CN107655457A - A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image - Google Patents
A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image Download PDFInfo
- Publication number
- CN107655457A CN107655457A CN201611206995.2A CN201611206995A CN107655457A CN 107655457 A CN107655457 A CN 107655457A CN 201611206995 A CN201611206995 A CN 201611206995A CN 107655457 A CN107655457 A CN 107655457A
- Authority
- CN
- China
- Prior art keywords
- data
- mud
- image
- disaster
- region
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Geophysics And Detection Of Objects (AREA)
- Image Processing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image, including：According to historical disaster data and meteorological present situation, Primary Location is carried out to mud-stone flow disaster region；According to the disaster position of Primary Location, the satellite image data of position is obtained；Computer Automatic Recognition is carried out to the mud-rock flow generation area in satellite image, extracts the mud-rock flow region of automatic identification；According to the position in the mud-rock flow region of automatic identification, the dem data of position is obtained；Satellite image, dem data are carried out image co-registration, and carry out image enhaucament, 3D landform image of the generation with elevation information；Based on 3D landform images, artificial correction is carried out to the mud-rock flow region of automatic identification；According to revised mud-rock flow identification region, Geological Hazards of debris area image is drawn, and combines other datas, judges possibility, scale and resolution that disaster further occurs.
Description
This technology is applied to Geological Hazards Monitoring field, specifically a kind of mud-rock flow geology calamity based on remote sensing satellite image
Evil recognition methods.
Background technology
The influence of mud-stone flow disaster expands to the numerous areas such as city, industry, ecological environment by past agricultural.Mudflow
It is a kind of common geological disaster in loess plateau, it breaks out unexpected, breaks with tremendous force and rapid, destructive power is strong, to Loess Plateau Region
Industrial and agricultural production and people's lives cause serious harm.The mudflow of burst often rushes in village and cities and towns, damage house,
Factory, enterprises and institutions and various equipment and facility, people and animals and farmland are also buried, notably causes village to ruin people and dies.Face monitoring police
Forecast of the report i.e. in zero hour to a few hours, is according to Pluviogram hourly, force of rain information, dangerous omen, monitoring instrument system
Determine foundation, the mud-rock flow of cities and towns, industrial and mineral and traffic and transportation sector is faced calamity take refuge with succour it is significant.
Traditional mud-stone flow disaster investigation relies primarily on field reconnaissance completion.Because mud-rock flow takes place mostly in mountain area, ground
Shape landforms are sufficiently complex, the equal extreme of weather conditions, transportation condition, condition of work, life condition, and field investigation works not only
Be difficult to carry out, and to emit great life danger, cause mud-stone flow disaster situation at all can not comprehensively, fast investigation it is clear
Chu.
3S technologies are GPS geo-location system, RS remote sensing technologies, GIS GIS-Geographic Information System, be it is a kind of it is convenient, fast, into
This relatively low and effective identification, the mode judged.Vanguard satellite can provide global history remotely-sensed data in the world at present.Pass through
Comparison and analysis to Geological Hazards of debris area historical data, can be selection and the calamity of Geological Hazards of debris area administration way
Harmful development trend provides a large amount of true and reliable data.GIS technology has the interpretation analysis function powerful to remotely-sensed data, is
Geological Hazards of debris administers the effective platform provided with monitoring.
The content of the invention
It is an object of the invention to for above-mentioned the problems of the prior art, there is provided a kind of mud based on remote sensing satellite image
Rock glacier geological disaster recognition methods, it is characterised in that comprise the following steps：
Step 1, according to historical disaster data and meteorological present situation, Primary Location is carried out to mud-stone flow disaster region；
Step 2, according to the disaster position of Primary Location, the satellite image data of position is obtained；
Step 3, Computer Automatic Recognition is carried out to the mud-rock flow generation area in satellite image, extracts the mud of automatic identification
Rock glacier region；
Step 4, according to the position in the mud-rock flow region of automatic identification, the dem data of position is obtained；
Step 5, satellite image, dem data are carried out image co-registration, and carries out image enhaucament, generation has elevation information
3D landform images；
Step 6, based on 3D landform images, artificial correction is carried out to the mud-rock flow region of automatic identification；
Step 7, according to revised mud-rock flow identification region, Geological Hazards of debris area image is drawn, and combine it
His data, judges possibility, scale and resolution that disaster further occurs.
