CN115131370A - Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment - Google Patents
Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment Download PDFInfo
- Publication number
- CN115131370A CN115131370A CN202210786681.3A CN202210786681A CN115131370A CN 115131370 A CN115131370 A CN 115131370A CN 202210786681 A CN202210786681 A CN 202210786681A CN 115131370 A CN115131370 A CN 115131370A
- Authority
- CN
- China
- Prior art keywords
- forest
- unit
- sampling
- block
- carbon
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 98
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 98
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 46
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 46
- 239000001301 oxygen Substances 0.000 title claims abstract description 46
- 230000009919 sequestration Effects 0.000 title claims abstract description 42
- 238000011156 evaluation Methods 0.000 title claims abstract description 18
- 230000008901 benefit Effects 0.000 title claims abstract description 12
- 238000005070 sampling Methods 0.000 claims abstract description 116
- 238000005192 partition Methods 0.000 claims abstract description 74
- 238000000034 method Methods 0.000 claims abstract description 60
- 239000002028 Biomass Substances 0.000 claims abstract description 55
- 238000011835 investigation Methods 0.000 claims abstract description 31
- 230000011218 segmentation Effects 0.000 claims abstract description 30
- 230000008569 process Effects 0.000 claims description 14
- 238000003709 image segmentation Methods 0.000 claims description 3
- 241000894007 species Species 0.000 description 38
- 241000196324 Embryophyta Species 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 9
- 240000005020 Acaciella glauca Species 0.000 description 4
- 239000000203 mixture Substances 0.000 description 4
- 239000002023 wood Substances 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 235000018185 Betula X alpestris Nutrition 0.000 description 2
- 235000018212 Betula X uliginosa Nutrition 0.000 description 2
- 241000218691 Cupressaceae Species 0.000 description 2
- 235000018782 Dacrydium cupressinum Nutrition 0.000 description 2
- 241000218657 Picea Species 0.000 description 2
- 240000007263 Pinus koraiensis Species 0.000 description 2
- 235000011615 Pinus koraiensis Nutrition 0.000 description 2
- 235000013697 Pinus resinosa Nutrition 0.000 description 2
- 235000008582 Pinus sylvestris Nutrition 0.000 description 2
- 241000219000 Populus Species 0.000 description 2
- 241000219492 Quercus Species 0.000 description 2
- 241000124033 Salix Species 0.000 description 2
- 241000328973 Tilia miqueliana Species 0.000 description 2
- 241001106462 Ulmus Species 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 239000001839 pinus sylvestris Substances 0.000 description 2
- 238000004904 shortening Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010170 biological method Methods 0.000 description 1
- 210000000481 breast Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20152—Watershed segmentation
-
- 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
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Geometry (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Image Analysis (AREA)
Abstract
A method and equipment for evaluating carbon sequestration, oxygen release capacity and benefit of forest belong to the technical field of forestry. In order to solve the problem that the accuracy of an evaluation result is to be further improved in the existing mode of determining sampling representativeness based on searching aiming at the condition that forest diversity is relatively uniform. The method comprises the steps of collecting an image over a block to be detected, determining the boundary of the block to be detected, and segmenting the image by using a watershed algorithm; then determining a first sampling point according to the centroid of the unit segmentation region and the intersection point of the connecting lines of the adjacent centroids; determining a second sampling point based on the global chromaticity gradient and the half-link chromaticity difference of each unit partition area, and simultaneously determining a third sampling point based on each unit partition area; then, forest data investigation is carried out based on sampling points, and the corresponding biomass is determined; and filling and interpolating grids divided by the image of the block to be measured according to the biomass, and obtaining the carbon reserve of the forest in the block to be measured based on the biomass of each tree species so as to obtain the total carbon reserve of the forest.
Description
Technical Field
The invention relates to a method for evaluating carbon sequestration capacity, oxygen release and benefit of a forest, and belongs to the technical field of forestry.
Background
Carbon sequestration refers to the process of replacing CO2 directly into the atmosphere in a way that captures the carbon and safely sequesters it. The method mainly comprises a biological method, a micrometeorological method, a new technical model estimation method applying remote sensing and the like, a ground carbon isotope method and the like. The biomass method is determined by calculating parameters such as biomass on a unit area, forest area, distribution ratio of the biomass in each organ of the tree, average carbon content of each organ of the tree and the like, although the biomass method has the characteristics of directness, clearness and simple technology, the accuracy is realized by accurately investigating the biomass on the unit area and the like, and the method needs a great amount of detailed investigation, so that the workload is greatly increased, and the investigation generally needs long time, so that the investigation of the whole area cannot be reflected on the basis of the same time node, and the investigation accuracy is reduced to a certain extent.
If the artificial influence factors and the change indexes of the carbon storage amount are neglected, the carbon storage amount can be estimated by taking the carbon storage amount calculation mode in the carbon sink as a reference, wherein in order to solve the problems existing in the traditional biomass method, the application number 202011231747.X of northeast forestry university and the like' a forest carbon storage amount measuring method, a forest data survey is carried out through the actual forest space range corresponding to the sampling survey unit, and the biomass of the tree species in the corresponding grid is determined; and analyzing by combining the integrally estimated region based on the image of the region subjected to sampling investigation, thereby realizing the estimation of the biomass of each tree species in the grid, and further determining the biomass and forest carbon storage amount of each tree species in the block to be detected. The method can reduce the workload to a great extent and improve the estimation accuracy. In fact, the method also provides ideas and technical support for assessment of the carbon sequestration capacity of the forest, but when the method is used for assessment of the carbon sequestration capacity, problems exist, such as large calculation amount and low calculation efficiency of the method. More importantly, when the forest diversity is more uniform, especially when the crowns of the upper tree layers (trees are higher) in the forest are relatively uniform, the accuracy of searching and determining the search grid lines is influenced, so that the representativeness of the sampling grid is influenced, and the accuracy of the final evaluation result is influenced.
