs/color/t_convert.html）。
（ｃ１）得られた色情報についての変数であるＬ*、ａ*、ｂ*を説明変数とし、樹木成長データを目的変数として、一般化線形モデルにより、検量線モデル及び／又は検量式を作成する。
（ｄ１）前記検量線モデル及び／又は検量式を用いて、樹木成長の推定（予測）を行う。
変数の個数は限定されないが、当該個数を増やすことによってより正確な予測が可能になる場合がある。例えば変数を３個又は４個以上用いる当該方法は好ましく、６個以上用いる方法はより好ましい。
用いられる変数は、典型的には上記Ｌ*、ａ*及びｂ*であり、この場合検量式は一次式である。またこれら３つの変数に加えてＬａ、Ｌｂ及びａｂをさらに用いる方法は好ましい。各変数の交互作用を考慮して説明パラメーターの個数が増えるため、予測の精度が向上するからである。
また、土壌の色の分析結果に加重して、ｐＨ及び／又はＥＣの測定データを考慮すると一層精度の高い予測結果が得られるため好ましい。この傾向はＬ*、ａ*及びｂ*のみに加えてｐＨ及びＥＣの測定データを考慮した場合に顕著である。Ｌ*、ａ*及びｂ*は全炭素などの有機物の量や交換性養分の量をより強く反映する一方、予測に寄与するｐＨ及びＥＣについては反映しづらいためであると考えられる。
上記スキャナー、デジタルカメラや土色計といった色情報取得装置は、土壌の色情報を与えるものであれば限定されない。色情報取得装置の形態としては、携帯可能な小型のものが挙げられる。当該携帯可能な小型色情報取得装置を移動機具に搭載して、地こしらえの際に当該植林地全体の土壌についての色情報を取得するようにしたものは好ましい。移動機具を用いることにより、より広範な領域についての色情報を正確かつ効率的に取得することができるばかりでなく、地上移動型機具には表面より下方に存する土壌についての色情報の取得も可能になるからである。
色情報取得装置に加えて、得られた色情報を変換・解析し、解析結果を樹高に換算して樹木の成長度合いの推定・予測を行う機能をさらに具備した移動機具を含むシステムは、本発明の方法の実施のためにより好ましく用いられる。解析結果を樹高に換算する際に用いる換算式を環境条件に応じて改変できるシステムは、一層より好ましい。環境条件には、土壌型、気候、季節、地形及び微地形といった立地条件が、天候などの条件とともに包含される。
本発明において用いられる色情報取得装置を搭載した移動機具として、トラクターや林業機械（ハーベスタ及びスキッダ等）といった地上移動型機具、ならびに有人航空機や無人航空機（ドローン及びラジコンヘリ等）といった空中移動型機具が例示される。移動機具として、移動パターンが規則的である、トラクター及びハーベスタは好ましく、無人航空機も操作が簡便であるため好ましい。 Each of the methods of the present invention using soil color, soil properties and / or soil nutrients are described below.
A. Method Using Soil Color Since the analysis of soil color can be performed relatively easily, the method using the analysis result is preferable in terms of simplicity.
If soil color is used, Lab information is obtained and the correlation with height and nutrients is analyzed.
The three coordinates of CIELAB are: color brightness (L * = 0 is black, L * = 100 is white diffuse color, white reflection color is higher), position between red / magenta and green (a * , Negative values are closer to green, positive values are closer to magenta), and positions between yellow and blue (b *, negative values are closer to blue, positive values are closer to yellow).
When using soil color, the method of the present invention includes at least the following steps:
(A1) An image of soil is acquired by a color information acquisition device such as a scanner, a digital camera, or a soil color meter, and average color information (RGB, etc.) of the sample is obtained by image analysis software.
(B1) The above color information is converted into CIE Lab color space information established by CIE (Commission Internationale de l'Eclairage) (Conversion formulas between each color information such as RGB to CIE Lab are publicly available) Http://www.cs.rit.edu/~ncs/color/t_convert.html).
(C1) A calibration curve model and / or calibration formula is created by a generalized linear model using L *, a *, and b *, which are variables for the obtained color information, as explanatory variables and tree growth data as an objective variable. To do.
(D1) Tree growth is estimated (predicted) using the calibration curve model and / or calibration equation.
The number of variables is not limited, but more accurate prediction may be possible by increasing the number. For example, the method using 3 or 4 variables is preferable, and the method using 6 or more variables is more preferable.
The variables used are typically the above L *, a * and b *, where the calibration equation is a linear equation. A method of further using La, Lb and ab in addition to these three variables is preferable. This is because the number of explanatory parameters increases in consideration of the interaction of each variable, so that the accuracy of prediction is improved.
In addition, weighting the soil color analysis results and considering the pH and / or EC measurement data is preferable because more accurate prediction results can be obtained. This tendency is remarkable when the measurement data of pH and EC are considered in addition to L *, a * and b * alone. L *, a *, and b * are more likely to reflect the amount of organic matter such as total carbon and the amount of exchangeable nutrients, while it is difficult to reflect the pH and EC that contribute to the prediction.
The color information acquisition device such as the scanner, the digital camera, or the earth color meter is not limited as long as it provides the color information of the soil. As a form of the color information acquisition device, a small portable device can be cited. It is preferable that the portable small color information acquisition device is mounted on a mobile device so as to acquire color information about the soil of the entire plantation when preparing the ground. By using mobile equipment, it is possible not only to acquire color information for a wider area accurately and efficiently, but also for ground mobile equipment, it is possible to acquire color information about the soil below the surface. Because it becomes.
In addition to the color information acquisition device, a system including a mobile device further having a function of converting / analyzing the obtained color information, converting the analysis result into a tree height, and estimating / predicting the growth degree of the tree More preferably used for carrying out the method of the invention. A system that can modify the conversion formula used when converting the analysis result into tree height according to the environmental conditions is even more preferable. Environmental conditions include location conditions such as soil type, climate, season, topography, and microtopography along with conditions such as weather.
As mobile devices equipped with the color information acquisition device used in the present invention, ground mobile devices such as tractors and forestry machines (harvesters, skidder, etc.), and aerial mobile devices, such as manned aircraft and unmanned aircraft (drone, radio controlled helicopter, etc.) Is exemplified. As the mobile equipment, a tractor and a harvester having a regular movement pattern are preferable, and an unmanned aircraft is also preferable because the operation is simple.
Ａ．土壌の色を用いる方法
土壌の色の分析は比較的容易に行うことができるため、当該分析結果を用いる方法は簡便さの点において好ましい。
土壌の色を用いる場合、Ｌａｂの情報を入手し、高さおよび養分との相関を解析する。
ＣＩＥＬＡＢの３つの座標は、色の明度（Ｌ*＝０は黒、Ｌ*＝１００は白の拡散色で、白の反射色はさらに高い）、赤／マゼンタと緑の間の位置（ａ*、負の値は緑寄りで、正の値はマゼンタ寄り）、黄色と青の間の位置（ｂ*、負の値は青寄り、正の値は黄色寄り）に対応している。
土壌の色を用いる場合、本発明の方法は少なくとも以下の各ステップを含む：
（ａ１）スキャナー、デジタルカメラや土色計などの色情報取得装置により土壌の画像を取得し、画像解析ソフトによりサンプルの平均的な色情報（ＲＧＢなど）を得る。
（ｂ１）上記色情報をＣＩＥ（Commission Internationale de l’Eclairage：国際照明委員会）により策定されたＣＩＥ Ｌａｂ色空間情報に変換する（ＲＧＢからＣＩＥ Ｌａｂなど各色情報間の変換式は公開されている。http://www.cs.rit.edu/~ncs/color/t_convert.html）。
（ｃ１）得られた色情報についての変数であるＬ*、ａ*、ｂ*を説明変数とし、樹木成長データを目的変数として、一般化線形モデルにより、検量線モデル及び／又は検量式を作成する。
（ｄ１）前記検量線モデル及び／又は検量式を用いて、樹木成長の推定（予測）を行う。
変数の個数は限定されないが、当該個数を増やすことによってより正確な予測が可能になる場合がある。例えば変数を３個又は４個以上用いる当該方法は好ましく、６個以上用いる方法はより好ましい。
用いられる変数は、典型的には上記Ｌ*、ａ*及びｂ*であり、この場合検量式は一次式である。またこれら３つの変数に加えてＬａ、Ｌｂ及びａｂをさらに用いる方法は好ましい。各変数の交互作用を考慮して説明パラメーターの個数が増えるため、予測の精度が向上するからである。
また、土壌の色の分析結果に加重して、ｐＨ及び／又はＥＣの測定データを考慮すると一層精度の高い予測結果が得られるため好ましい。この傾向はＬ*、ａ*及びｂ*のみに加えてｐＨ及びＥＣの測定データを考慮した場合に顕著である。Ｌ*、ａ*及びｂ*は全炭素などの有機物の量や交換性養分の量をより強く反映する一方、予測に寄与するｐＨ及びＥＣについては反映しづらいためであると考えられる。
上記スキャナー、デジタルカメラや土色計といった色情報取得装置は、土壌の色情報を与えるものであれば限定されない。色情報取得装置の形態としては、携帯可能な小型のものが挙げられる。当該携帯可能な小型色情報取得装置を移動機具に搭載して、地こしらえの際に当該植林地全体の土壌についての色情報を取得するようにしたものは好ましい。移動機具を用いることにより、より広範な領域についての色情報を正確かつ効率的に取得することができるばかりでなく、地上移動型機具には表面より下方に存する土壌についての色情報の取得も可能になるからである。
色情報取得装置に加えて、得られた色情報を変換・解析し、解析結果を樹高に換算して樹木の成長度合いの推定・予測を行う機能をさらに具備した移動機具を含むシステムは、本発明の方法の実施のためにより好ましく用いられる。解析結果を樹高に換算する際に用いる換算式を環境条件に応じて改変できるシステムは、一層より好ましい。環境条件には、土壌型、気候、季節、地形及び微地形といった立地条件が、天候などの条件とともに包含される。
本発明において用いられる色情報取得装置を搭載した移動機具として、トラクターや林業機械（ハーベスタ及びスキッダ等）といった地上移動型機具、ならびに有人航空機や無人航空機（ドローン及びラジコンヘリ等）といった空中移動型機具が例示される。移動機具として、移動パターンが規則的である、トラクター及びハーベスタは好ましく、無人航空機も操作が簡便であるため好ましい。 Each of the methods of the present invention using soil color, soil properties and / or soil nutrients are described below.
