[1] [1] CAO Y, FENG W, QUAN Y,et al. Forest disaster detection method based on ensemble spatial-spectral genetic algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 7375-7390. DOI: 10.1109/JSTARS.2022.3199539

[2] [2] AZIZ G, MINALLAH N, SAEED A,et al. Remote sensing based forest cover classification using machine learning[J]. Scientific Reports, 2024, 14: 69. DOI: 10.1038/s41598-023-50863-1

[3] [3] CURTIS A, KYLE P. Methods for mapping and monitoring global glaciovolcanism[J]. Journal of Volcanology and Geothermal Research, 2017, 333: 134-144. DOI: 10.1016/j.jvolgeores.2017.01.017

[5] [5] ZHU L, LI W, CIAIS P,et al. Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation[J]. Nature Geoscience, 2023, 16(3): 244-249. DOI: 10.1038/s41561-023-01137-y

[6] [6] TSEGAY G, MENG X Z. Impact of ex-closure in above and below ground carbon stock biomass[J]. Forests, 2021, 12(2): 130. DOI: 10.3390/f12020130

[7] [7] VILHAR U, KERMAVNAR J, KOZAMERNIK E,et al. The effects of large-scale forest disturbances on hydrology: An overview with special emphasis on Karst aquifer systems[J]. Earth-Science Reviews, 2022, 235: 104243. DOI: 10.1016/j.earscirev.2022.104243

[8] [8] MCDOWELL N G, ALLEN C D, ANDERSON-TEIXEIRA K,et al. Pervasive shifts in forest dynamics in a changing world[J]. Science, 2020, 368(6494): eaaz9463. DOI: 10.1126/science.aaz9463

[11] [11] DE LUCA G, SILVA J M N, MODICA G. A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems[J]. GIScience & Remote Sensing, 2021, 58(4): 516-541. DOI: 10.1080/15481603.2021.1907896

[15] [15] ORYNBAIKYZY A, GESSNER U, MACK B,et al. Crop type classification using fusion of Sentinel-1 and Sentinel-2 data: Assessing the impact of feature selection, optical data availability, and parcel sizes on the accuracies[J]. Remote Sensing, 2020, 12(17): 2779. DOI: 10.3390/rs12172779

[16] [16] XU J, ZENG Q, ZHANG Z. The relationship between Amazon rainforest deforestation and economic development[J]. Highlights in Business, Economics and Management, 2023, 5: 273-278. DOI: 10.54097/hbem.v5i.5085

[17] [17] MULLISSA A, VOLLRATH A, ODONGO-BRAUN C,et al. Sentinel-1 SAR backscatter analysis ready data preparation in Google Earth Engine[J]. Remote Sensing, 2021, 13(10): 1954. DOI: 10.3390/rs13101954

[21] [21] NAVARRO G, CABALLERO I, SILVA G,et al. Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 58: 97-106. DOI: 10.1016/j.jag.2017.02.003

[22] [22] VERAVERBEKE S, GITAS I, KATAGIS T,et al. Assessing post-fire vegetation recovery using red-near infrared vegetation indices: Accounting for background and vegetation variability[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 68: 28-39. DOI: 10.1016/j.isprsjprs.2011. 12.007

[23] [23] BIRTH G S, MCVEY G R. Measuring the color of growing turf with a reflectance spectrophotometer[J]. Agronomy Journal, 1968, 60(6): 640-643. DOI: 10.2134/agronj1968.00021962006000060016x

[24] [24] RICHARDSON A J, WIEGAND C L. Distinguishing vegetation from soil background information[J]. Photogrammetric Engineering and Remote Sensing, 1977, 43(12): 1541-1522. DOI: 10.1109/TGE.1977.294499

[25] [25] RAMA RAO N, GARG P K, GHOSH S K,et al. Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery[J].The Journal of Agricultural Science, 2008, 146（1）: 65-75. DOI: 10.1017/s0021859607007514

[26] [26] AGAPIOU A. Estimating proportion of vegetation cover at the vicinity of archaeological sites using Sentinel-1 and -2 data, supplemented by crowdsourced OpenStreetMap geodata[J]. Applied Sciences, 2020, 10（14）: 4764. DOI: 10.3390/app10144764

[27] [27] OLAK E, CHANDRA M, SUNAR F.The use of Sentinel 1/2 vegetation indexes with Gee time series data in detecting land cover changes in the Sinop nuclear power plant construction site[J].The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021, XLIII-B3-2021: 701-706. DOI: isprs-archives-xliii-b3-2021-701-2021

[28] [28] FLORES A, HERNDON K, THAPA R,et al. The SAR Hand-book: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation[M]. 2019, DOI: 10.25966/nr2c-s697

[29] [29] CHANG J G, SHOSHANY M, OH Y. Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(12): 7102-7108. DOI: 10.1109/TGRS.2018.2848285

[33] [33] LI P, XU H. Land-cover change detection using one-class support vector machine[J]. Photogrammetric Engineering and Remote Sensing, 2010, 76(3): 255-263. DOI: 10.14358/PERS.76.3.255

[34] [34] DOBRINI D, GAPAROVI M, MEDAK D. Evaluation of feature selection methods for vegetation mapping using multitemporal sentinel imagery[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022, XLIII-B3-2022: 485-491. DOI: 10.5194/isprs- archives-xliii-b3-2022-485-2022

[35] [35] LESTARI A I, RIZKINIA M, SUDIANA D. Evaluation of combining optical and SAR imagery for burned area mapping using machine learning[C]//2021 IEEE 11th Annual Computing and Communication Workshop and Conference(CCWC).2021: 0052-0059. DOI: 10.1109/CCWC51732.2021.9376117.

[36] [36] ATKINS J W, BOND-LAMBERTY B, FAHEY R T,et al. Application of multidimensional structural characterization to detect and describe moderate forest disturbance[J]. Ecosphere, 2020, 11(6): e03156. DOI: 10.1002/ecs2.3156

[37] [37] SENF C, SEIDL R. Mapping the forest disturbance regimes of Europe[J].Nature Sustainability, 2020, 4(1): 63-70. DOI: 10.1038/s41893-020-00609-y