Disclosure of Invention
The method aims to solve the problem that the accuracy of an evaluation result is to be further improved in the conventional sampling representativeness determination mode based on searching under the condition of relatively uniform forest diversity.
A forest carbon sequestration capacity assessment method comprises the following steps:
s1, recording an area to be subjected to forest carbon sequestration capacity evaluation as a block to be detected, collecting an image over the block to be detected and determining the boundary of the block to be detected; extracting an image in the boundary of the block to be detected and recording the image as an image of the block to be detected;
s2, aiming at the block image to be detected, carrying out image segmentation by using a watershed algorithm, and marking each region in the segmentation result as a unit segmentation region;
s3, obtaining the centroid of each unit segmentation area; then connecting the centroids of two adjacent unit segmentation areas, and recording as an adjacent centroid connecting line, and recording the intersection point of the boundary of the two adjacent unit segmentation areas and the adjacent centroid connecting line as an adjacent centroid connecting line intersection point;
s4, recording the intersection point of the connection line of the centroid of each unit partition area and the adjacent centroid as a first sampling point; the first sampling point is marked as N1;
s5, dividing the block image to be detected into images corresponding to RGB three channels, extracting a G channel image and taking a G numerical value corresponding to a pixel as a chromatic value; the minimum chroma value in the G-channel image is denoted as Gmin, the maximum chroma value is denoted as Gmax,
determining a global chromaticity gradient delta according to the minimum chromaticity value Gmin and the maximum chromaticity value Gmax in the G channel image;
for each unit division area on the G channel image, marking the centroid chroma value in the unit division area I as G IC Recording the chroma value of the intersection point of the connecting lines of the adjacent centroids corresponding to the unit partition region I and the adjacent unit partition region J as G IJ Calculating the half-link chromaticity difference Δ G CJ ＝|G IC -G IJ L, |; a half-connecting line is a corresponding partial connecting line in the unit partition area I on the adjacent centroid connecting line; setting a second sampling point on a half-connecting line in the unit partition area I, wherein the number of the second sampling points is
Represents rounding down;
recording an area between two adjacent semi-connecting lines in each unit partition area I as a semi-connecting line interval, and if one unit partition area I only has one semi-connecting line, taking an area except the semi-connecting line in the whole unit partition area I as the semi-connecting line interval;
the second sampling points in all the unit partition areas are marked as N2;
s7, judging whether the N1+ N2 is larger than or equal to the pre-designed total sampling point number N, if so, determining the sampling point number N1+ N2 as a final sampling point number N', and executing a step S8;
otherwise, for each unit partition area on the G-channel image, marking the minimum chroma value in the unit partition area I as Gmin I The maximum chroma value is denoted as Gmax I Calculating a unit division area chromaticity difference Δ G Im ＝(Gmax I -Gmin I ) Setting a third sampling point in the unit segmentation region I according to the chromaticity difference of the unit segmentation region; recording third sampling points of all unit segmentation areas as N4, and determining the number of the sampling points N1+ N2+ N4 as the number of final sampling points N';
s8, determining the spatial position of the sampling investigation unit according to the physical spatial positions corresponding to all the sampling points in the number N';
s9, carrying out forest data investigation in the actual space range corresponding to the sampling investigation unit; determining the biomass B of the corresponding tree species alpha in the sampling investigation unit according to the biomass equation corresponding to the tree species TREE-i,α,I ；
S10, carrying out grid division on the image of the block to be detected, and respectively carrying out grid division on different tree species alpha according to the biomass B of the tree species alpha corresponding to the sampling survey unit TREE-i,α,I Filling and interpolating biomass on the grids, and further determining the biomass of each tree species in each grid;
then, determining the biomass B of the ith carbon layer of each tree species in the image of the block to be detected according to the biomass of each tree species in each grid TREE-i,α ；
S11, according to the ith carbon of each tree species in the block to be testedBiomass of layer B TREE-i,α Obtaining the carbon reserve C of the ith carbon layer of the forest in the block to be detected TREE-i,α ；
Finally, according to the carbon reserve C of the ith carbon layer of the forest in the block to be detected TREE-i,α Obtaining the total carbon reserve C of the forest TREE-BSL,α Namely the carbon sequestration capacity of forests.
Further, the global chromaticity gradient δ is as follows
Wherein min (·, ·) represents the minimum value taken therein; l, W is the maximum length and width in the physical space corresponding to the block to be measured; l1 and W1 are the length and width of the sampling investigation unit in the corresponding physical space; chi is a gradient dispensing coefficient, and when L, W, L1 and W1 units are meters, the chi takes a value of 100.