Preferably, wherein, the step 1, according to historical disaster data and meteorological present situation, mud-stone flow disaster region is carried out
Primary Location, specifically include：According to data information, expert carries out preliminary prediction by experience hair region easy to mud-stone flow disaster
Positioning, or, technical staff carries GPS device and goes to Geological Hazards of debris Yi Fa areas or mud-rock flow disaster area, to disaster region
Carry out Primary Location.
Preferably, wherein, the step 2, the satellite image data of acquisition is the T M images for including 7 wave bands.
Preferably, wherein, the step 3, Computer Automatic Recognition is carried out to the mud-rock flow generation area in satellite image
Before, in addition to satellite image image preprocessing step is carried out, described image pretreatment includes：Geometric correction, multiband number
Word synthesis, image mosaic.
Preferably, wherein, the step 4, the dem data is earth surface model, have recorded region ground
Height value and topographic(al) feature.
Preferably, wherein, the step 7, other datas include：Meteorological data, hydrographic data, vegetation data,
Shape relief data, geologic data, mankind's activity data；Wherein, the meteorological data includes precipitation situation, and the hydrology factor includes ground
Table water and underground aqueous condition, vegetation data include vegetation bed course situation, and topography and geomorphology data include the gradient, absolute elevation, relative
The discrepancy in elevation, width, length of grade, slope aspect, geologic data include rift structure, formation lithology, and mankind's activity data include soil exploitation shape
Condition.
A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image provided by the invention, it is by 3S technologies
GPS (global positioning system), RS (remote sensing), GIS (GIS-Geographic Information System) three combination, determine mud-rock flow generation area
Position, analysis, final scale, the extent of injury for realizing identification mud-rock flow, it is that a kind of convenient, fast, cost is relatively low and effective calamity
Evil identification and the mode judged.
Brief description of the drawings
Method flow diagram proposed by the invention Fig. 1.
Embodiment
For a better understanding of the present invention, the description of reference implementation example below in conjunction with the accompanying drawings, the method for the present invention is carried out
Further instruction.
For the comprehensive understanding present invention, numerous details are refer in the following detailed description.But art technology
Personnel are it should be understood that the present invention can realize without these details.In embodiment, known side is not described in detail
Method, process, component, in order to avoid unnecessarily make embodiment cumbersome.
Shown in Figure 1, the present invention of the invention provides a kind of Geological Hazards of debris based on remote sensing satellite image and known
Other method, it is characterised in that comprise the following steps：
Step 1, according to historical disaster data and meteorological present situation, Primary Location is carried out to mud-stone flow disaster region；
Step 2, according to the disaster position of Primary Location, the satellite image data of position is obtained；
Step 3, Computer Automatic Recognition is carried out to the mud-rock flow generation area in satellite image, extracts the mud of automatic identification
Rock glacier region；
Step 4, according to the position in the mud-rock flow region of automatic identification, the dem data of position is obtained；
Step 5, satellite image, dem data are carried out image co-registration, and carries out image enhaucament, generation has elevation information
3D landform images；
Step 6, based on 3D landform images, artificial correction is carried out to the mud-rock flow region of automatic identification；
Step 7, according to revised mud-rock flow identification region, Geological Hazards of debris area image is drawn, and combine it
His data, judges possibility, scale and resolution that disaster further occurs.
Preferably, wherein, the step 1, according to historical disaster data and meteorological present situation, mud-stone flow disaster region is carried out
Primary Location, specifically include：According to data information, expert carries out preliminary prediction by experience hair region easy to mud-stone flow disaster
Positioning, or, technical staff carries GPS device and goes to Geological Hazards of debris Yi Fa areas or mud-rock flow disaster area, to disaster region
Carry out Primary Location.
Preferably, wherein, the step 2, the satellite image data of acquisition is the T M images for including 7 wave bands.
Preferably, wherein, the step 3, Computer Automatic Recognition is carried out to the mud-rock flow generation area in satellite image
Before, in addition to satellite image image preprocessing step is carried out, described image pretreatment includes：Geometric correction, multiband number
Word synthesis, image mosaic.
Preferably, wherein, the step 4, the dem data is earth surface model, have recorded region ground
Height value and topographic(al) feature.
Preferably, wherein, the step 7, other datas include：Meteorological data, hydrographic data, vegetation data,
Shape relief data, geologic data, mankind's activity data；Wherein, the meteorological data includes precipitation situation, and the hydrology factor includes ground
Table water and underground aqueous condition, vegetation data include vegetation bed course situation, and topography and geomorphology data include the gradient, absolute elevation, relative
The discrepancy in elevation, width, length of grade, slope aspect, geologic data include rift structure, formation lithology, and mankind's activity data include soil exploitation shape
Condition.