A. Method Using Soil Color Since the analysis of soil color can be performed relatively easily, the method using the analysis result is preferable in terms of simplicity.
If soil color is used, Lab information is obtained and the correlation with height and nutrients is analyzed.
The three coordinates of CIELAB are: color brightness (L * = 0 is black, L * = 100 is white diffuse color, white reflection color is higher), position between red / magenta and green (a * , Negative values are closer to green, positive values are closer to magenta), and positions between yellow and blue (b *, negative values are closer to blue, positive values are closer to yellow).
When using soil color, the method of the present invention includes at least the following steps:
(A1) An image of soil is acquired by a color information acquisition device such as a scanner, a digital camera, or a soil color meter, and average color information (RGB, etc.) of the sample is obtained by image analysis software.
(B1) The above color information is converted into CIE Lab color space information established by CIE (Commission Internationale de l'Eclairage) (Conversion formulas between each color information such as RGB to CIE Lab are publicly available) Http://www.cs.rit.edu/~ncs/color/t_convert.html).
(C1) A calibration curve model and / or calibration formula is created by a generalized linear model using L *, a *, and b *, which are variables for the obtained color information, as explanatory variables and tree growth data as an objective variable. To do.
(D1) Tree growth is estimated (predicted) using the calibration curve model and / or calibration equation.
The number of variables is not limited, but more accurate prediction may be possible by increasing the number. For example, the method using 3 or 4 variables is preferable, and the method using 6 or more variables is more preferable.
The variables used are typically the above L *, a * and b *, where the calibration equation is a linear equation. A method of further using La, Lb and ab in addition to these three variables is preferable. This is because the number of explanatory parameters increases in consideration of the interaction of each variable, so that the accuracy of prediction is improved.
In addition, weighting the soil color analysis results and considering the pH and / or EC measurement data is preferable because more accurate prediction results can be obtained. This tendency is remarkable when the measurement data of pH and EC are considered in addition to L *, a * and b * alone. L *, a *, and b * are more likely to reflect the amount of organic matter such as total carbon and the amount of exchangeable nutrients, while it is difficult to reflect the pH and EC that contribute to the prediction.
The color information acquisition device such as the scanner, the digital camera, or the earth color meter is not limited as long as it provides the color information of the soil. As a form of the color information acquisition device, a small portable device can be cited. It is preferable that the portable small color information acquisition device is mounted on a mobile device so as to acquire color information about the soil of the entire plantation when preparing the ground. By using mobile equipment, it is possible not only to acquire color information for a wider area accurately and efficiently, but also for ground mobile equipment, it is possible to acquire color information about the soil below the surface. Because it becomes.
In addition to the color information acquisition device, a system including a mobile device further having a function of converting / analyzing the obtained color information, converting the analysis result into a tree height, and estimating / predicting the growth degree of the tree More preferably used for carrying out the method of the invention. A system that can modify the conversion formula used when converting the analysis result into tree height according to the environmental conditions is even more preferable. Environmental conditions include location conditions such as soil type, climate, season, topography, and microtopography along with conditions such as weather.
As mobile devices equipped with the color information acquisition device used in the present invention, ground mobile devices such as tractors and forestry machines (harvesters, skidder, etc.), and aerial mobile devices, such as manned aircraft and unmanned aircraft (drone, radio controlled helicopter, etc.) Is exemplified. As the mobile equipment, a tractor and a harvester having a regular movement pattern are preferable, and an unmanned aircraft is also preferable because the operation is simple.
Ｂ．土壌の特性を用いる方法
土壌の養分が樹木の成長に影響すると考えられるが、樹木の成長に必要な養分は多種類あり、成長との関連を明らかにするための方法は未だ確立されていないばかりでなく、全ての養分を測定することは、時間的費用的に制約がある場合が多い。また、樹木の成長には様々な養分が関連しあい、必要な養分量とともに、過剰である場合には却って成長制限要因となる場合もあるため、養分による樹木の成長予測は、必ずしも容易ではないといった事情がある。
一方、土壌の特性（全炭素量、ＥＣ（電気伝導度）、交換性Ｃａ量、交換性Ｍｇ量及び交換性Ｋ量等）を反映する土壌の赤外スペクトル情報を得て樹木成長を推定するための検量線モデルを作成すると、精度が高い樹木成長の推定（予測）を行うことができる。したがって当該方法は、簡便さと精度のバランスに優れている。
この場合、本発明の方法は少なくとも以下の各ステップを含む：
（ａ２）赤外分光測定装置により、土壌サンプルの赤外分光スペクトルデータを得る。
（ｂ２）前記赤外分光スペクトルデータの標準化処理を行う。
（ｃ２）樹木高さを説明変数とし、多変量解析ＰＬＳ解析を行い、検量線モデル及び／又は検量式を作成する。
（ｄ２）前記検量線モデル及び／又は検量式により、樹木成長の推定（予測）を行う。
より具体的には、例えば赤外分光装置を用いて複数の標準土壌サンプルの３５０～７８００ｃｍ－１の赤外スペクトルデータを取得し、最適な波数範囲として８００～１２００ｃｍ－１を選択し、４ｃｍ－１ごとにスムージング処理を行い、スペクトルデータの標準化処理を行ってよい。樹高など樹木成長のデータ結果を用いて、ＰＬＳ回帰により検量線モデルを作成する。その検量線モデルを用いて、樹木成長が未知である場所において樹木を植栽した場合の樹高成長を推定（予測）する。
なお４ｃｍ－１ごとにスムージング処理を行うことにより、２ｃｍ－１～１６ｃｍ－１の範囲のスムージング処理のうち最も正確な予測がなされる。
当該方法において反映される土壌の特性として、全炭素量を含むものは好ましく、交換性Ｃａ、Ｍｇ及びＫ等の微量成分の量を含むものはより好ましい。また、全Ｆｅ及び／又は全Ｓｉを反映してもよい。
上記赤外分光測定装置の形態としては室内据え置き型のもののほか、デジタルカメラや赤外スペクトルカメラといった携帯可能な小型のものを含むものが挙げられる。
携帯可能な小型のものを含む赤外分光測定装置の場合、当該赤外分光測定装置を移動機具に搭載して、地こしらえの際に当該植林地全体の土壌についての色情報を取得するようにしたものは好ましい。移動機具を用いることにより、より広範な領域についての赤外スペクトルデータ情報を正確かつ効率的に取得することができるばかりでなく、地上移動型機具の場合には表面より下方に存する土壌についての赤外スペクトルデータ情報の取得も可能になるからである。
赤外スペクトルデータ情報取得装置に加えて、得られた赤外スペクトルデータ情報を変換・解析し、解析結果を樹高に換算して樹木の成長度合いの推定・予測を行う機能を備えた機材をさらに具備した移動機具を含むシステムは、本発明の方法の実施のためにより好ましく用いられる。解析結果を樹高に換算する際に用いる換算式を環境条件に応じて改変できるシステムは、一層より好ましい。当該機材は上記測定装置の一部を構成していてもよく、あるいは上記測定装置と独立した別個の機材であってもよい。
本発明において用いられる赤外分光測定装置を搭載した移動機具として、トラクターや林業機械（ハーベスタ及びスキッダ等）といった地上移動型機具、ならびに有人航空機や無人航空機（ドローン及びラジコンヘリ等）といった空中移動型機具が例示される。移動機具として、移動パターンが規則的である、トラクター及びハーベスタは好ましく、無人航空機も操作が簡便であるため好ましい。 赤外分光測定装置に加えて色情報取得装置を搭載した本発明のシステムによれば、樹木の成長予測をより精密に行うことができるため好ましい。 B. Methods using soil properties Soil nutrients are thought to affect tree growth, but there are many types of nutrients necessary for tree growth, and no method has yet been established to clarify the relationship with growth. Rather, measuring all nutrients is often time-constrained. In addition, it is not always easy to predict the growth of trees by nutrients because various nutrients are related to the growth of trees, and in addition to the required nutrients, it may become a growth limiting factor if it is excessive. There are circumstances.