Further, the process of setting the third sample point in the unit partitioned area I according to the unit partitioned area chromaticity difference includes the steps of:
the third sampling point N3 in all the unit divided regions is N- (N1+ N2); the number of third sampling points in each unit partition region I is the number of corresponding third sampling points distributed from N3 according to the ratio of the chromaticity difference of all the unit partition regions;
in the process of obtaining the corresponding number of third sampling points from N3 according to the ratio of the chroma differences of all the unit partition areas in each unit partition area I, if the number of the third sampling points obtained by the unit partition area I according to the ratio is less than 1, the number of the third sampling points obtained by the unit partition area I according to the ratio is set to be 1, otherwise, the corresponding number obtained from N3 according to the ratio of the chroma differences of all the unit partition areas in each unit partition area I is rounded downwards and is used as the number of the third sampling points in the unit partition area I;
and distributing the third sampling point in the unit partition area I to each half-connecting line interval.
Further, the process of allocating the third sampling point in the unit partition area I to each half-link interval is as follows:
and aiming at the third sampling point in the unit segmentation region I, distributing three sampling points for each half-link interval according to the area ratio of the area of each half-link interval to the area of the unit segmentation region I.
Further, the range of the sampling survey unit is L1 × W1;
wherein N is the number of the total sampling points which are pre-designed,
representing upward rounding, wherein L, W respectively represents the maximum length and the maximum width in the physical space corresponding to the block to be measured; k is a radical of 1 、k 2 Adjusting coefficients for the samples;
if L1 xW 1 is less than 5 m x 5 m, the spatial range of the sample survey unit is set to 5 m x 5 m.
Further, biomass B of the corresponding tree species alpha in the sampling survey unit TREE-i,α,I
Wherein,
for the biomass B of the single forest plant corresponding to the tree species alpha in the sampling survey unit α Average value of (1), R α The ratio (dimensionless) of the underground biomass of the forest tree to the aboveground biomass of the tree species alpha, N TREE-i,α,I For sampling toneLooking up the number of plants of the tree species alpha in the actual space range corresponding to the unit I, A TREE-i The area of the ith carbon layer in the real space corresponding to the real space range corresponding to the sampling investigation unit.
A forest oxygen release capacity assessment method comprises the following steps:
firstly, evaluating the carbon sequestration capacity of a forest in a block to be tested by utilizing a forest carbon sequestration capacity evaluation method; and estimating the oxygen release amount of the forest according to the relation between plant carbon sequestration and oxygen release, and estimating the oxygen release capacity of the forest according to the oxygen release amount of the forest.
A method for evaluating forest carbon sequestration and oxygen release benefits comprises the following steps:
firstly, evaluating the carbon fixing capacity and/or oxygen releasing capacity of a forest in a block to be tested by utilizing a forest oxygen releasing capacity evaluation method; then determining the carbon storage amount and/or oxygen release amount in a certain period of time;
and finally, determining the carbon fixing and oxygen releasing benefits according to the carbon storage and/or oxygen releasing value in the corresponding time based on the carbon storage and/or oxygen releasing in a certain period of time.
A forest carbon sequestration capacity assessment device comprises a processor and a memory, wherein at least one instruction is stored in the memory and is loaded and executed by the processor to realize the forest carbon sequestration capacity assessment method.
A forest oxygen release capacity assessment apparatus comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to realize a forest oxygen release capacity assessment method.
Has the beneficial effects that:
the method and the device have the advantages that the image is segmented by utilizing the characteristics of the forest in the image, then the image is partitioned according to the characteristics embodied by different areas based on the internal characteristics of the segmentation of the different areas in the image, compared with a grid searching mode, the characteristics of the image can be well utilized, even if the forest diversity is uniform, the method and the device can effectively and fully utilize the image difference presented by the different characteristics of the forest to determine the sampling point, ensure that the sampling is carried out according to the actual forest difference embodied by the image, ensure that the distribution condition of the forest plant diversity of the investigation area can be fully and truly embodied in the investigation, and further improve the accuracy of the determined evaluation result.
Meanwhile, compared with the prior art, particularly the process of judging the peripheral grids of each seed grid in the 202011231747.X scheme, the scheme of the invention has simple calculation, does not need to search for the grids, and equivalently reduces the number of cyclic judgment, thereby greatly saving the calculation amount and shortening the calculation and estimation time.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The first specific implementation way is as follows: the present embodiment is described in connection with figure 1,
the embodiment is a method for evaluating the carbon sequestration capacity of a forest, which comprises the following steps:
s1, recording an area to be subjected to forest carbon sequestration capacity evaluation as a block to be detected, collecting an image over the block to be detected and determining the boundary of the block to be detected; the image in the boundary of the block to be detected is extracted and recorded as the image of the block to be detected, so that the image of the block to be detected can be processed only subsequently, the calculated amount can be saved, and the subsequent processing process can be prevented from being influenced by the diversity distribution of plants outside the block to be detected as much as possible;
it should be noted that the process of determining the boundary of the block to be detected may be determined based on the above-air acquired image, or may be determined in other manners, for example, by using a positioning system such as a GPS positioning system or a beidou positioning system, or by using a remote sensing image in combination with a geographic marker, or the like.
In consideration of convenience and accuracy of operation, the method determines the boundary of the block to be measured of the forest carbon storage amount based on the aerial image; the process may be performed using conventional techniques, such as the method of 202011231747.X in some embodiments. Since the process is prior art, the present invention is not described in detail.
