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遥感技术与应用  2021, Vol. 36 Issue (1): 25-32    DOI: 10.11873/j.issn.1004-0323.2021.1.0025
综述     
斑块状植被遥感检测研究进展
刘庆生1,2()
1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
2.江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023
Review of Patch Vegetation Detection from Remotely Sensed Data
Qingsheng Liu1,2()
1.State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China
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摘要:

斑块状植被是世界上干旱—半干旱区常见的景观类型,对于它们的形成、结构和演替研究能够提高人们对干旱—半干旱地区生态系统动态及其重要的生态水文过程的理解,具有重要的理论研究意义和应用价值。传统的基于地面调查和长期定位观测的方法观测范围有限,已无法满足目前区域斑块状植被分布及其空间格局特征研究的需要。利用遥感技术快速重复获取大面积对地观测数据,已成为斑块状植被检测的主要发展方向。通过对近20 a斑块状植被遥感检测相关文献的综述,阐述了现有研究中使用的航空和高分辨率卫星遥感数据、基于像元的检测方法、基于对象的检测方法和基于形态学的检测方法,以及各自的局限性和优势。在此基础上,对今后斑块状植被遥感检测的研究方向进行了展望,应加强高空间高光谱分辨率卫星遥感数据和低空无人机高光谱和激光雷达图像的应用,重视面向粘连斑块的新型图像分割算法研发,以期进一步提高斑块状植被检测的精度。

关键词: 斑块状植被航拍图像卫星影像检测方法    
Abstract:

Patch vegetation is a common landscape type in arid and semi-arid areas in the world. To detect patch vegetation using remotely sensed images is important for studying its pattern formation, function, and succession mechanisms, and understanding its impact on the ecohydrological processes in arid and semi-arid areas. This article reviews the current status of patch vegetation detection based on remote sensing technology, including remotely sensed data source such as aerial photographs and high-resolution satellite images, and application of detection approaches from pixel-based, object-based, and morphology-based methods, respectively. It is pointed out that the image quality, acquisition date of imagery, and the composition and structure of vegetation patch have an important influence on the classification of vegetation patch. For the overlapping patches, a better image segmentation algorithm is needed to be applied for improving detection accuracy. Finally, the research directions of remote sensing detection of vegetation patch are suggested in order to provide reference for monitoring patch vegetation patterns and dynamics in the future, including an application of high-spatial and spectral satellite remotely sensed imagery and unmanned aerial vehicle, and the development of more advanced image segmentation algorithms.

Key words: Patch vegetation    Aerial photograph    Satellite imagery    Detection methods
收稿日期: 2020-01-20 出版日期: 2021-04-13
ZTFLH:  X171.4  
基金资助: 国家自然科学基金项目(41671422);国家重点研发计划项目(2016YFC1402701);中国科学院战略性先导专项(XDA20030302);国家山洪灾害调查项目(SHZH?IWHR?57)
作者简介: 刘庆生(1971-),男,山西忻州人,博士,研究员,主要从事遥感信息提取与分析研究。E?mail: liuqs@lreis.ac.cn
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引用本文:

刘庆生. 斑块状植被遥感检测研究进展[J]. 遥感技术与应用, 2021, 36(1): 25-32.

Qingsheng Liu. Review of Patch Vegetation Detection from Remotely Sensed Data. Remote Sensing Technology and Application, 2021, 36(1): 25-32.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.1.0025        http://www.rsta.ac.cn/CN/Y2021/V36/I1/25

1 Tschinkel W R. The Life Cycle and Life Span of Namibian Fairy Circles[J]. PLoS One, 2012,7(6): e38056. doi: 10.1371/journal.pone.0038056.
doi: 10.1371/journal.pone.0038056
2 Bordeu I, Clerc M, Couteron P, et al. Self-replication of Localized Vegetation Patches in Scarce Environments[J]. Scientific Reports, 2016, 6: 33703. doi: 10.1038/srep33703.
doi: 10.1038/srep33703
3 Liu Q, Liu G, Huang C, et al. Soil Physicochemical Properties Associated with Quasi-circular Vegetation Patches in the Yellow River Delta, China[J]. Geoderma, 2019, 337: 202-214.
4 Rietkerk M, Dekker S C, De Ruiter P C, et al. Self-organized Patchiness and Catastrophic Shifts in Ecosystems[J]. Science, 2004, 305: 1926-1929.