On the other hand, tree growth is estimated by obtaining soil infrared spectrum information reflecting soil characteristics (total carbon content, EC (electrical conductivity), exchangeable Ca content, exchangeable Mg content, exchangeable K content, etc.). Therefore, it is possible to estimate (predict) tree growth with high accuracy. Therefore, this method is excellent in balance between simplicity and accuracy.
In this case, the method of the present invention comprises at least the following steps:
(A2) The infrared spectroscopic spectrum data of the soil sample is obtained by the infrared spectroscopic measurement device.
(B2) The infrared spectrum data is standardized.
(C2) Using the tree height as an explanatory variable, multivariate analysis PLS analysis is performed, and a calibration curve model and / or calibration formula is created.
(D2) Tree growth is estimated (predicted) by the calibration curve model and / or calibration equation.
More specifically, for example, infrared spectrum data of 350 to 7800 cm −1 of a plurality of standard soil samples is acquired using an infrared spectroscopic device, and 800 to 1200 cm −1 is selected as an optimal wave number range, and 4 cm − A smoothing process may be performed for each and a standardization process of spectrum data may be performed. A calibration curve model is created by PLS regression using tree growth data results such as tree height. Using the calibration curve model, tree height growth is estimated (predicted) when trees are planted in a place where tree growth is unknown.
Note that by performing the smoothing process every 4 cm −1 , the most accurate prediction among the smoothing processes in the range of 2 cm −1 to 16 cm −1 is made.
As the soil characteristics reflected in the method, those containing the total amount of carbon are preferable, and those containing the amount of trace components such as exchangeable Ca, Mg and K are more preferable. Moreover, you may reflect all Fe and / or all Si.
As a form of the infrared spectroscopic measurement apparatus, in addition to an indoor stationary type, a portable type including a digital camera and an infrared spectrum camera can be used.
In the case of an infrared spectroscopic measurement device including a small portable device, the infrared spectroscopic measurement device is mounted on a mobile device so that color information about the soil of the entire plantation can be acquired when preparing the ground. What is done is preferable. The use of mobile equipment not only enables accurate and efficient acquisition of infrared spectral data information over a wider area, but in the case of ground mobile equipment, redness of soil that exists below the surface. This is because the outside spectrum data information can be acquired.
In addition to the infrared spectrum data information acquisition device, equipment that has the function to convert and analyze the obtained infrared spectrum data information, convert the analysis results into tree heights, and estimate and predict the degree of tree growth A system including the mobile equipment provided is more preferably used for carrying out the method of the invention. A system that can modify the conversion formula used when converting the analysis result into tree height according to the environmental conditions is even more preferable. The equipment may constitute a part of the measuring apparatus or may be a separate equipment independent of the measuring apparatus.
As mobile equipment equipped with the infrared spectroscopic measurement device used in the present invention, ground mobile equipment such as tractors and forestry machines (harvesters, skidder, etc.), and aeronautical mobile type such as manned aircraft and unmanned aircraft (drones, radio controlled helicopters, etc.) The equipment is exemplified. As the mobile equipment, a tractor and a harvester having a regular movement pattern are preferable, and an unmanned aircraft is also preferable because the operation is simple. According to the system of the present invention in which a color information acquisition device is mounted in addition to the infrared spectroscopic measurement device, it is preferable because tree growth can be predicted more precisely.
土壌の養分が樹木の成長に影響すると考えられるが、樹木の成長に必要な養分は多種類あり、成長との関連を明らかにするための方法は未だ確立されていないばかりでなく、全ての養分を測定することは、時間的費用的に制約がある場合が多い。また、樹木の成長には様々な養分が関連しあい、必要な養分量とともに、過剰である場合には却って成長制限要因となる場合もあるため、養分による樹木の成長予測は、必ずしも容易ではないといった事情がある。
一方、土壌の特性（全炭素量、ＥＣ（電気伝導度）、交換性Ｃａ量、交換性Ｍｇ量及び交換性Ｋ量等）を反映する土壌の赤外スペクトル情報を得て樹木成長を推定するための検量線モデルを作成すると、精度が高い樹木成長の推定（予測）を行うことができる。したがって当該方法は、簡便さと精度のバランスに優れている。
この場合、本発明の方法は少なくとも以下の各ステップを含む：
（ａ２）赤外分光測定装置により、土壌サンプルの赤外分光スペクトルデータを得る。
（ｂ２）前記赤外分光スペクトルデータの標準化処理を行う。
（ｃ２）樹木高さを説明変数とし、多変量解析ＰＬＳ解析を行い、検量線モデル及び／又は検量式を作成する。
（ｄ２）前記検量線モデル及び／又は検量式により、樹木成長の推定（予測）を行う。
より具体的には、例えば赤外分光装置を用いて複数の標準土壌サンプルの３５０～７８００ｃｍ－１の赤外スペクトルデータを取得し、最適な波数範囲として８００～１２００ｃｍ－１を選択し、４ｃｍ－１ごとにスムージング処理を行い、スペクトルデータの標準化処理を行ってよい。樹高など樹木成長のデータ結果を用いて、ＰＬＳ回帰により検量線モデルを作成する。その検量線モデルを用いて、樹木成長が未知である場所において樹木を植栽した場合の樹高成長を推定（予測）する。
なお４ｃｍ－１ごとにスムージング処理を行うことにより、２ｃｍ－１～１６ｃｍ－１の範囲のスムージング処理のうち最も正確な予測がなされる。
当該方法において反映される土壌の特性として、全炭素量を含むものは好ましく、交換性Ｃａ、Ｍｇ及びＫ等の微量成分の量を含むものはより好ましい。また、全Ｆｅ及び／又は全Ｓｉを反映してもよい。
上記赤外分光測定装置の形態としては室内据え置き型のもののほか、デジタルカメラや赤外スペクトルカメラといった携帯可能な小型のものを含むものが挙げられる。
携帯可能な小型のものを含む赤外分光測定装置の場合、当該赤外分光測定装置を移動機具に搭載して、地こしらえの際に当該植林地全体の土壌についての色情報を取得するようにしたものは好ましい。移動機具を用いることにより、より広範な領域についての赤外スペクトルデータ情報を正確かつ効率的に取得することができるばかりでなく、地上移動型機具の場合には表面より下方に存する土壌についての赤外スペクトルデータ情報の取得も可能になるからである。
赤外スペクトルデータ情報取得装置に加えて、得られた赤外スペクトルデータ情報を変換・解析し、解析結果を樹高に換算して樹木の成長度合いの推定・予測を行う機能を備えた機材をさらに具備した移動機具を含むシステムは、本発明の方法の実施のためにより好ましく用いられる。解析結果を樹高に換算する際に用いる換算式を環境条件に応じて改変できるシステムは、一層より好ましい。当該機材は上記測定装置の一部を構成していてもよく、あるいは上記測定装置と独立した別個の機材であってもよい。
本発明において用いられる赤外分光測定装置を搭載した移動機具として、トラクターや林業機械（ハーベスタ及びスキッダ等）といった地上移動型機具、ならびに有人航空機や無人航空機（ドローン及びラジコンヘリ等）といった空中移動型機具が例示される。移動機具として、移動パターンが規則的である、トラクター及びハーベスタは好ましく、無人航空機も操作が簡便であるため好ましい。 赤外分光測定装置に加えて色情報取得装置を搭載した本発明のシステムによれば、樹木の成長予測をより精密に行うことができるため好ましい。 B. Methods using soil properties Soil nutrients are thought to affect tree growth, but there are many types of nutrients necessary for tree growth, and no method has yet been established to clarify the relationship with growth. Rather, measuring all nutrients is often time-constrained. In addition, it is not always easy to predict the growth of trees by nutrients because various nutrients are related to the growth of trees, and in addition to the required nutrients, it may become a growth limiting factor if it is excessive. There are circumstances.