S2, aiming at the block image to be detected, carrying out image segmentation by using a watershed algorithm, and marking each region in the segmentation result as a unit segmentation region;
s3, obtaining the centroid of each unit partition area; then connecting the centroids of the two adjacent unit segmentation areas, and recording as an adjacent centroid connecting line, and recording the intersection point of the boundary of the two adjacent unit segmentation areas and the adjacent centroid connecting line as an adjacent centroid connecting line intersection point;
s4, recording the intersection point of the connecting line of the centroid of each unit partition area and the adjacent centroid as a first sampling point; the first sampling point is marked as N1;
s5, dividing the block image to be detected into images corresponding to RGB three channels, extracting a G channel image and taking a G numerical value corresponding to a pixel as a chromatic value; recording the minimum chroma value of the G channel image as Gmin, recording the maximum chroma value as Gmax, and recording the minimum chroma value and the maximum chroma value in the G channel image as Gmin and Gmax
As a result of the global chrominance gradient delta,
min (·, ·) represents the minimum value taken;
s6, mapping the unit segmentation region corresponding to the S2 and the centroids, the adjacent centroid connecting lines and the intersections of the adjacent centroid connecting lines determined in the step S3 to the G-channel image;
for each unit division area on the G channel image, the centroid chroma value in the unit division area I is marked as G IC Recording the chroma value of the intersection point of the adjacent centroid connecting lines corresponding to the unit partition region I and the adjacent unit partition region J as G IJ Calculating the half-link chromaticity difference Δ G CJ ＝|G IC -G IJ L, |; a half-connecting line is a corresponding partial connecting line in the unit partition area I on the adjacent centroid connecting line;
setting a second sampling point on the adjacent centroid connecting line (namely, a half connecting line) between the unit partition area I and the intersection point of the adjacent centroid connecting lines, wherein the second sampling point is used for samplingNumber of points is
Represents rounding down;
each unit partition area I may correspond to a plurality of half-links, an area between two half-links which are close to each other is marked as a half-link interval, if one unit partition area I has only one half-link, an area except the half-link in the whole unit partition area I is taken as the half-link interval;
the second sampling points in all the unit partition areas are marked as N2;
s7, judging whether the N1+ N2 is larger than or equal to N, if so, determining the number of the sampling points N1+ N2 as the final sampling point number N', and executing a step S8;
otherwise, for each unit partition area on the G-channel image, marking the minimum chroma value in the unit partition area I as Gmin I The maximum chroma value is denoted as Gmax I Calculating a unit division area chromaticity difference Δ G Im ＝(Gmax I -Gmin I ) And setting a third sampling point in the unit partition area I according to the unit partition area chroma difference:
the third sampling point N3 in all the unit divided regions is N- (N1+ N2); the number of third sampling points in each unit partition area I is the corresponding number of third sampling points distributed from N3 according to the ratio of the chroma differences of all the unit partition areas;
in the process of obtaining the corresponding number of third sampling points from N3 according to the ratio of the chromaticity difference of all the unit partition areas in each unit partition area I, if the number of the third sampling points obtained according to the ratio of the chromaticity difference of the unit partition areas I is less than 1, the number of the third sampling points obtained according to the ratio of the unit partition areas I is set to 1, otherwise, the corresponding number obtained from N3 according to the ratio of the chromaticity difference of all the unit partition areas in each unit partition area I is rounded downwards to be used as the number of the third sampling points in the unit partition areas I;
for a third sampling point in the unit segmentation region I, distributing three sampling points for each half-link region according to the area ratio of the area of each half-link region to the area of the unit segmentation region I;
the rounding operation is performed when the number of the sampling points is distributed to each unit partition region, so that the number of the third sampling points of all the unit partition regions may not be equal to N3 finally, the number of the third sampling points of all the unit partition regions at the moment is recorded as N4, and the number of the sampling points N1+ N2+ N4 is determined as the final number of the sampling points N';
s8, determining the spatial position of a sampling investigation unit according to the physical spatial positions corresponding to all the sampling points in the number N', wherein the range of the sampling investigation unit is L1 multiplied by W1;
wherein N is the total number of the pre-designed sampling points,
representing upward rounding, L, W being the maximum length and maximum width in the physical space corresponding to the block to be measured respectively; k is a radical of formula 1 、k 2 For sampling the adjustment coefficient, the adjustment coefficient is determined according to the physical space corresponding to the actual block to be measured, and when the unit of L, W is meter, k is 1 、k 2 The value is generally 100;
if L1 xW 1 is less than 5 m x 5 m, the spatial range of the sample survey unit is set to 5 m x 5 m;
s9, carrying out forest data investigation in the actual space range corresponding to the sampling investigation unit, wherein the investigation data can be investigated and filled according to DB23T 2475 and 2019 technical Specification for construction of forestry carbon transfer measurement and detection system and related technical regulations; the investigated forest data comprises various tree species, sample number, corresponding breast height DBH, tree height H, forest age and other data;
according toThe biomass equation corresponding to the tree species determines the biomass B corresponding to the tree species alpha in the sampling investigation unit TREE-i,α,I (ii) a Alpha belongs to the set of all tree species in the block to be tested, if no tree species alpha exists in the sampling investigation unit, the corresponding data and biomass are 0;
wherein,
for the biomass B of the single forest plant corresponding to the tree species alpha in the sampling survey unit α Average value of (1), R α The ratio (dimensionless) of the underground biomass of the forest tree to the aboveground biomass of the tree species alpha, N TREE-i,α,I The number of the plants of the tree species alpha in the actual space range corresponding to the sampling survey unit I, A TREE-i The area of the ith carbon layer in the actual space corresponding to the actual space range corresponding to the sampling investigation unit;
the biomass equation corresponding to the tree species can be determined according to the prior art, and each parameter in the biomass can be determined by referring to a specific tree species and related data in the block to be measured, for example, specific tree species and related data are shown in tables 1 to 2 for the characteristics of northeast forests.