Preferably, wherein, the step 3, Computer Automatic Recognition is carried out to the mud-rock flow generation area in satellite image,
The mud-rock flow region of automatic identification is extracted, is specifically included：
Step 3-1, atural object segmentation, the region being partitioned into where different atural objects are carried out in satellite image；
Step 3-2, the spectral signature of different zones after extraction atural object segmentation；
Step 3-3, establish mud-rock flow discrimination index；
Step 3-4, discrimination index is calculated according to the spectral signature of different zones, extraction is wherein more than the discrimination index of threshold value
The region at place；
Step 3-5, the result using the region extracted as debris flow region Computer Automatic Recognition.
Preferably, wherein, the step 6, based on 3D landform images, the mud-rock flow region of automatic identification is manually repaiied
Just, specifically include：
Step 6-1, according to geometric shape of the debris flow region on image, establish mud-rock flow manual identified geometric landmarks；
Geometric shape of the debris flow region on image is shown as：Include rear wall, gliding mass, leading edge, source area, Circulation Area, accumulation
Area；
Step 6-2, by distinguishing the color, tone, texture or the shade that have differences with periphery atural object the geometry that is formed
Morphological landmarks, the Computer Automatic Recognition result of debris flow region is modified.
Preferably, wherein, the step 3-3, mud-rock flow discrimination index is：
Wherein, LTMiThe brightness value of TM the i-th wave bands of image is represented, a represents correction value.
It can be seen that 3S technologies are GPS (global positioning system), RS (remote sensing), GIS (GIS-Geographic Information System) three by the present invention
Combination, mud-rock flow generation area is positioned, analyzed, final scale, the extent of injury for realizing identification mud-rock flow, is a kind of
Convenient, fast, cost is relatively low and effective disaster identification and the mode judged.
Here the preferred embodiments of the present invention are only illustrated, but its meaning is not intended to limit the scope of the invention, applicability and is matched somebody with somebody
Put.On the contrary, the detailed description to embodiment can be carried out those skilled in the art.It will be understood that without departing from appended power
In the case of the spirit and scope of the invention that sharp claim determines, some details can be made the appropriate changes and modifications.
Claims (6)
1. a kind of Geological Hazards of debris recognition methods based on remote sensing satellite image, it is characterised in that comprise the following steps：
Step 1, according to historical disaster data and meteorological present situation, Primary Location is carried out to mud-stone flow disaster region；
Step 2, according to the disaster position of Primary Location, the satellite image data of position is obtained；
Step 3, Computer Automatic Recognition is carried out to the mud-rock flow generation area in satellite image, extracts the mud-rock flow of automatic identification
Region；
Step 4, according to the position in the mud-rock flow region of automatic identification, the dem data of position is obtained；
Step 5, satellite image, dem data are carried out image co-registration, and carries out image enhaucament, 3D of the generation with elevation information
Landform image；
Step 6, based on 3D landform images, artificial correction is carried out to the mud-rock flow region of automatic identification；
Step 7, according to revised mud-rock flow identification region, Geological Hazards of debris area image is drawn, and combines other moneys
Expect data, judge possibility, scale and resolution that disaster further occurs.
2. the method according to claim 11, wherein, the step 1, according to historical disaster data and meteorological present situation, to mud
Rock glacier disaster region carries out Primary Location, specifically includes：According to data information, expert is by experience to mud-stone flow disaster Yi Fa areas
Domain carries out preliminary prediction positioning, or, technical staff carries GPS device and goes to Geological Hazards of debris Yi Fa areas or mud-rock flow
Disaster area, Primary Location is carried out to disaster region.
3. according to the method for claim 1, wherein, the step 2, the satellite image data of acquisition is to include 7 wave bands
TM images.
4. according to the method for claim 1, wherein, the step 3, the mud-rock flow generation area in satellite image is carried out
Before Computer Automatic Recognition, in addition to image preprocessing step is carried out to satellite image, described image pretreatment includes：Geometry
Correction, multiband digit synthesis, image mosaic.
5. according to the method for claim 1, wherein, the step 4, the dem data is earth surface model, be have recorded
The height value and topographic(al) feature on region ground.