On the other hand, tree growth is estimated by obtaining soil infrared spectrum information reflecting soil characteristics (total carbon content, EC (electrical conductivity), exchangeable Ca content, exchangeable Mg content, exchangeable K content, etc.). Therefore, it is possible to estimate (predict) tree growth with high accuracy. Therefore, this method is excellent in balance between simplicity and accuracy.
In this case, the method of the present invention comprises at least the following steps:
(A2) The infrared spectroscopic spectrum data of the soil sample is obtained by the infrared spectroscopic measurement device.
(B2) The infrared spectrum data is standardized.
(C2) Using the tree height as an explanatory variable, multivariate analysis PLS analysis is performed, and a calibration curve model and / or calibration formula is created.
(D2) Tree growth is estimated (predicted) by the calibration curve model and / or calibration equation.
More specifically, for example, infrared spectrum data of 350 to 7800 cm −1 of a plurality of standard soil samples is acquired using an infrared spectroscopic device, and 800 to 1200 cm −1 is selected as an optimal wave number range, and 4 cm − A smoothing process may be performed for each and a standardization process of spectrum data may be performed. A calibration curve model is created by PLS regression using tree growth data results such as tree height. Using the calibration curve model, tree height growth is estimated (predicted) when trees are planted in a place where tree growth is unknown.
Note that by performing the smoothing process every 4 cm −1 , the most accurate prediction among the smoothing processes in the range of 2 cm −1 to 16 cm −1 is made.
As the soil characteristics reflected in the method, those containing the total amount of carbon are preferable, and those containing the amount of trace components such as exchangeable Ca, Mg and K are more preferable. Moreover, you may reflect all Fe and / or all Si.
As a form of the infrared spectroscopic measurement apparatus, in addition to an indoor stationary type, a portable type including a digital camera and an infrared spectrum camera can be used.
In the case of an infrared spectroscopic measurement device including a small portable device, the infrared spectroscopic measurement device is mounted on a mobile device so that color information about the soil of the entire plantation can be acquired when preparing the ground. What is done is preferable. The use of mobile equipment not only enables accurate and efficient acquisition of infrared spectral data information over a wider area, but in the case of ground mobile equipment, redness of soil that exists below the surface. This is because the outside spectrum data information can be acquired.
In addition to the infrared spectrum data information acquisition device, equipment that has the function to convert and analyze the obtained infrared spectrum data information, convert the analysis results into tree heights, and estimate and predict the degree of tree growth A system including the mobile equipment provided is more preferably used for carrying out the method of the invention. A system that can modify the conversion formula used when converting the analysis result into tree height according to the environmental conditions is even more preferable. The equipment may constitute a part of the measuring apparatus or may be a separate equipment independent of the measuring apparatus.
As mobile equipment equipped with the infrared spectroscopic measurement device used in the present invention, ground mobile equipment such as tractors and forestry machines (harvesters, skidder, etc.), and aeronautical mobile type such as manned aircraft and unmanned aircraft (drones, radio controlled helicopters, etc.) The equipment is exemplified. As the mobile equipment, a tractor and a harvester having a regular movement pattern are preferable, and an unmanned aircraft is also preferable because the operation is simple. According to the system of the present invention in which a color information acquisition device is mounted in addition to the infrared spectroscopic measurement device, it is preferable because tree growth can be predicted more precisely.
Ｃ．土壌の養分を用いる方法
上記のとおり養分による樹木の成長予測は必ずしも容易ではないといった事情はあるにせよ、有効な栄養成分（養分）を特定し、樹木の成長予測を行うことは、一層精度の高い予測方法を与えると推測された。本発明者らはかかる推測の下、土壌の養分を用いる樹木の成長予測の方法について探究し、その結果本発明の他の態様を創出するに至った。
土壌の養分を分析することにより樹木の成長を予測する本発明の方法によれば、一般に上記Ａ．土壌の色及びＢ．土壌の特性のみを用いる方法より精度の高い予測結果を与える点において好ましい。
土壌の養分を分析することを含む本発明の方法は、少なくとも以下の各ステップを含む：
（ａ３）全炭素、全窒素、酢酸アンモニア抽出による交換性微量金属元素（Ｃａ、Ｋ、Ｍｇ）の量（割合）を測定し、さらに土壌ｐＨ及び電気伝導度（ＥＣ）も併せて測定する。
（ｂ３）これら土壌の養分についての測定値を説明変数とし、樹高などの樹木成長の測定結果を用いて、一般化線形モデルにより、樹木成長を推定するための検量線モデル及び／又は検量式を作成する。
交換性微量金属元素として上記Ｃａ、Ｋ、Ｍｇのほか、ＡｌやＮａといった成分についての分析結果を加味することにより、一層精度の高い予測が可能となるため好ましい。ＡｌやＮａといった成分は、樹木の成長に対する影響は比較的小さいことを併せ考えれば、かかる態様が好ましいことは予想外のことである。 C. Method of using soil nutrients As mentioned above, it is not always easy to predict tree growth by nutrients, but it is more accurate to identify effective nutrients (nutrients) and predict tree growth. It was speculated to give a high prediction method. Based on such assumptions, the present inventors have investigated a method for predicting the growth of trees using soil nutrients, and as a result have created other aspects of the present invention.
According to the method of the present invention for predicting tree growth by analyzing soil nutrients, the above-mentioned A. Soil color and B. This method is preferable in that it provides a prediction result with higher accuracy than the method using only soil characteristics.
The method of the present invention comprising analyzing soil nutrients includes at least the following steps:
(A3) The amount (ratio) of exchangeable trace metal elements (Ca, K, Mg) by extraction of total carbon, total nitrogen, and ammonia acetate is measured, and the soil pH and electrical conductivity (EC) are also measured.
(B3) Using the measurement values of nutrients of these soils as explanatory variables, and using the measurement results of tree growth such as tree height, a standardized linear model and / or a calibration equation for estimating tree growth using a generalized linear model create.
In addition to the above-mentioned Ca, K, and Mg as exchangeable trace metal elements, it is preferable to take into account the analysis results of components such as Al and Na, since prediction with higher accuracy becomes possible. In view of the fact that components such as Al and Na have a relatively small influence on tree growth, it is unexpected that such an embodiment is preferable.
上記のとおり養分による樹木の成長予測は必ずしも容易ではないといった事情はあるにせよ、有効な栄養成分（養分）を特定し、樹木の成長予測を行うことは、一層精度の高い予測方法を与えると推測された。本発明者らはかかる推測の下、土壌の養分を用いる樹木の成長予測の方法について探究し、その結果本発明の他の態様を創出するに至った。
土壌の養分を分析することにより樹木の成長を予測する本発明の方法によれば、一般に上記Ａ．土壌の色及びＢ．土壌の特性のみを用いる方法より精度の高い予測結果を与える点において好ましい。
土壌の養分を分析することを含む本発明の方法は、少なくとも以下の各ステップを含む：
（ａ３）全炭素、全窒素、酢酸アンモニア抽出による交換性微量金属元素（Ｃａ、Ｋ、Ｍｇ）の量（割合）を測定し、さらに土壌ｐＨ及び電気伝導度（ＥＣ）も併せて測定する。
（ｂ３）これら土壌の養分についての測定値を説明変数とし、樹高などの樹木成長の測定結果を用いて、一般化線形モデルにより、樹木成長を推定するための検量線モデル及び／又は検量式を作成する。
交換性微量金属元素として上記Ｃａ、Ｋ、Ｍｇのほか、ＡｌやＮａといった成分についての分析結果を加味することにより、一層精度の高い予測が可能となるため好ましい。ＡｌやＮａといった成分は、樹木の成長に対する影響は比較的小さいことを併せ考えれば、かかる態様が好ましいことは予想外のことである。 C. Method of using soil nutrients As mentioned above, it is not always easy to predict tree growth by nutrients, but it is more accurate to identify effective nutrients (nutrients) and predict tree growth. It was speculated to give a high prediction method. Based on such assumptions, the present inventors have investigated a method for predicting the growth of trees using soil nutrients, and as a result have created other aspects of the present invention.