TABLE 1 reference value of underground biomass ratio (R) of main tree species (group) of a certain province
| Tree seeds (group) | R | Tree seeds (group) | R |
| Larch leaf | 0.490 | Broad class of soft | 0.443 |
| Red pine | 0.414 | Rhizoma picrorhizae | 0.464 |
| Pinus sylvestris (a kind of Chinese character of Zhang Zi) | 0.460 | Broad leaf mixture | 0.482 |
| Korean pine | 0.396 | Class of hard broad | 0.598 |
| Spruce | 0.342 | Needle and broad mixing | 0.486 |
| Fir | 0.366 | Needle leaf mixer | 0.405 |
| Poplar | 0.378 | Miscellaneous tree | 0.515 |
| Quercus species | 0.676 | Tilia Miqueliana Maxim | 0.420 |
| Willow | 0.443 | Elm tree | 0.598 |
| Cypress wood | 0.478 | Birch wood | 0.541 |
TABLE 2 Biomass equation of major tree species in a certain province
S10, performing grid division on the block image to be detected, and respectively performing grid division on different tree species alpha according to biomass B of the tree species alpha corresponding to the sampling investigation unit TREE-i，α，I Filling and interpolating biomass on the grids, and further determining the biomass of each tree species in each grid;
then, determining the biomass B of the ith carbon layer of each tree species in the image of the block to be detected according to the biomass of each tree species in each grid TREE-i，α ；
S11, according to the biomass B of the ith carbon layer of each tree species in the block to be tested TREE-i，α Obtaining the carbon reserve C of the ith carbon layer of the forest in the block to be detected TREE-i，α (expressed as CO2 equivalents):
wherein, CF α Biomass carbon content of the tree species alpha;
the carbon content of biomass can be determined by referring to specific tree species and related data in the test area, such as the characteristics of the northeast forest, and the specific tree species and corresponding carbon content of biomass are shown in Table 3
TABLE 3 carbon Content (CF) reference value for biomass of main tree species (group) in province, unit tc (td. m.) -1
| Tree seeds (group) | CF | Tree seeds (set) | CF |
| Larch leaf | 0.521 | Broad class of soft | 0.485 |
| Red pine | 0.515 | Rhizoma picrorhizae | 0.497 |
| Pinus sylvestris (L.) Merr | 0.522 | Broad leaf mixture | 0.490 |
| Korean pine | 0.511 | Class of hard broad | 0.497 |
| Spruce | 0.521 | Needle and broad mixture | 0.510 |
| Fir | 0.496 | Needle leaf mixture | 0.491 |
| Poplar | 0.500 | Miscellaneous tree | 0.483 |
| Quercus species | 0.500 | Tilia Miqueliana Maxim | 0.439 |
| Willow | 0.485 | Elm tree | 0.497 |
| Cypress wood | 0.510 | Birch wood | 0.498 |
Finally, according to the carbon reserve C of the ith carbon layer of the forest in the block to be detected TREE-i，α Obtaining the total carbon reserve C of the forest TREE-BSL，α Namely the carbon sequestration capacity of forests.
The method and the device have the advantages that the image is segmented by utilizing the characteristics of the forest in the image, then the image is partitioned according to the characteristics embodied by different areas based on the internal characteristics of the segmentation of the different areas in the image, compared with a grid searching mode, the characteristics of the image can be well utilized, even if the forest diversity is uniform, the method and the device can effectively and fully utilize the image difference presented by the different characteristics of the forest to determine the sampling point, ensure that the sampling is carried out according to the actual forest difference embodied by the image, ensure that the distribution condition of the forest plant diversity of the investigation area can be fully and truly embodied in the investigation, and further improve the accuracy of the determined evaluation result.
Meanwhile, compared with the prior art, particularly the process of judging the peripheral grids of each seed grid in the 202011231747.X scheme, the scheme of the invention has simple calculation, does not need to search for the grids, and equivalently reduces the number of cyclic judgment, thereby greatly saving the calculation amount and shortening the calculation and estimation time.
The second embodiment is as follows:
the embodiment is a method for evaluating the oxygen release capacity of a forest, which comprises the following steps:
firstly, evaluating the carbon sequestration capacity of a forest in a block to be tested by a forest carbon sequestration capacity evaluation method;
and then estimating the oxygen release capacity of the forest according to the relationship between the carbon sequestration and the oxygen release of the plants.
The method for evaluating the carbon sequestration capacity of the forest in the block to be tested is used for evaluating the carbon sequestration capacity of the forest by using the method for evaluating the carbon sequestration capacity of the forest.
The third concrete implementation mode:
the embodiment is a method for evaluating forest carbon sequestration and oxygen release benefits, which is characterized by comprising the following steps of:
firstly, evaluating the carbon fixing capacity and/or oxygen releasing capacity of a forest in a block to be tested by utilizing a forest oxygen releasing capacity evaluation method; then determining the carbon storage amount and/or oxygen release amount in a certain period of time;
and finally, determining the carbon fixing and oxygen releasing benefits according to the carbon storage and/or oxygen releasing value in the corresponding time based on the carbon storage and/or oxygen releasing in a certain period of time.
A method for evaluating the oxygen release capacity of a forest adopts the method for evaluating the oxygen release capacity of the forest described in the second embodiment.