6. according to the method for claim 1, wherein, the step 7, other datas include：Meteorological data, hydrology number
According to, vegetation data, topography and geomorphology data, geologic data, mankind's activity data；Wherein, the meteorological data includes precipitation situation,
The hydrology factor includes surface water and groundwater situation, and vegetation data include vegetation bed course situation, topography and geomorphology data include the gradient,
Absolute elevation, relative relief, width, length of grade, slope aspect, geologic data include rift structure, formation lithology, mankind's activity packet
Include soil Exploitation Status.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611206995.2A CN107655457B (en) | 2016-12-23 | 2016-12-23 | A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611206995.2A CN107655457B (en) | 2016-12-23 | 2016-12-23 | A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107655457A true CN107655457A (en) | 2018-02-02 |
| CN107655457B CN107655457B (en) | 2018-09-25 |
Family
ID=61127382
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611206995.2A Active CN107655457B (en) | 2016-12-23 | 2016-12-23 | A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107655457B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108509882A (en) * | 2018-03-22 | 2018-09-07 | 北京航空航天大学 | Track mud-rock flow detection method and device |
| CN109059871A (en) * | 2018-09-03 | 2018-12-21 | 湖南城市学院 | A kind of accidental pollution event emergency remote control systems and method |
| CN109406755A (en) * | 2018-10-30 | 2019-03-01 | 四川安信科创科技有限公司 | A kind of mountain area falling and landslide hazards high position property discrimination method based on geomorphic unit |
| CN110954065A (en) * | 2019-12-03 | 2020-04-03 | 西南交通大学 | Railway maintenance system based on image analysis and use method thereof |
| CN111142119A (en) * | 2020-01-10 | 2020-05-12 | 中国地质大学（北京） | Mine geological disaster dynamic identification and monitoring method based on multi-source remote sensing data |
| CN112330150A (en) * | 2020-11-05 | 2021-02-05 | 中国科学院、水利部成都山地灾害与环境研究所 | Early-stage judging and identifying method for expandability of large-area debris flow disasters |
| CN112669572A (en) * | 2020-12-17 | 2021-04-16 | 四方智能(武汉)控制技术有限公司 | Unmanned ship system for intelligent inspection of river basin reservoir bank |
| WO2021128696A1 (en) * | 2019-12-24 | 2021-07-01 | 中国矿业大学 | Method for spaceborne/airborne image data fusion to identify site features of coal mining area |
| CN113129258A (en) * | 2021-03-02 | 2021-07-16 | 成都正和德能风险管理咨询有限公司 | Historical image tracing method for insurance target |
| CN113705429A (en) * | 2021-08-26 | 2021-11-26 | 广东电网有限责任公司广州供电局 | Landslide geological disaster remote sensing interpretation method, device, equipment and storage medium |
| CN114049565A (en) * | 2021-11-08 | 2022-02-15 | 中国公路工程咨询集团有限公司 | Geological disaster identification method and device based on remote sensing image and DEM data |
| CN114636417A (en) * | 2022-05-23 | 2022-06-17 | 珠海翔翼航空技术有限公司 | Aircraft forced landing path planning method, system and equipment based on image recognition |
| CN114882366A (en) * | 2022-05-26 | 2022-08-09 | 广州市城市规划勘测设计研究院 | Three-dimensional scene catastrophe monitoring and early warning method |
| CN115422766A (en) * | 2022-09-26 | 2022-12-02 | 北京云庐科技有限公司 | Debris flow monitoring method and system based on digital twinning technology |
| CN115731361A (en) * | 2022-11-22 | 2023-03-03 | 广东佛山地质工程勘察院 | Geological disaster enhanced display method based on laser LiDAR data |
| CN116030354A (en) * | 2023-03-29 | 2023-04-28 | 东华理工大学南昌校区 | Geological disaster analysis method and system based on remote sensing data fusion |
| CN117115644A (en) * | 2023-08-08 | 2023-11-24 | 江苏省地质调查研究院 | Disaster analysis method and device based on image data |
| CN117649608A (en) * | 2024-01-29 | 2024-03-05 | 阿坝州林业和草原科学技术研究所 | Pine wood nematode disease identification system and method based on remote sensing monitoring |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102967850A (en) * | 2011-09-02 | 2013-03-13 | 吴立新 | Disaster remote sensing automatic identification method based on multi-scale grid and fractal dimensional changes |