According to the method of the present invention for predicting tree growth by analyzing soil nutrients, the above-mentioned A. Soil color and B. This method is preferable in that it provides a prediction result with higher accuracy than the method using only soil characteristics.
The method of the present invention comprising analyzing soil nutrients includes at least the following steps:
(A3) The amount (ratio) of exchangeable trace metal elements (Ca, K, Mg) by extraction of total carbon, total nitrogen, and ammonia acetate is measured, and the soil pH and electrical conductivity (EC) are also measured.
(B3) Using the measurement values of nutrients of these soils as explanatory variables, and using the measurement results of tree growth such as tree height, a standardized linear model and / or a calibration equation for estimating tree growth using a generalized linear model create.
In addition to the above-mentioned Ca, K, and Mg as exchangeable trace metal elements, it is preferable to take into account the analysis results of components such as Al and Na, since prediction with higher accuracy becomes possible. In view of the fact that components such as Al and Na have a relatively small influence on tree growth, it is unexpected that such an embodiment is preferable.
また土壌の特性及び土壌の養分を分析することの両方を含む本発明の方法も好ましい。さらにまた、土壌の色を分析することをさらに含むことにより、一層精度の高い予測が可能になる場合があり好ましい。
土壌の色、特性及び養分についての各説明変数と各種特性項目（ｐＨ、ＥＣ、元素量）についての相関を調査し、前記項目を適宜用いて検量式／検量線の作成に用いてよい。 Also preferred is the method of the invention which includes both analyzing soil properties and soil nutrients. Furthermore, it may be possible to predict with higher accuracy by further including analyzing the color of the soil.
You may investigate the correlation about each explanatory variable about the color of a soil, a characteristic, and a nutrient, and various characteristic items (pH, EC, element amount), and you may use it for preparation of a calibration formula / calibration curve using the said item suitably.
土壌の色、特性及び養分についての各説明変数と各種特性項目（ｐＨ、ＥＣ、元素量）についての相関を調査し、前記項目を適宜用いて検量式／検量線の作成に用いてよい。 Also preferred is the method of the invention which includes both analyzing soil properties and soil nutrients. Furthermore, it may be possible to predict with higher accuracy by further including analyzing the color of the soil.
You may investigate the correlation about each explanatory variable about the color of a soil, a characteristic, and a nutrient, and various characteristic items (pH, EC, element amount), and you may use it for preparation of a calibration formula / calibration curve using the said item suitably.
本発明の方法が適用される面積はとくに限定されず、１植林地に相当する面積である数１００～数１０００ｈａから数ｈａ以下まで適用可能である。面積に応じてサンプリング方法・数を改変してよい。
土壌のサンプリング数、サンプリングを行う場所の数（密度）及びサンプリングする土壌の量は、調査を行う土地の面請及び調査において必要な精度に応じて適宜決定してよい。
サンプルとして事前に成長調査を行う樹木の本数も、調査を行う土地の面請及び調査において必要な精度に応じて決定してよい。 The area to which the method of the present invention is applied is not particularly limited, and can be applied from several hundred to several thousand ha to several ha or less, which is an area corresponding to one plantation. The sampling method / number may be changed according to the area.
The number of samples of soil, the number of places (density) where sampling is performed, and the amount of soil to be sampled may be appropriately determined according to the accuracy of the land application and survey to be surveyed.
The number of trees for which a growth survey is performed in advance as a sample may be determined according to the accuracy required for the surface survey and survey for the land to be surveyed.
土壌のサンプリング数、サンプリングを行う場所の数（密度）及びサンプリングする土壌の量は、調査を行う土地の面請及び調査において必要な精度に応じて適宜決定してよい。
サンプルとして事前に成長調査を行う樹木の本数も、調査を行う土地の面請及び調査において必要な精度に応じて決定してよい。 The area to which the method of the present invention is applied is not particularly limited, and can be applied from several hundred to several thousand ha to several ha or less, which is an area corresponding to one plantation. The sampling method / number may be changed according to the area.
The number of samples of soil, the number of places (density) where sampling is performed, and the amount of soil to be sampled may be appropriately determined according to the accuracy of the land application and survey to be surveyed.
The number of trees for which a growth survey is performed in advance as a sample may be determined according to the accuracy required for the surface survey and survey for the land to be surveyed.
地形の影響を低減するために、サンプリングは各形状の部位から均等に行うことは好ましい。さらに地域・地形、樹種、季節及び期間（年数）等の影響を排除するために、比較的狭いプロットでサンプリング等を行えばよい。比較的広いエリアにおいて予測を行う場合には、これらの潜在的な変動要因についてのデータを検量線に用いて行われる解析の中に入れることにより、これらの変動要因を入れ込んだ予測も可能である。
In order to reduce the influence of the terrain, it is preferable to perform the sampling evenly from the parts of each shape. Furthermore, in order to eliminate the influence of the region / terrain, tree species, season, period (years), etc., sampling or the like may be performed with a relatively narrow plot. When forecasting in a relatively large area, it is possible to make predictions that incorporate these fluctuation factors by including data on these potential fluctuation factors in the analysis performed using the calibration curve. is there.
本発明は前記いずれかの方法により、植林予定地における樹木の成長度合いを植林のための樹木の植栽前及び／又は植栽後に予測することを含む植林方法にも関する。
当該予測に用いられる本発明の予測方法は限定されず、時間、場所、コスト等を勘案して適宜決定してよい。簡便さの観点からは土壌の色を用いる方法が有利であり、予測の正確さの観点からは土壌の養分の分析結果を用いる方法が有利である。また土壌の特性を用いる方法は、土壌の色を用いる方法及び土壌の養分の分析結果を用いる方法の両方の利点を相当程度併せて有するという優位性がある。 The present invention also relates to an afforestation method including predicting the growth degree of a tree in a planned plantation site before and / or after planting a tree for planting by any one of the methods described above.
The prediction method of this invention used for the said prediction is not limited, You may determine suitably considering time, a place, cost, etc. From the viewpoint of simplicity, a method using soil color is advantageous, and from a viewpoint of accuracy of prediction, a method using analysis results of soil nutrients is advantageous. In addition, the method using the characteristics of the soil has an advantage that it has a considerable combination of the advantages of both the method using the color of the soil and the method using the analysis result of the nutrients of the soil.
当該予測に用いられる本発明の予測方法は限定されず、時間、場所、コスト等を勘案して適宜決定してよい。簡便さの観点からは土壌の色を用いる方法が有利であり、予測の正確さの観点からは土壌の養分の分析結果を用いる方法が有利である。また土壌の特性を用いる方法は、土壌の色を用いる方法及び土壌の養分の分析結果を用いる方法の両方の利点を相当程度併せて有するという優位性がある。 The present invention also relates to an afforestation method including predicting the growth degree of a tree in a planned plantation site before and / or after planting a tree for planting by any one of the methods described above.
The prediction method of this invention used for the said prediction is not limited, You may determine suitably considering time, a place, cost, etc. From the viewpoint of simplicity, a method using soil color is advantageous, and from a viewpoint of accuracy of prediction, a method using analysis results of soil nutrients is advantageous. In addition, the method using the characteristics of the soil has an advantage that it has a considerable combination of the advantages of both the method using the color of the soil and the method using the analysis result of the nutrients of the soil.
本発明の方法を行うに際して用いられる機材は限定されないところ、以下を含む、本発明の方法を行うためのシステムは好ましい：
（ア）土壌の色及び／又は土壌の特性を測定する測定装置；及び
（イ）前記装置を搭載した移動機具。
かかるシステムのうち、測定装置を用いて得られた測定値を樹高に換算して樹木の成長度合いを予測する機能を備えた機具をさらに具備するシステムはより好ましく、移動機具が地上移動型機具又は空中移動型機具であるシステムはより一層好ましい。
上記システムの例として、色情報取得装置及び／又は赤外分光測定装置を搭載した移動機具を移動手段として含むものが挙げられる。当該移動機具として、トラクターや林業機械（ハーベスタ及びスキッダ等）といった地上移動型機具、ならびに有人航空機や無人航空機（ドローン及びラジコンヘリ等）といった空中移動型機具が例示され、トラクター及びハーベスタは好ましく、無人航空機も好ましい。 The equipment used in performing the method of the present invention is not limited, but a system for performing the method of the present invention is preferred, including:
(A) a measuring device for measuring soil color and / or soil properties; and (a) a mobile device equipped with the device.
Among such systems, a system further comprising a device having a function of predicting the growth degree of a tree by converting a measurement value obtained using a measuring device into a tree height is more preferable, and the mobile device is a ground mobile device or A system that is an airborne instrument is even more preferred.