The fourth concrete implementation mode is as follows:
the embodiment is forest carbon sequestration capacity assessment equipment, which comprises a processor and a memory, and it should be understood that any equipment described in the present invention, which comprises the processor and the memory, may also comprise other units and modules for performing display, interaction, processing, control and other functions through signals or instructions;
the memory is stored with at least one instruction, and the at least one instruction is loaded and executed by the processor to realize the forest carbon sequestration capacity assessment method.
It should be appreciated that the memory includes a non-transitory machine-readable medium having stored thereon instructions, which may be used to program a computer system, or other electronic device. Storage media may include, but is not limited to, magnetic storage media, optical storage media; a magneto-optical storage medium comprising: read only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM), and flash memory layers; or other type of media suitable for storing electronic instructions.
The fifth concrete implementation mode:
the embodiment is forest oxygen release capacity evaluation equipment which comprises a processor and a memory, and it should be understood that the equipment comprises any equipment comprising the processor and the memory, and the equipment can also comprise other units and modules which perform display, interaction, processing, control and other functions through signals or instructions;
the memory is stored with at least one instruction which is loaded and executed by the processor to realize the forest oxygen release capacity evaluation method.
It should be appreciated that the memory includes a non-transitory machine-readable medium having stored thereon instructions, which may be used to program a computer system, or other electronic device. Storage media may include, but is not limited to, magnetic storage media, optical storage media; a magneto-optical storage medium comprising: read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM), and flash memory layers; or other type of media suitable for storing electronic instructions.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.
Claims (10)
1. A forest carbon sequestration capacity assessment method is characterized by comprising the following steps:
s1, recording an area to be subjected to forest carbon sequestration capacity evaluation as a block to be detected, collecting an image over the block to be detected and determining the boundary of the block to be detected; extracting an image in the boundary of the block to be detected and marking the image as an image of the block to be detected;
s2, aiming at the block image to be detected, carrying out image segmentation by using a watershed algorithm, and marking each region in the segmentation result as a unit segmentation region;
s3, obtaining the centroid of each unit segmentation area; then connecting the centroids of two adjacent unit segmentation areas, and recording as an adjacent centroid connecting line, and recording the intersection point of the boundary of the two adjacent unit segmentation areas and the adjacent centroid connecting line as an adjacent centroid connecting line intersection point;
s4, recording the intersection point of the connecting line of the centroid of each unit partition area and the adjacent centroid as a first sampling point; the first sampling point is marked as N1;
s5, dividing the block image to be detected into images corresponding to RGB three channels, extracting a G channel image and taking a G numerical value corresponding to a pixel as a chromatic value; the minimum chroma value in the G-channel image is denoted as Gmin, the maximum chroma value is denoted as Gmax,
determining a global chromaticity gradient delta according to the minimum chromaticity value Gmin and the maximum chromaticity value Gmax in the G channel image;
for each sheet on the G-channel imageA unit division region, wherein the chromatic value of the centroid in the unit division region I is marked as G IC Recording the chroma value of the intersection point of the adjacent centroid connecting lines corresponding to the unit partition region I and the adjacent unit partition region J as G IJ Calculating the half-link chromaticity difference Δ G CJ ＝|G IC -G IJ L, |; a half-connecting line is a corresponding partial connecting line in the unit partition area I on the adjacent centroid connecting line; setting a second sampling point on a half-connecting line in the unit partition area I, wherein the number of the second sampling points is
Represents rounding down;
recording an area between two adjacent semi-connecting lines in each unit partition area I as a semi-connecting line interval, and if one unit partition area I only has one semi-connecting line, taking an area except the semi-connecting line in the whole unit partition area I as the semi-connecting line interval;
the second sampling points in all the unit partition areas are marked as N2;
s7, judging whether the N1+ N2 is larger than or equal to the pre-designed total sampling point number N, if so, determining the sampling point number N1+ N2 as a final sampling point number N', and executing a step S8;
otherwise, for each unit partition area on the G-channel image, marking the minimum chroma value in the unit partition area I as Gmin I The maximum chroma value is denoted as Gmax I Calculating a unit division area chromaticity difference Δ G Im ＝(Gmax I -Gmin I ) Setting a third sampling point in the unit segmentation region I according to the chromaticity difference of the unit segmentation region; recording the third sampling points of all the unit segmentation areas as N4, and determining the number of the sampling points N1+ N2+ N4 as the final number of the sampling points N';
s8, determining the spatial position of the sampling investigation unit according to the physical spatial positions corresponding to all the sampling points in the number N';
s9, proceeding in the actual space range corresponding to the sampling investigation unitCarrying out forest data survey; determining the biomass B of the corresponding tree species alpha in the sampling survey unit according to the biomass equation corresponding to the tree species TREE-i,α,I ；
S10, performing grid division on the block image to be detected, and respectively performing grid division on different tree species alpha according to biomass B of the tree species alpha corresponding to the sampling investigation unit TREE-i,α,I Filling and interpolating biomass on the grids, and further determining the biomass of each tree species in each grid;
then, determining the biomass B of the ith carbon layer of each tree species in the image of the block to be detected according to the biomass of each tree species in each grid TREE-i,α ；
S11, according to the biomass B of the ith carbon layer of each tree species in the block to be tested TREE-i,α Obtaining the carbon reserve C of the ith carbon layer of the forest in the block to be detected TREE-i,α ；
Finally, according to the carbon reserve C of the ith carbon layer of the forest in the block to be detected TREE-i,α Obtaining the total carbon reserve C of the forest TREE-BSL,α Namely the carbon sequestration capacity of forests.