| CN104881834A (en) * | 2015-06-25 | 2015-09-02 | 辽宁有色勘察研究院 | Mud-rock flow geological disaster identification and determination method based on 3S technology |
-
2016
- 2016-12-23 CN CN201611206995.2A patent/CN107655457B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102967850A (en) * | 2011-09-02 | 2013-03-13 | 吴立新 | Disaster remote sensing automatic identification method based on multi-scale grid and fractal dimensional changes |
| CN104881834A (en) * | 2015-06-25 | 2015-09-02 | 辽宁有色勘察研究院 | Mud-rock flow geological disaster identification and determination method based on 3S technology |
Non-Patent Citations (1)
| Title |
|---|
| 张明华: ""公路工程地质灾害遥感图像识别及解译方法"", 《遥感技术与应用》 * |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108509882A (en) * | 2018-03-22 | 2018-09-07 | 北京航空航天大学 | Track mud-rock flow detection method and device |
| CN109059871A (en) * | 2018-09-03 | 2018-12-21 | 湖南城市学院 | A kind of accidental pollution event emergency remote control systems and method |
| CN109406755A (en) * | 2018-10-30 | 2019-03-01 | 四川安信科创科技有限公司 | A kind of mountain area falling and landslide hazards high position property discrimination method based on geomorphic unit |
| CN109406755B (en) * | 2018-10-30 | 2020-08-18 | 四川安信科创科技有限公司 | Mountain landslide geological disaster high-order property identification method based on landform unit |
| CN110954065A (en) * | 2019-12-03 | 2020-04-03 | 西南交通大学 | Railway maintenance system based on image analysis and use method thereof |
| WO2021128696A1 (en) * | 2019-12-24 | 2021-07-01 | 中国矿业大学 | Method for spaceborne/airborne image data fusion to identify site features of coal mining area |
| CN111142119A (en) * | 2020-01-10 | 2020-05-12 | 中国地质大学（北京） | Mine geological disaster dynamic identification and monitoring method based on multi-source remote sensing data |
| CN111142119B (en) * | 2020-01-10 | 2021-08-17 | 中国地质大学（北京） | Mine geological disaster dynamic identification and monitoring method based on multi-source remote sensing data |
| CN112330150A (en) * | 2020-11-05 | 2021-02-05 | 中国科学院、水利部成都山地灾害与环境研究所 | Early-stage judging and identifying method for expandability of large-area debris flow disasters |
| CN112330150B (en) * | 2020-11-05 | 2023-08-11 | 中国科学院、水利部成都山地灾害与环境研究所 | Early judging and identifying method for disaster initiation of large-area debris flow |
| CN112669572A (en) * | 2020-12-17 | 2021-04-16 | 四方智能(武汉)控制技术有限公司 | Unmanned ship system for intelligent inspection of river basin reservoir bank |
| CN112669572B (en) * | 2020-12-17 | 2023-07-04 | 四方智能(武汉)控制技术有限公司 | Unmanned ship system for intelligent inspection of river basin bank |
| CN113129258A (en) * | 2021-03-02 | 2021-07-16 | 成都正和德能风险管理咨询有限公司 | Historical image tracing method for insurance target |
| CN113705429A (en) * | 2021-08-26 | 2021-11-26 | 广东电网有限责任公司广州供电局 | Landslide geological disaster remote sensing interpretation method, device, equipment and storage medium |
| CN114049565A (en) * | 2021-11-08 | 2022-02-15 | 中国公路工程咨询集团有限公司 | Geological disaster identification method and device based on remote sensing image and DEM data |
| CN114636417A (en) * | 2022-05-23 | 2022-06-17 | 珠海翔翼航空技术有限公司 | Aircraft forced landing path planning method, system and equipment based on image recognition |
| CN114882366A (en) * | 2022-05-26 | 2022-08-09 | 广州市城市规划勘测设计研究院 | Three-dimensional scene catastrophe monitoring and early warning method |
| CN115422766A (en) * | 2022-09-26 | 2022-12-02 | 北京云庐科技有限公司 | Debris flow monitoring method and system based on digital twinning technology |
| CN115731361A (en) * | 2022-11-22 | 2023-03-03 | 广东佛山地质工程勘察院 | Geological disaster enhanced display method based on laser LiDAR data |
| CN115731361B (en) * | 2022-11-22 | 2024-05-03 | 广东佛山地质工程勘察院 | Geological disaster enhanced display method based on laser LiDAR data |
| CN116030354A (en) * | 2023-03-29 | 2023-04-28 | 东华理工大学南昌校区 | Geological disaster analysis method and system based on remote sensing data fusion |
| CN116030354B (en) * | 2023-03-29 | 2023-06-16 | 东华理工大学南昌校区 | Geological disaster analysis method and system based on remote sensing data fusion |
| CN117115644A (en) * | 2023-08-08 | 2023-11-24 | 江苏省地质调查研究院 | Disaster analysis method and device based on image data |