As an example of the above-mentioned system, there is one that includes a mobile device equipped with a color information acquisition device and / or an infrared spectroscopic measurement device as a moving means. Examples of such mobile equipment include ground mobile equipment such as tractors and forestry machines (harvesters and skidder), and airborne mobile equipment such as manned aircraft and unmanned aircraft (drone and radio control helicopters). Tractors and harvesters are preferred, and unmanned Aircraft are also preferred.
（ア）土壌の色及び／又は土壌の特性を測定する測定装置；及び
（イ）前記装置を搭載した移動機具。
かかるシステムのうち、測定装置を用いて得られた測定値を樹高に換算して樹木の成長度合いを予測する機能を備えた機具をさらに具備するシステムはより好ましく、移動機具が地上移動型機具又は空中移動型機具であるシステムはより一層好ましい。
上記システムの例として、色情報取得装置及び／又は赤外分光測定装置を搭載した移動機具を移動手段として含むものが挙げられる。当該移動機具として、トラクターや林業機械（ハーベスタ及びスキッダ等）といった地上移動型機具、ならびに有人航空機や無人航空機（ドローン及びラジコンヘリ等）といった空中移動型機具が例示され、トラクター及びハーベスタは好ましく、無人航空機も好ましい。 The equipment used in performing the method of the present invention is not limited, but a system for performing the method of the present invention is preferred, including:
(A) a measuring device for measuring soil color and / or soil properties; and (a) a mobile device equipped with the device.
Among such systems, a system further comprising a device having a function of predicting the growth degree of a tree by converting a measurement value obtained using a measuring device into a tree height is more preferable, and the mobile device is a ground mobile device or A system that is an airborne instrument is even more preferred.
As an example of the above-mentioned system, there is one that includes a mobile device equipped with a color information acquisition device and / or an infrared spectroscopic measurement device as a moving means. Examples of such mobile equipment include ground mobile equipment such as tractors and forestry machines (harvesters and skidder), and airborne mobile equipment such as manned aircraft and unmanned aircraft (drone and radio control helicopters). Tractors and harvesters are preferred, and unmanned Aircraft are also preferred.
次に実施例に基づいて本発明を更により詳細に説明するが、本発明はこれらの実施例によって何等制限されるものではない。
Next, the present invention will be described in further detail based on examples, but the present invention is not limited to these examples.
（実施例１）
樹木の成長（樹高）を予測する方法についての試験（１）
[試験方法]
・試験地 ベトナム国ロンアン省内のアカシア・ハイブリッド植林地１年生。
・同一プロット内で樹木の成長にバラつき（３０ｃｍ～４３０ｃｍ）が生じていた。土壌成分の相違が原因と思われたが、明確には判じられなかった。
・樹木下の土壌０～５ｃｍをサンプリングした（合計２２地点）
・土壌の色（以下「Ａ法」ということがある）、土壌の特性（以下「Ｂ法」ということがある）又は土壌の養分（以下「Ｃ法」ということがある）を用いる方法のそれぞれにつき、説明変数と樹高との相関を調査した。Ａ法～Ｃ法のそれぞれにおいて用いられた手法は本明細書本文において上記したとおりである。 (Example 1)
Test for predicting tree growth (tree height) (1)
[Test method]
・ Test site First-year acacia hybrid plantation in Long An province, Vietnam.
-There was variation in the growth of trees (30 cm to 430 cm) within the same plot. The difference in soil composition was thought to be the cause, but it was not clearly understood.
・ Sampling 0-5cm under the tree (22 points in total)
Each of the methods using the color of the soil (hereinafter sometimes referred to as “Method A”), the characteristics of the soil (hereinafter sometimes referred to as “Method B”) or the nutrient of the soil (hereinafter also referred to as “Method C”) Therefore, the correlation between explanatory variables and tree height was investigated. The methods used in each of the methods A to C are as described above in the present specification.
樹木の成長（樹高）を予測する方法についての試験（１）
[試験方法]
・試験地 ベトナム国ロンアン省内のアカシア・ハイブリッド植林地１年生。
・同一プロット内で樹木の成長にバラつき（３０ｃｍ～４３０ｃｍ）が生じていた。土壌成分の相違が原因と思われたが、明確には判じられなかった。
・樹木下の土壌０～５ｃｍをサンプリングした（合計２２地点）
・土壌の色（以下「Ａ法」ということがある）、土壌の特性（以下「Ｂ法」ということがある）又は土壌の養分（以下「Ｃ法」ということがある）を用いる方法のそれぞれにつき、説明変数と樹高との相関を調査した。Ａ法～Ｃ法のそれぞれにおいて用いられた手法は本明細書本文において上記したとおりである。 (Example 1)
Test for predicting tree growth (tree height) (1)
[Test method]
・ Test site First-year acacia hybrid plantation in Long An province, Vietnam.
-There was variation in the growth of trees (30 cm to 430 cm) within the same plot. The difference in soil composition was thought to be the cause, but it was not clearly understood.
・ Sampling 0-5cm under the tree (22 points in total)
Each of the methods using the color of the soil (hereinafter sometimes referred to as “Method A”), the characteristics of the soil (hereinafter sometimes referred to as “Method B”) or the nutrient of the soil (hereinafter also referred to as “Method C”) Therefore, the correlation between explanatory variables and tree height was investigated. The methods used in each of the methods A to C are as described above in the present specification.
[結果]
Ａ法、Ｂ法及びＣ法を用いた結果を以下に示す。
（１）Ａ法
ｐＨ及びＥＣを考慮せずＬ*、ａ*及びｂ*のみを用いた場合、予測高さと実測高さとの決定係数（Ｒ２）は０．４４０７であった（図２Ａ）。
また、一般化線形モデルにより2次までの交互作用を含めて作成した予測式から得られた予測高さと実測高さとの決定係数は０．５４７８に向上し（図２Ｂ）、さらにｐＨ及びＥＣの測定値を考慮すると決定係数は０．７９８７にさらに向上した（図２Ｃ）。
（２）Ｂ法
予測高さと実測高さとの決定係数は０．８４１９であった（図３）。なお５つのサンプルについての赤外スペクトルデータ及びそのスムージング処理後のデータ（チャート）を図４Ａ及び図４Ｂに示す。
（３）Ｃ法
土壌養分として通常用いられることの多い、ｐＨ、ＥＣ、全炭素、全窒素、交換性Ｃａ、Ｍｇ、Ｋを用いて一般化線形モデルにより、予測式を作成した。交互作用を含まない１次式により作成した場合、決定係数は０．６５９９であった（「養分１法」、図５Ａ）。
次に土壌養分として通常用いられることの多い、ｐＨ、ＥＣ、全炭素、全窒素、交換性Ｃａ、Ｍｇ、Ｋに加えて、通常では植物の成長に影響が少なかったり、有害である成分も含めて、一般化線形モデルにより、予測式を作成した。交互作用を含まない１次式により作成した。養分１法に用いられた養分種に加えて、Ａｌ、Ｂ、Ｆｅ、Ｎａ、Ｓｉ、Ｚｎを説明変数に加えたところ、決定係数は０．８８１６となりより予測精度の高い測定が可能となった（「養分２法」、図５Ｂ）。
なお、養分２法における回帰直線の作成に用いられた各養分種に割り当てられた係数は下表のとおりである（表１）。切片についても同様に下表に挿入して数値を示した。 [result]
The results using the A method, the B method and the C method are shown below.
(1) Method A When only L *, a *, and b * were used without considering pH and EC, the coefficient of determination (R 2 ) between the predicted height and the measured height was 0.4407 (FIG. 2A). .
In addition, the coefficient of determination between the predicted height and the measured height obtained from the prediction formula created by including the interaction up to the second order by the generalized linear model is improved to 0.5478 (FIG. 2B), and the pH and EC are further improved. Taking into account the measured value, the coefficient of determination was further improved to 0.7987 (FIG. 2C).
(2) Method B The determination coefficient between the predicted height and the actually measured height was 0.8419 (FIG. 3). In addition, the infrared spectrum data about five samples and the data (chart) after the smoothing process are shown in FIGS. 4A and 4B.
(3) Method C A prediction formula was created by a generalized linear model using pH, EC, total carbon, total nitrogen, exchangeable Ca, Mg, and K, which are often used as soil nutrients. When prepared by a linear equation that does not include an interaction, the coefficient of determination was 0.6599 (“Nutrition 1 Method”, FIG. 5A).