5 Ravi S, D’Odorico P, Wang L, et al. Form and Function of Grass Ring Patterns in Arid Grasslands: The Role of Abiotic Controls[J]. Oecologia, 2008, 158: 545-555.
6 Du Jianhui, Yan Ping, Dong Yuxiang. Water Driving Mechanism of Patched Vegetation Formation in Arid Areas: A Review[J]. Chinese Journal of Ecology, 2012, 31(8): 2137-2144.
6 杜建会, 严平, 董玉祥. 干旱地区斑块状植被格局形成的水分驱动机制及其研究进展[J]. 生态学杂志, 2012, 31(8): 2137-2144.
7 Martinez-Garcia R, Calabrese J M, Hernandez-Garcia E, et al. Vegetation Pattern Formation in Semiarid Systems Without Facilitative Mechanisms[J]. Geophysical Research Letters, 2013, 40: 6143-6147.
8 Bonanomi G, Incerti G, Stinca A, et al. Ring Formation in Clonal Plants[J]. Community Ecology, 2014, 15(1): 77-86.
9 Getzin S, Wiegand K, Wiegand T, et al. Clarifying Misunderstandings Regarding Vegetation Self-organisation and Spatial Patterns of Fairy Circles in Namibia: A Response to Recent Termite Hypotheses[J]. Ecological Entomology, 2015, 40: 669-675.
10 Liu Qingsheng, Liu Gaohuan, Huang Chong, et al. Remote Sensing Analysis on the Spatial-temporal Dynamics of Quasi-circular Vegetation Patches in the Modern Yellow River Delta, China[J]. Remote Sensing Technology and Application, 2016, 31(2): 349-358.
10 刘庆生, 刘高焕, 黄翀, 等. 现代黄河三角洲类圆形植被斑块时空动态遥感分析[J]. 遥感技术与应用, 2016, 31(2): 349-358.
11 Trodd N M, Dougill A J. Monitoring Vegetation Dynamics in Semi-arid African Rangelands: Use and Limiations of Earth Observation Data to Characterize Vegetation Structure[J]. Applied Geography, 1998, 18(4): 315-330.
12 Underwood E C, Ustin S L, Ramirez C M. A Comparison of Spatial and Spectral Image Resolution for Mapping Invasive Plants in Coastal California[J]. Environmental Management, 2007, 39(1): 63-83.
13 Liu Q, Liu, G, Huang C, et al. Using SPOT 5 Fusion-ready Imagery to Detect Chinese Tamarisk (Saltcedar) with Mathematical Morphological Method[J]. International Journal of Digital Earth, 2014, 7(3): 217-228.
14 Von Hardenberg J, Kletter A Y, Yizhag H, et al. Periodic Versus Scale-free Patterns in Dryland Vegetation[J]. Proceedings of The Royal Society B, 2010, 277(1688): 1771-1776.
15 Kadmon R, Harari-Kremer R. Studying Long-term Vegetation Dynamics Using Digital Processing of Historical Aerial Photographs[J]. Remote Sensing of Environment, 1999, 68: 164-176.
16 Couteron P, Lejeune O. Periodic Spotted Patterns in Semi-arid Vegetation Explained by a Propagation-inhibition Model[J]. Journal of Ecology, 2001, 89: 616-628.
17 Frenkel R E, Boss T R. Introduction, Establishment and Spread of Spartina Patens on Cox Island, Siuslaw Estuary, Oregon[J]. Wetlands, 1988, 8: 33-49.
18 Becker T, Getzin S. The Fairy Circles of Kaokoland (North-West Namibia) Origin, Distribution, and Characteristics[J]. Basic and Applied Ecology, 2000, 1(2): 149-159.
19 Barbier N, Couteron P, Lejoly J, et al. Self-organized Vegetation Patterning as a Fingerprint of Climate and Human Impact on Semi-arid Ecosystems[J]. Journal of Ecology, 2006, 94: 537-547.
20 Strand E K, Smith A M S, Bunting S C, et al. Wavelet Estimation of Plant Spatial Patterns in Multitemporal Aerial Photography[J]. International Journal of Remote Sensing, 2006, 27(10): 2049-2054.