| CN117649608A (en) * | 2024-01-29 | 2024-03-05 | 阿坝州林业和草原科学技术研究所 | Pine wood nematode disease identification system and method based on remote sensing monitoring |
| CN117649608B (en) * | 2024-01-29 | 2024-03-29 | 阿坝州林业和草原科学技术研究所 | Pine wood nematode disease identification system and method based on remote sensing monitoring |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107655457B (en) | 2018-09-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107655457B (en) | A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image | |
| Zhu et al. | IMPROVED KARST SINKHOLE MAPPING IN KENTUCKY USING LIDAR TECHNIQUES: A PILOT STUDY IN FLOYDS FORK WATERSHED. | |
| Lin et al. | Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography | |
| Ellis et al. | Mapping selective logging impacts in Borneo with GPS and airborne lidar | |
| CN107358327A (en) | Landslide liability assessment method based on unmanned aerial vehicle remote sensing images | |
| Williams et al. | Surface rupture of the Hundalee fault during the 2016 Mw 7.8 Kaikōura earthquake | |
| Thompson Jobe et al. | Evidence of previous faulting along the 2019 Ridgecrest, California, earthquake ruptures | |
| Chang et al. | Geomorphological evolution of landslides near an active normal fault in northern Taiwan, as revealed by lidar and unmanned aircraft system data | |
| CN106874832B (en) | The recognition methods in mud-rock flow burst area in a kind of remote sensing image | |
| CN108241182B (en) | A kind of debris flow monitoring system | |
| CN110111376B (en) | Coal mining subsidence water area calculation method | |
| Hammack et al. | Using drone-mounted geophysical sensors to map legacy oil and gas infrastructure | |
| Alrehaili et al. | Use of remote sensing, GIS and groundwater monitoring to estimate artificial groundwater recharge in Riyadh, Saudi Arabia | |
| CN108241825A (en) | A kind of method that remote sensing images are carried out with mud-stone flow disaster region drawing | |
| Yusoff et al. | Mapping of post-event earthquake induced landslides in Sg. Mesilou using LiDAR | |
| Fu et al. | Estimation of landslides and road capacity after august 8, 2017, MS7. 0 jiuzhaigou earthquake using high-resolution remote sensing images | |
| Westin | Downie slide: an integrated remote sensing approach to characterization of a very slow moving landslide | |
| Jayawardena | Characteristics of neotectonic faulting in the mount lofty and flinders ranges, South Australia | |
| Kageyama et al. | Analysis of the segments extracted by automated lineament detection | |
| Litynski et al. | US Department of Energy efforts to advance remote sensing technologies for monitoring geologic storage operations | |
| Hesse | Extraction of archaeological features from high-resolution LIDAR data | |
| Bruning | Digital processing and data compilation approach for using remotely sensed imagery to identify geological lineaments in hard-rock terrains: an application for groundwater explorations in Nicaragua | |
| Fu et al. | Mapping mining-induced ground fissures and their evolution using UAV photogrammetry | |
| CN117784075B (en) | Karst development intensity grading method integrating radar remote sensing and space analysis | |
| Oyinkuro et al. | Change detection analysis in Yenagoa due to urbanization using remote sensing and geographic information systems |
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 | ||
| CB03 | Change of inventor or designer information |
Inventor after: Tian Yuan Inventor after: Du Jing Inventor after: Xie Guojun Inventor after: Wang Meng Inventor after: Li Yuguang Inventor before: Xie Guojun Inventor before: Wang Meng Inventor before: Li Yuguang |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 101399 No. 2 East Airport Road, Shunyi Airport Economic Core Area, Beijing (1st, 5th and 7th floors of Industrial Park 1A-4) Patentee after: Zhongke Star Map Co., Ltd. Address before: 101399 Building 1A-4, National Geographic Information Technology Industrial Park, Guomen Business District, Shunyi District, Beijing Patentee before: Space Star Technology (Beijing) Co., Ltd. |
|
| CP03 | Change of name, title or address |