2. A forest carbon sequestration capacity assessment method as claimed in claim 1, characterized in that the global chromaticity gradient δ is as follows
Wherein min (·,) represents the minimum value taken therein; l, W is the maximum length and width in the physical space corresponding to the block to be measured; l1 and W1 are the length and width of the sampling investigation unit in the corresponding physical space; and chi is a gradient dispensing coefficient, and when L, W, L1 and W1 units are meters, the chi is 100.
3. The forest carbon sequestration capacity assessment method according to claim 2, wherein the process of setting the third sampling point in the unit partition area I according to the unit partition area chromaticity difference comprises the following steps:
the third sample point N3 in all the unit divisional areas is N- (N1+ N2); the number of third sampling points in each unit partition region I is the number of corresponding third sampling points distributed from N3 according to the ratio of the chromaticity difference of all the unit partition regions;
in the process of obtaining the corresponding number of third sampling points from N3 according to the ratio of the chromaticity difference of all the unit partition areas in each unit partition area I, if the number of the third sampling points obtained according to the ratio of the chromaticity difference of the unit partition areas I is less than 1, the number of the third sampling points obtained according to the ratio of the unit partition areas I is set to 1, otherwise, the corresponding number obtained from N3 according to the ratio of the chromaticity difference of all the unit partition areas in each unit partition area I is rounded downwards to be used as the number of the third sampling points in the unit partition areas I;
and distributing the third sampling point in the unit partition area I into each half-connecting line interval.
4. A forest carbon sequestration capacity assessment method according to claim 3, wherein the process of allocating the third sampling point in the unit partition area I to each half-link interval is as follows:
and aiming at the third sampling point in the unit segmentation region I, distributing three sampling points for each half-link interval according to the area ratio of the area of each half-link interval to the area of the unit segmentation region I.
5. The method for evaluating the carbon sequestration capacity of the forest as claimed in claim 4, wherein the range of the sampling survey unit is L1 xW 1;
wherein N is the number of the total sampling points which are pre-designed,
representing upward rounding, wherein L, W respectively represents the maximum length and the maximum width in the physical space corresponding to the block to be measured; k is a radical of 1 、k 2 Adjusting coefficients for the samples;
if L1 xW 1 is less than 5 m x 5 m, the spatial range of the sample survey unit is set to 5 m x 5 m.
6. A method as claimed in any one of claims 1 to 5, wherein the biomass B of the sample survey unit corresponding to the species α is determined by the carbon sequestration capacity of the forest TREE-i,α,I
Wherein,
the biomass B of the single forest plant corresponding to the tree species alpha in the sampling survey unit α Average value of (1), R α The ratio (dimensionless) of the forest underground biomass to the above-ground biomass of the tree species alpha, N TREE-i,α,I The number of the plants of the tree species alpha in the actual space range corresponding to the sampling investigation unit I, A TREE-i The area of the ith carbon layer in the real space corresponding to the real space range corresponding to the sampling investigation unit.
7. A forest oxygen release capacity assessment method is characterized by comprising the following steps:
firstly, evaluating the carbon sequestration capacity of the forest in the block to be tested by using the carbon sequestration capacity evaluation method for the forest as claimed in claim 6; and estimating the oxygen release amount of the forest according to the relation between plant carbon sequestration and oxygen release, and estimating the oxygen release capacity of the forest according to the oxygen release amount of the forest.
8. A method for evaluating forest carbon sequestration and oxygen release benefits is characterized by comprising the following steps:
firstly, evaluating the carbon fixing capacity and/or the oxygen releasing capacity of the forest in the block to be tested by using the forest oxygen releasing capacity evaluation method as claimed in claim 7; then determining the carbon storage amount and/or oxygen release amount in a certain period of time;
and finally, determining the carbon fixing and oxygen releasing benefits according to the carbon storage and/or oxygen releasing value in the corresponding time based on the carbon storage and/or oxygen releasing in a certain period of time.
9. A forest carbon sequestration capacity assessment apparatus, characterized in that the apparatus comprises a processor and a memory, the memory having stored therein at least one instruction, the at least one instruction being loaded and executed by the processor to implement a forest carbon sequestration capacity assessment method as claimed in one of claims 1 to 6.