21 Kakembo V. Vegetation Patchiness and Implications for Landscape Function: The Case of Pteronia Incana Invader Species in Ngqushwa Rural Municipality, Eastern Cape, South Africa[J]. Catena, 2009, 77: 180-186.
22 Odindi J O, Kakembo V. Comparison of Pixel and Sub-pixel based Techniques to Separate Pteronia Incana Invaded Areas Using Multi-temporal High-resolution Imagery[J]. Journal of Applied Remote Sensing, 2009, 3: 033545. doi: 10.1117/1.3229983.
doi: 10.1117/1.3229983
23 Bryson M, Reid A, Ramos F, et al. Airborne Vision-based Mapping and Classification of Large Farmland Environments[J]. Journal of Field Robotics, 2010, 27(5): 639-655.
24 Koc-san D, Selim S. Aslan N,et al. Automatic Citrus Tree Extraction from UAV Images and Digital Surface Models Using Circular Hough Transform[J]. Computers and Electronics in Agriculture, 2018, 150: 289-301.
25 Qiu Yannin, Ren Shiyu, Wang Xin. et al. The Spatial Dynamics of Vegetation Revealed by Unmanned Aerial Vehicles Images in a Straw-checkerboards-based Ecological Restoration Area[J]. Acta Ecologica Sinica, 2019, 39(24): 1-10.
25 邱燕宁, 任世钰, 王鑫,等. 基于无人机影像的草方格生态恢复区植被空间格局演化研究[J]. 生态学报, 2019, 39(24): 1-10.
26 Zhang X, Zhang F, Qi Y, et al. New Research Methods for Vegetation Information Extraction based on Visible Light Remote Sensing Images from an Unmanned Aerial Vehicle (UAV)[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 78: 215-226.
27 Zhang Heyu, Guan Wenke, Li Zhipeng, et al. Research on Vegetation Coverage and Spatial Distribution Characteristics in Gobi Region based on UAV Image[J]. Journal of Arid Land Resources and Environment, 2020, 34(2): 161-167.
27 张和钰, 管文轲, 李志鹏,等. 基于无人机影像的戈壁区植被空间分布特征及其主要影响因素研究[J]. 干旱区资源与环境, 2020, 34(2): 161-167.
28 Liu Q, Liu G, Huang C, et al. Monitoring Vegetation Recovery at Abandoned Land[C]//The 8th International Congress on Image and Signal Processing (CISP 2015), Shenyang, 2015: 88-92.
29 Taylor S, Kumar L, Reid N. Accuracy Comparison of Quickbird, Landsat TM and SPOT 5 Imagery for Lantana Camara Mapping[J]. 2011, Journal of Spatial Science, 2011, 56(2): 241-252.
30 Zhang Y, Liu Q, Liu G, et al. Mapping of Circular or Elliptical Vegetation Community Patches: A Comparative Use of SPOT-5, ALOS and ZY-3 Imagery[C]// The 8th International Congress on Image and Signal Processing (CISP 2015), Shenyang, 2015: 666-671.
31 Boggs G S. Assessment of SPOT 5 and QuickBird Remotely Sensed Imagery for Mapping Tree Cover in Savannas[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12: 217-224.
32 Liu Q, Huang C, Liu G, et al. Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China[J]. Sensors, 2018, 18: 2733. doi: 10.3390/s18082733.
doi: 10.3390/s18082733
33 Wang J, Xiao X, Qin Y, et al. Characterizing the Encroachment of Juniper Forests into Sub-humid and Semi-arid Prairies from 1984 to 2010 Using PALSAR and Landsat Data[J]. Remote Sensing of Environment, 2018, 205: 166-179.
34 Fernandes M R, Aguiar F C, Silva J M N, et al. Optimal Attributes for the Object based Detection of Giant Reed in Riparian Habitats: A Comparative Study between Airborne High Spatial Resolution and WorldView-2 Imagery[J]. Journal of Applied Earth Observation and Geoinformation, 2014, 32: 79-91.
35 Laliberte A S, Rango A, Havstad K M, et al. Object-oriented Image Analysis for Mapping Shrub Encroachment from 1937 to 2003 in Southern New Mexico[J]. Remote Sensing of Environment, 2004, 93: 198-210.
36 Shekede M D, Murwira A, Masocha M. Wavelet-based Detection of Bush Encroachment in a Savanna Using Multi-Temporal Aerial Photographs and Satellite Imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 35: 209-216.
37 Li D, Ke Y H, Gong H, et al. Object-based Urban Tree Species Classification Using Bi-temporal WorldView-2 and WorldView-3 Images[J]. Remote Sensing, 2015, 7: 16917-16937. doi: 10.3390/rs71215861.
doi: 10.3390/rs71215861
38 Karlson M, Ostwald M, Reese H, et al. Assessing the Potential of Multi-seasonal WorldView-2 Imagery for Mapping West African Agroforstry Tree Species[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 50: 80-88.
39 Clark M L, Buck-Diaz J, Evens J. Mapping of Forest Alliance with Simulated Multi-seasonal Hyperspectral Satellite Imagery[J]. Remote Sensing of Environment, 2018, 210: 490-507.
40 Hutt C, Koppe W, Miao Y X, et al. Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-polarization SAR Satellite Images[J]. Remote Sensing, 2016, 8: 684. doi: 10.3390/rs8080684.
doi: 10.3390/rs8080684
41 Zhao Y Y, Feng D L, Yu L, et al. Detailed Dynamic Land Cover Mapping of Chile: Accuracy Improvement by Integrating Multi-temporal Data[J]. Remote Sensing of Environment, 2016, 183: 170-185.
42 Islam K, Jashimuddin M, Nath B, et al. Land Use Classification and Change Detection by Using Multi-temporal Remotely Sensed Imagery: The Case of Chunati Wildlife Sancturay, Bangladesh[J]. The Egyptian Journal of Remote Sensing and Space Sciences, 2018, 21: 37-47.
43 Liu Q, Song H, Liu G, et al. Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest[J]. Remote Sensing, 2019, 11: 1216. doi: 10.3390/rs11101216.
doi: 10.3390/rs11101216
44 Albrecht C F, Joubert J J, De Rycke P H. Origin of the Enigmatic, Circular, Barren Patches (‘Fairy Rings’) of the Pro-Namib[J]. South African Journal of Science, 2001, 97: 23-27.
45 Backes M, Jacobi J. Classification of Weed Patches in QuickBird Images: Verification by Ground Truth Data[J]. EARSeL eProceedings, 2006, 5: 173-179.
46 Liu Q. Using the CBERS-04 Multispectral Data Tasseled Cap Transformation to Detect the Quasi-Circular Vegetation Patches[C]// IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), Yokohama, Japan, 2019: 3708-3711.
47 Castillejo-Gonzalez I L, Pena-Barragan J M, Jurado-Exposito M, et al. Evaluation of Pixel- and Object-based Approaches for Mapping Wild Oat (Avena sterilis) Weed Patches in Wheat Fields Using QuickBird Imagery for Site-Specific Management[J].European Journal of Agronomy,2014,59:57-66.
48 Mafanya M, Tsele P, Botai J, et al. Evaluating Pixel and Object Based Image Classification Techniques for Mapping Plant Invasions from UAV Derived Aerial Imagery: Harrisia Pomanensis as a Case Study[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 129: 1-11.
49 McGlynn I, Okin G S. Characterization of Shrub Distribution Using High Spatial Resolution Remote Sensing: Ecosystem Implications for a Former Chihuahuan Desert Grassland. Remote Sensing of Environment, 2006, 101: 554-566.
50 Levin N, Mcalpine C, Phinn S, et al. Mapping Forest Patches and Scattered Trees from SPOT Images and Testing Their Ecological Importance for Woodland Birds in a Fragmented Agricultural Landscape[J]. International Journal of Remote Sensing, 2009, 30(12): 3147-3169.
51 Browning D M, Laliberte A S, Rango A. Temporal Dynamics of Shrub Proliferation: Linking Patches to Landscapes[J]. International Journal of Geographical Information Science, 2011, 25(6): 913-930.
52 Ghosh A, Joshi P K. A Comparison of Selected Classification Algorithms for Mapping Bamboo Patches in Lower Gangetic Plains Using Very High Resolution WorldView 2 Imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 26: 298-311.
53 Shi L, Liu Q, Huang C, et al. Comparing Pixel-based Random Forest and the Object-based Support Vector Machine Approaches to Map the Quasi-circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery[J]. IEEE Access,2020,8,228955-228966.doi:10.1109/ACCESS.2020. 3045057.
doi: 10.1109/ACCESS.2020. 3045057
54 Perez A J, Lopez F, Benlloch J V, et al. Colour and Shape Analysis Techniques for Weed Detection in Cereal Fields[J]. Computers and Electronics in Agriculture, 2000, 25(3): 197-212.
55 Vogt P, Riitters K H, Estreguil C, et al. Mapping Spatial Patterns with Morphological Image Processing[J]. Landscape Ecology, 2007, 22: 171-177.
56 Liu Q, Liu G, Huang C, et al. Using ALOS High Spatial Resolution Image to Detect Vegetation Patches[J]. Procedia Environmental Sciences, 2011, 10: 896-901.
57 Liu Qingsheng, Zhang Yunjie, Liu Gaohuan, et al. Comparison of Different Spatial Resolution Images of ZY-3 Satellite to Patch Vegetation Detection[J]. Bulletin of Surveying and Mapping, 2014(11): 16-20.刘庆生, 张韵婕, 刘高焕,等. 资源三号卫星不同空间分辨率图像斑状植被检测比较研究[J]. 测绘通报, 2014(11): 16-20.
58 Fassnacht E E, Latifi H, Sterenczak K, et al. A Review of Studies on Tree Species Classification from Remotely Sensed Data[J]. Remote Sensing of Environment, 2016, 186: 64-87.
59 Jing L, Hu B, Noland T, et al. An Individual Tree Crown Delineation Method based on Multi-scale Segmentation of Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 70: 88-98.
60 Guo Yushan, Liu Qingsheng, Liu Gaohuan, et al. Individual Tree Crown Extraction of High Resolution Image Based on Marker-controlled Watershed Segmentation Method[J]. Journal of Geo-information Sciences, 2016, 18(9): 1259-1266.
60 郭昱杉, 刘庆生, 刘高焕,等. 基于标记控制分水岭分割方法的高分辨率遥感影像单木树冠提取[J]. 地球信息科学学报, 2016, 18(9): 1259-1266.
61 Yang Huihua, Zhao Lingling, Pan Xipeng, et al. Overlapping Cell Segmentation based on Level Set and Concave Area Detection[J]. Journal of Beijing University of Posts and Telecommunications, 2016, 39(6): 11-16.
61 杨辉华, 赵玲玲, 潘细朋,等. 基于水平集和凹点区域检测的粘连细胞分割方法[J]. 北京邮电大学学报, 2016, 39(6): 11-16.
62 Wang Pin, Liu Qianqian, Wang Lirui, et al. Image Segmentation and Classification of Cytological Cells Based on Multi-features Clustering and Chain Splitting Model[J]. Journal of Biomedical Engineering, 2017, 34(4): 614-621.
62 王品, 刘倩倩, 王力锐,等. 多特征聚类与粘连分离模型的细胞抹片图像分割与分类[J]. 生物医学工程杂志, 2017, 34(4): 614-621.
63 Wang Ya. Adaptive Marked Watershed Segmentation Algorithm for Red Blood Cell Images[J]. Journal of Image and Graphics, 2017, 22(12): 1779-1787.
63 王娅. 血液红细胞图像自适应标记分水岭分割算法[J]. 中国图象图形学报, 2017, 22(12): 1779-1787.
64 Roerdink J B T M, Meijster A. The Watershed Transform: Definitions, Algorithms and Parallelization Strategies[J]. Fundamenta Informaticae, 2001, 41: 187-228.
65 Sharma A K, Bala A. Maker based Watershed Transformation for Image Segmentation[J]. International Journal of Computer Science Engineering and Information Technology Research, 2013, 3(4): 187-192.
66 Dzyubachyk O, Van Cappellen W, Essers J, et al. Advanced Level Set-based Cell Tracking in Time-lapse Fluorescence Microscopy[J]. IEEE Transactions on Medical Imaging, 2010, 29: 852-867.
67 Liao M, Zhao Y, Li X, et al. Automatic Segmentation for Cell Images based on Bottleneck Detection and Ellipse Fitting[J]. Neurocomputing, 2016, 173: 615-622.
68 Molnar C, Jermyn I H, Kato Z, et al. Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours[J]. Scientific Reports, 2016, 6: 32412. doi: 10.1038/srep32412.
doi: 10.1038/srep32412
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