Next, in addition to pH, EC, total carbon, total nitrogen, exchangeable Ca, Mg, and K, which are usually used as soil nutrients, include components that usually have little impact on plant growth or are harmful. Thus, a prediction formula was created using a generalized linear model. Created by a linear equation without interaction. When Al, B, Fe, Na, Si, and Zn were added to the explanatory variables in addition to the nutrient species used in thenutrient 1 method, the coefficient of determination was 0.8816, enabling measurement with higher prediction accuracy. ("Nutrition 2 method", FIG. 5B).
The coefficients assigned to each nutrient species used to create the regression line in thenutrient 2 method are as shown in the following table (Table 1). The section was similarly inserted in the table below and the numerical value was shown.
Ａ法、Ｂ法及びＣ法を用いた結果を以下に示す。
（１）Ａ法
ｐＨ及びＥＣを考慮せずＬ*、ａ*及びｂ*のみを用いた場合、予測高さと実測高さとの決定係数（Ｒ２）は０．４４０７であった（図２Ａ）。
また、一般化線形モデルにより2次までの交互作用を含めて作成した予測式から得られた予測高さと実測高さとの決定係数は０．５４７８に向上し（図２Ｂ）、さらにｐＨ及びＥＣの測定値を考慮すると決定係数は０．７９８７にさらに向上した（図２Ｃ）。
（２）Ｂ法
予測高さと実測高さとの決定係数は０．８４１９であった（図３）。なお５つのサンプルについての赤外スペクトルデータ及びそのスムージング処理後のデータ（チャート）を図４Ａ及び図４Ｂに示す。
（３）Ｃ法
土壌養分として通常用いられることの多い、ｐＨ、ＥＣ、全炭素、全窒素、交換性Ｃａ、Ｍｇ、Ｋを用いて一般化線形モデルにより、予測式を作成した。交互作用を含まない１次式により作成した場合、決定係数は０．６５９９であった（「養分１法」、図５Ａ）。
次に土壌養分として通常用いられることの多い、ｐＨ、ＥＣ、全炭素、全窒素、交換性Ｃａ、Ｍｇ、Ｋに加えて、通常では植物の成長に影響が少なかったり、有害である成分も含めて、一般化線形モデルにより、予測式を作成した。交互作用を含まない１次式により作成した。養分１法に用いられた養分種に加えて、Ａｌ、Ｂ、Ｆｅ、Ｎａ、Ｓｉ、Ｚｎを説明変数に加えたところ、決定係数は０．８８１６となりより予測精度の高い測定が可能となった（「養分２法」、図５Ｂ）。
なお、養分２法における回帰直線の作成に用いられた各養分種に割り当てられた係数は下表のとおりである（表１）。切片についても同様に下表に挿入して数値を示した。 [result]
The results using the A method, the B method and the C method are shown below.
(1) Method A When only L *, a *, and b * were used without considering pH and EC, the coefficient of determination (R 2 ) between the predicted height and the measured height was 0.4407 (FIG. 2A). .
In addition, the coefficient of determination between the predicted height and the measured height obtained from the prediction formula created by including the interaction up to the second order by the generalized linear model is improved to 0.5478 (FIG. 2B), and the pH and EC are further improved. Taking into account the measured value, the coefficient of determination was further improved to 0.7987 (FIG. 2C).
(2) Method B The determination coefficient between the predicted height and the actually measured height was 0.8419 (FIG. 3). In addition, the infrared spectrum data about five samples and the data (chart) after the smoothing process are shown in FIGS. 4A and 4B.
(3) Method C A prediction formula was created by a generalized linear model using pH, EC, total carbon, total nitrogen, exchangeable Ca, Mg, and K, which are often used as soil nutrients. When prepared by a linear equation that does not include an interaction, the coefficient of determination was 0.6599 (“
Next, in addition to pH, EC, total carbon, total nitrogen, exchangeable Ca, Mg, and K, which are usually used as soil nutrients, include components that usually have little impact on plant growth or are harmful. Thus, a prediction formula was created using a generalized linear model. Created by a linear equation without interaction. When Al, B, Fe, Na, Si, and Zn were added to the explanatory variables in addition to the nutrient species used in the
The coefficients assigned to each nutrient species used to create the regression line in the
（実施例２）樹木の成長（樹高）を予測する方法についての試験（２）
実施例１と同様な試験をパプア・ニューギニアのユーカリ（７年生から２０年生）について行った。
[試験方法]
・試験地：パプア・ニューギニア国のユーカリ植林地７年生から２０年生。
・１５年生時の主林木の平均樹高について地位指数４を平均とし、２ｍごとに１～７までの７段階の樹高に区分し、樹高の実測値と赤外分光法による予測値とを比較することとした。なお、「地位指数」は、あるプロットにおける主林木の平均樹高をより簡便に表すために本試験において採用された指標である。
・土壌供試サンプルとして、樹木下の土壌０～１０ｃｍの深さの地点からプロット当たり５点採取し、混合したものを各プロットにおける土壌供試サンプルとして用いた。
・土壌供試サンプルについて赤外分光スペクトルデータを得た後、前記赤外分光スペクトルデータの標準化処理を行った。さらに、樹木高さを説明変数とし、多変量解析ＰＬＳ解析を行い、検量式を作成した。前記検量式により、説明変数と樹高との相関を表す指標としての決定係数を計算し、樹木成長の推定（予測）の可否を判断した。 (Example 2) Test (2) for predicting the growth (tree height) of trees
A test similar to that of Example 1 was conducted on Papua New Guinea Eucalyptus (7th to 20th grade).
[Test method]
・ Test site: 7th to 20th year eucalyptus plantation in Papua New Guinea.
・ The average height of the main forest tree at the 15th year is averaged with a position index of 4 and divided into 7 levels from 1 to 7 every 2 m, and the measured height of the tree is compared with the predicted value by infrared spectroscopy. It was decided. “Position index” is an index adopted in this test in order to more easily express the average tree height of a main forest tree in a certain plot.
-As a soil test sample, 5 points were collected per plot from a depth of 0 to 10 cm of soil under the tree, and a mixture was used as a soil test sample in each plot.
-After obtaining infrared spectrum data for the soil sample, the infrared spectrum data was standardized. Furthermore, using the tree height as an explanatory variable, multivariate analysis PLS analysis was performed to create a calibration formula. Using the calibration formula, a coefficient of determination as an index representing the correlation between the explanatory variable and the tree height was calculated, and whether or not estimation (prediction) of tree growth was possible was determined.
実施例１と同様な試験をパプア・ニューギニアのユーカリ（７年生から２０年生）について行った。
[試験方法]
・試験地：パプア・ニューギニア国のユーカリ植林地７年生から２０年生。
・１５年生時の主林木の平均樹高について地位指数４を平均とし、２ｍごとに１～７までの７段階の樹高に区分し、樹高の実測値と赤外分光法による予測値とを比較することとした。なお、「地位指数」は、あるプロットにおける主林木の平均樹高をより簡便に表すために本試験において採用された指標である。
・土壌供試サンプルとして、樹木下の土壌０～１０ｃｍの深さの地点からプロット当たり５点採取し、混合したものを各プロットにおける土壌供試サンプルとして用いた。
・土壌供試サンプルについて赤外分光スペクトルデータを得た後、前記赤外分光スペクトルデータの標準化処理を行った。さらに、樹木高さを説明変数とし、多変量解析ＰＬＳ解析を行い、検量式を作成した。前記検量式により、説明変数と樹高との相関を表す指標としての決定係数を計算し、樹木成長の推定（予測）の可否を判断した。 (Example 2) Test (2) for predicting the growth (tree height) of trees
A test similar to that of Example 1 was conducted on Papua New Guinea Eucalyptus (7th to 20th grade).
[Test method]
・ Test site: 7th to 20th year eucalyptus plantation in Papua New Guinea.
・ The average height of the main forest tree at the 15th year is averaged with a position index of 4 and divided into 7 levels from 1 to 7 every 2 m, and the measured height of the tree is compared with the predicted value by infrared spectroscopy. It was decided. “Position index” is an index adopted in this test in order to more easily express the average tree height of a main forest tree in a certain plot.
-As a soil test sample, 5 points were collected per plot from a depth of 0 to 10 cm of soil under the tree, and a mixture was used as a soil test sample in each plot.
-After obtaining infrared spectrum data for the soil sample, the infrared spectrum data was standardized. Furthermore, using the tree height as an explanatory variable, multivariate analysis PLS analysis was performed to create a calibration formula. Using the calibration formula, a coefficient of determination as an index representing the correlation between the explanatory variable and the tree height was calculated, and whether or not estimation (prediction) of tree growth was possible was determined.
[結果]
樹高についての説明変数（予測値）と実測値との決定係数は０．４８５２であり（図６）、ユーカリについても成長が予測できることが明らかになった。
土壌供試サンプルのサンプリングをより深い地点の土壌も併せて行うことにより、より精度の高い成長の予測が可能になることも示唆された。 [result]
The coefficient of determination between the explanatory variable (predicted value) and the actual measured value for the tree height was 0.4852 (FIG. 6), and it became clear that the growth of eucalyptus can also be predicted.
It was also suggested that more accurate predictions of growth can be made by sampling soil samples together with deeper soils.
樹高についての説明変数（予測値）と実測値との決定係数は０．４８５２であり（図６）、ユーカリについても成長が予測できることが明らかになった。
土壌供試サンプルのサンプリングをより深い地点の土壌も併せて行うことにより、より精度の高い成長の予測が可能になることも示唆された。 [result]
The coefficient of determination between the explanatory variable (predicted value) and the actual measured value for the tree height was 0.4852 (FIG. 6), and it became clear that the growth of eucalyptus can also be predicted.
It was also suggested that more accurate predictions of growth can be made by sampling soil samples together with deeper soils.
本発明によれば、従来の方法が達成し得なかった植林前に樹木の成長を予測する方法が提供される。したがって、本発明は、植林産業および関連産業の発展に寄与するところ大である。
According to the present invention, a method is provided for predicting the growth of trees before afforestation that conventional methods could not achieve. Therefore, the present invention greatly contributes to the development of afforestation industry and related industries.
According to the present invention, a method is provided for predicting the growth of trees before afforestation that conventional methods could not achieve. Therefore, the present invention greatly contributes to the development of afforestation industry and related industries.
Claims (10)
- 以下の工程を含む、対象である土地に樹木を植えた際の該樹木の成長度合いを予測する方法：
（ａ）上記土地の近傍に栽植された複数の樹木についての成長に関するデータを取得する工程
（ｂ）前記樹木のそれぞれの下の土壌について、土壌の色、土壌の特性及び／又は土壌の養分を分析する工程
（ｃ）該分析の結果から説明変数を得る工程
（ｄ）該説明変数を用いて、前記樹木についての成長に関するデータとの検量式を得る工程
（ｅ）上記土地の土壌について土壌の色、土壌の特性及び／又は土壌の養分を分析し、当該分析結果を前記検量式に当てはめて、上記土地に樹木を植えた際の該樹木の成長度合いを計算し、予測する工程。 A method for predicting the degree of growth of a tree when planted on the target land, including the following steps:
(A) The process of acquiring the data regarding the growth about the several trees planted in the vicinity of the said land (b) About the soil under each of the said trees, soil color, soil characteristics, and / or soil nutrients Step of analyzing (c) Step of obtaining an explanatory variable from the result of the analysis (d) Step of obtaining a calibration formula with data relating to the growth of the tree using the explanatory variable (e) Analyzing the color, soil characteristics and / or soil nutrients, applying the analysis results to the calibration formula, and calculating and predicting the degree of growth of the trees when the trees are planted on the land. - 土壌の色を分析することを含み、分析の結果得られる説明変数として少なくともＬ*、ａ*、ｂ*を含む請求項１に記載の方法。 The method according to claim 1, comprising analyzing soil color, and including at least L *, a *, and b * as explanatory variables obtained as a result of the analysis.
- 土壌の特性を赤外分光法により分析することを含み、分析の結果得られる説明変数として少なくとも赤外分光スペクトルを含む請求項１に記載の方法。 The method according to claim 1, comprising analyzing soil characteristics by infrared spectroscopy, and including at least an infrared spectrum as an explanatory variable obtained as a result of the analysis.
- 土壌の養分を分析することを含み、分析の結果得られる説明変数として少なくとも全炭素、全窒素、交換性Ｃａ、交換性Ｋ及び交換性Ｍｇの量を含む請求項１に記載の方法。 The method according to claim 1, comprising analyzing soil nutrients, and including at least total carbon, total nitrogen, exchangeable Ca, exchangeable K, and exchangeable Mg as explanatory variables obtained as a result of the analysis.
- 土壌の特性及び土壌の養分を分析することを含む請求項１に記載の方法。 2. The method of claim 1, comprising analyzing soil properties and soil nutrients.
- 土壌の色を分析することをさらに含む請求項５に記載の方法。 6. The method of claim 5, further comprising analyzing the color of the soil.
- 請求項１～６のいずれかに記載の方法により、植林予定地における樹木の成長度合いを植林のための樹木の植栽前及び／又は植栽後に予測することを含む、植林方法。 A planting method comprising predicting the degree of growth of a tree in a planned plantation site before and / or after planting a tree for planting by the method according to any one of claims 1 to 6.
- 以下を含む、請求項１～６のいずれかに記載の方法を行うためのシステム：
（ア）土壌の色及び／又は土壌の特性を測定する測定装置；及び
（イ）前記装置を搭載した移動機具。 A system for performing the method of any of claims 1-6, comprising:
(A) a measuring device for measuring soil color and / or soil properties; and (a) a mobile device equipped with the device. - 測定装置を用いて得られた測定値を樹高に換算して樹木の成長度合いを予測する機能を備えた機材をさらに具備する、請求項８に記載のシステム。 The system according to claim 8, further comprising equipment having a function of predicting a growth degree of a tree by converting a measured value obtained using a measuring device into a tree height.
- 移動機具が地上移動型機具又は空中移動型機具である、請求項８又は９に記載のシステム。
The system according to claim 8 or 9, wherein the mobile device is a ground mobile device or an airborne mobile device.
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| WO2018193984A1 (en) * | 2017-04-17 | 2018-10-25 | 日本製紙株式会社 | Method for predicting productivity of afforested area |
| CN110853699A (en) * | 2019-10-30 | 2020-02-28 | 北京林业大学 | Method for establishing single-tree growth model under large-area condition |
| CN111462312A (en) * | 2020-04-01 | 2020-07-28 | 中国林业科学研究院资源信息研究所 | Dynamic visual simulation method and system for subbranch height considering spatial structure |
| WO2021070953A1 (en) * | 2019-10-09 | 2021-04-15 | 株式会社日立製作所 | Powder mixing system, and powder mixing method |
| JP6906824B1 (en) * | 2020-12-11 | 2021-07-21 | 日本森林総研株式会社 | Information processing device, tree growth rate prediction system, tree growth rate prediction method and program |
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| JP2005017115A (en) * | 2003-06-26 | 2005-01-20 | Iwate Prefecture | Estimation device of organic component for soil |
| JP2008079549A (en) * | 2006-09-28 | 2008-04-10 | Mitsubishi Paper Mills Ltd | Method for evaluating tree growth |
| JP2008203153A (en) * | 2007-02-21 | 2008-09-04 | Yanmar Co Ltd | Soil diagnostic method and soil diagnostic device |
| JP2008206421A (en) * | 2007-02-23 | 2008-09-11 | Kansai Electric Power Co Inc:The | Mangrove growth forecasting system, mangrove afforestation right land judging system, mangrove growth forecasting method, and mangrove afforestation right land judging method |
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| JP2008079549A (en) * | 2006-09-28 | 2008-04-10 | Mitsubishi Paper Mills Ltd | Method for evaluating tree growth |
| JP2008203153A (en) * | 2007-02-21 | 2008-09-04 | Yanmar Co Ltd | Soil diagnostic method and soil diagnostic device |
| JP2008206421A (en) * | 2007-02-23 | 2008-09-11 | Kansai Electric Power Co Inc:The | Mangrove growth forecasting system, mangrove afforestation right land judging system, mangrove growth forecasting method, and mangrove afforestation right land judging method |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018193984A1 (en) * | 2017-04-17 | 2018-10-25 | 日本製紙株式会社 | Method for predicting productivity of afforested area |
| JPWO2018193984A1 (en) * | 2017-04-17 | 2019-11-07 | 日本製紙株式会社 | How to predict plantation productivity |
| WO2021070953A1 (en) * | 2019-10-09 | 2021-04-15 | 株式会社日立製作所 | Powder mixing system, and powder mixing method |
| CN110853699A (en) * | 2019-10-30 | 2020-02-28 | 北京林业大学 | Method for establishing single-tree growth model under large-area condition |
| CN111462312A (en) * | 2020-04-01 | 2020-07-28 | 中国林业科学研究院资源信息研究所 | Dynamic visual simulation method and system for subbranch height considering spatial structure |
| JP6906824B1 (en) * | 2020-12-11 | 2021-07-21 | 日本森林総研株式会社 | Information processing device, tree growth rate prediction system, tree growth rate prediction method and program |
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| JPWO2016043007A1 (en) | 2017-06-29 |
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