10. A forest oxygen release capacity assessment apparatus, characterized in that the apparatus comprises a processor and a memory, wherein the memory has stored therein at least one instruction, which is loaded and executed by the processor to implement a forest oxygen release capacity assessment method as claimed in claim 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210786681.3A CN115131370B (en) | 2022-07-04 | 2022-07-04 | Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210786681.3A CN115131370B (en) | 2022-07-04 | 2022-07-04 | Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115131370A true CN115131370A (en) | 2022-09-30 |
| CN115131370B CN115131370B (en) | 2023-04-18 |
Family
ID=83381896
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210786681.3A Active CN115131370B (en) | 2022-07-04 | 2022-07-04 | Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115131370B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100040260A1 (en) * | 2007-12-20 | 2010-02-18 | Image Tree Corp. | Remote sensing and probabilistic sampling based method for determining the carbon dioxide volume of a forest |
| WO2012002482A1 (en) * | 2010-06-30 | 2012-01-05 | 株式会社パスコ | Evaluation device and evaluation method for carbon dioxide absorbing effectiveness |
| CN108009384A (en) * | 2017-12-26 | 2018-05-08 | 中国科学院东北地理与农业生态研究所 | A kind of urban forests organic C storage landscape scale deduction method |
| CN108647623A (en) * | 2018-05-04 | 2018-10-12 | 中国科学院遥感与数字地球研究所 | A kind of potential organic C storage remote sensing estimation method of forest based on resource constraint |
| CN111488910A (en) * | 2020-03-12 | 2020-08-04 | 河南城建学院 | Farmland soil organic carbon space-time data acquisition method |
| CN112183154A (en) * | 2019-07-01 | 2021-01-05 | 苏州五蕴明泰科技有限公司 | Method for accounting carbon reserves of biomass carbon pool |
| CN112287287A (en) * | 2020-11-06 | 2021-01-29 | 东北林业大学 | Method, system and device for measuring forest carbon sequestration |
| CN112861732A (en) * | 2021-02-10 | 2021-05-28 | 东北林业大学 | Method, system and device for monitoring land in ecological environment fragile area |
| CN114563402A (en) * | 2022-02-21 | 2022-05-31 | 国家林业和草原局华东调查规划院 | Forestry carbon sink measuring and calculating method |
-
2022
- 2022-07-04 CN CN202210786681.3A patent/CN115131370B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100040260A1 (en) * | 2007-12-20 | 2010-02-18 | Image Tree Corp. | Remote sensing and probabilistic sampling based method for determining the carbon dioxide volume of a forest |
| WO2012002482A1 (en) * | 2010-06-30 | 2012-01-05 | 株式会社パスコ | Evaluation device and evaluation method for carbon dioxide absorbing effectiveness |
| CN108009384A (en) * | 2017-12-26 | 2018-05-08 | 中国科学院东北地理与农业生态研究所 | A kind of urban forests organic C storage landscape scale deduction method |
| CN108647623A (en) * | 2018-05-04 | 2018-10-12 | 中国科学院遥感与数字地球研究所 | A kind of potential organic C storage remote sensing estimation method of forest based on resource constraint |
| CN112183154A (en) * | 2019-07-01 | 2021-01-05 | 苏州五蕴明泰科技有限公司 | Method for accounting carbon reserves of biomass carbon pool |
| CN111488910A (en) * | 2020-03-12 | 2020-08-04 | 河南城建学院 | Farmland soil organic carbon space-time data acquisition method |
| CN112287287A (en) * | 2020-11-06 | 2021-01-29 | 东北林业大学 | Method, system and device for measuring forest carbon sequestration |
| CN112861732A (en) * | 2021-02-10 | 2021-05-28 | 东北林业大学 | Method, system and device for monitoring land in ecological environment fragile area |
| CN114563402A (en) * | 2022-02-21 | 2022-05-31 | 国家林业和草原局华东调查规划院 | Forestry carbon sink measuring and calculating method |
Non-Patent Citations (2)
| Title |
|---|
| 孙钰森: "基于地理加权回归克里格模型的帽儿山地区森林碳储量空间分布" * |
| 韩爱惠: "森林生物量及碳储量遥感监测方法研究" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115131370B (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112287287B (en) | Method, system and device for measuring forest carbon sequestration | |
| Olivas et al. | Comparison of direct and indirect methods for assessing leaf area index across a tropical rain forest landscape | |
| CN105913017A (en) | Corresponding period double high resolution remote sensing image-based forest biomass estimation method | |
| CN116337146B (en) | Ecological quality evaluation and partitioning method and device based on improved remote sensing ecological index | |
| Blanchette et al. | Predicting wood fiber attributes using local-scale metrics from terrestrial LiDAR data: A case study of Newfoundland conifer species | |
| Kuyah et al. | Crown area allometries for estimation of aboveground tree biomass in agricultural landscapes of western Kenya | |
| CN114494882B (en) | Winter wheat remote sensing identification analysis method and system based on random forest | |
| CN114387528A (en) | Pine nematode disease monitoring space-air-ground integrated monitoring method | |
| CN104199027B (en) | Method based on the polarimetric radar large area near real-time Monitoring Rice phenological period of compacting | |
| CN113340286B (en) | Method and equipment for analyzing surveying and mapping information data of land planning surveying and mapping project and computer storage medium | |
| CN106530346A (en) | Cupressaceae plant crown image analysis method | |
| CN103954334A (en) | Fully automatic image pickup type water meter verification system and operating method thereof | |
| CN113821925A (en) | Wetland dynamic boundary determination method based on three elements of aquatic soil | |
| CN115131370B (en) | Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment | |
| Juniansah et al. | Mangrove leaf area index estimation using sentinel 2A imagery in Teluk Ratai, Pesawaran Lampung | |
| Song et al. | NLCS-A novel coordinate system for spatial analysis on hyperspectral leaf images and an improved nitrogen index for soybean plants | |
| CN110470258B (en) | Method and system for determining growth amount of Yunnan pine forest | |
| CN115063610B (en) | Soybean planting area identification method based on Sentinel-1 and 2 images | |
| CN116912672A (en) | Unmanned survey vessel-based biological integrity evaluation method for large benthonic invertebrates | |
| CN115019205A (en) | Rape flowering phase SPAD and LAI estimation method based on multispectral image of unmanned aerial vehicle | |
| CN114998724A (en) | Lake cyanobacterial bloom remote sensing monitoring method capable of resisting cloud layer interference | |
| CN113361952A (en) | Reed growth condition evaluation method | |
| Mõttus et al. | A forestry GIS-based study on evaluating the potential of imaging spectroscopy in mapping forest land fertility | |
| CN102445276A (en) | Preparation method for nitrogen fertilizer management leaf color chart of flue-cured tobacco | |
| Lama et al. | Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images. Water 2021, 13, 1333 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |