[1] [1] WULing，LIUXiangnan，LIUMeiling，et al. Review of the detection and attribution of multi-type forest disturbances using an ensemble of spatio-temporal-spectral information from remote sensing images［J］. National Remote Sensing Bulletin， 2024， 28（3）： 558-575.

[2] [2] YANWei， ZHOUWen， YILilong， et al. Research progress of remote sensing classification and change monitoring on forest types［J］. Remote Sensing Technology and Application， 2019， 34（3）： 445-454.

[3] [3] WANGNing， YUECairong， LUOHongbin， et al. Research progress on remote sensing image detection methods for forest disturbance［J］. World Forestry Research， 2022， 35（4）： 40-46.

[4] [4] ZHANGLiangpei， WUChen. Advances and future development of change detection for multi-temporal remote sensing imagery［J］. Acta Geodaetica et Cartographica Sinica， 2017， 46（10）： 1447-1459.

[5] [5] QIANS Y， XUEZ H， JIAM M， et al. Temporal-spectral-semantic-aware convolutional transformer network for multi-class tidal wetland change detection in Greater Bay Area［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2024， 216： 16. DOI：10.1016/j.isprsjprs.2024.07.024.

[6] [6] LIShiming， WANGZhihui， HANXuewen， et al. Overview of forest resources change detection methods using remote sensing techniques［J］. Journal of Beijing Forestry University， 2011， 33（3）： 132-138.

[7] [7] ZHANGLiyun， ZHAOTianzhong， XIAChaozong， et al. Application of change detection technologies of remote sensing to forestry［J］. World Forestry Research， 2016， 29（2）： 44-48.

[8] [8] ZHAODan， ZHANGMiao， YUMingzhao， et al. Monitoring agriculture and forestry recovery after the Wenchuan earthquake［J］. Journal of Remote Sensing， 2014， 18（4）： 958-970.

[9] [9] MANCINOG， NOLÈA， RIPULLONEF， et al. Landsat TM imagery and NDVI differencing to detect vegetation change： Assessing natural forest expansion in Basilicata， southern Italy［J］.Forest-Biogeosciences and Forestry，2014， 7（2）： 75-84. DOI： 10.3832/ifor0909-007

[10] [10] PANIGRAHYR K， RABINDRAK， et al. Forest cover change detection of Western Ghats of Maharashtra using satellite remote sensing based visual interpretation technique［J］. Current Science， 2010， 98（5）： 657-664. DOI：10.18520/cs/v98/i5/657-664.

[11] [11] ZHOUKe， YANGYongqing， ZHANGYanna， et al. Review of land use classification methods based on optical remote sensing images［J］. Science Technology and Engineering， 2021， 21（32）： 13603-13613.

[12] [12] LIUSicong， DUKecheng， ZHENGYongjie， et al. Remote sensing change detection technology in the era of artificial intelligence：Inheritance，development and challenges［J］.National Remote Sensing Bulletin，2023，27（9）：1975-1987.

[13] [13] WANGXuhong. A method experiment of making land use and land cover map based on satellite images—A national1∶50 000 land use/land cover map making experimental project［J］. Bulletin of Surveying and Mapping， 2001（S1）： 6-8.

[14] [14] WANGKai. Study on extraction of forest land type change information based on ZY-1-02C and OLI images［D］. Nanjing： Nanjing Forestry University， 2015.

[15] [15] PANGBo， WANGHao， NINGXiaogang. Application of decision tree post-classification comparison based on stable pixels in forestland change detection［J］. Chinese Journal of Ecology， 2018， 37（9）： 2849-2855.

[16] [16] WANGChongyang，TIANXin. Forest cover change detection based on GF-1 PMS data［J］. Remote Sensing Technology and Application， 2021， 36（1）： 208-216.

[17] [17] BAR S， PARIDAB R， PANDEYA C. Landsat-8 and Sentinel-2 based forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand， Western Himalaya［J］. Remote Sensing Applications： Society and Environment，2020，18：100324. DOI：10.1016/j.rsase. 2020.100324

[18] [18] WANGXiaohui， TANBingxiang， LIShiming， et al. Object-oriented forest change detection based on multi-feature change vector analysis［J］. Forest Research， 2021， 34（1）： 98-105.

[19] [19] FANYinglong， TANGSainan， TANBingxiang. Forest cover change detection based on multi-scale segmentation and tasseled cap transformation over plateau area［J］. Journal of Beijing Forestry University， 2023， 45（4）： 60-69.

[20] [20] CHENC F， SONN T， CHANGN-B， et al. Multi-decadal mangrove forest change detection and prediction in Honduras， central America， with landsat imagery and a Markov chain model［J］. Remote Sensing， 2013， 5（12）： 6408-6426. DOI： 10.3390/rs5126408

[21] [21] SUNJianming， ZHAOMengxin， HAOXuyao. Research review of remote sensing image change detection methods［J］. Computer Engineering and Applications， 2024， 60（20）： 30-48.

[22] [22] ZHANGXinlong， CHENXiuwan， LIFei， et al. Change detection method for high resolution remote sensing images using deep learning［J］. Acta Geodaetica et Cartographica Sinica， 2017， 46（8）： 999-1008.

[23] [23] MAYongjun，ZHANGYi，WANGGuanglai， et al. Improved forest change detection method for remote sensing imagery using UNet++［J］. Journal of Forest and Environment， 2024， 44（3）： 317-327.

[24] [24] LIHeng. Research on forest change detection methods based on remote sensing images［D］. Changsha： Central South University of Forestry and Technology， 2022.

[25] [25] AIQiuyi， HUANGHuaguo， GUOYing， et al. Forest change detection based on Siamese neural network with GF-2 image： A case of Jiande forest farm， Zhejiang［J］. Remote Sensing Technology and Application， 2024， 39（1）： 24-33.

[26] [26] XIANGJun.Dynamic forest change detection research based on deep learning［D］. Changsha： Central South University of Forestry and Technology， 2023.

[27] [27] XIANGJun， YANEnping，JIANGJiawei， et al. Research on forest change detection based on fully convolutional network and low resolution label［J］. Journal of Nanjing Forestry University（Natural Sciences Edition），2024，48（1）：187-195.

[28] [28] QIXinbo. Overview of object-oriented change detection in remote sensing images［J］. Beijing Surveying and Mapping， 2021， 35（4）： 427-431.

[29] [29] WUZ Y. Remote sensing image change detection based on U-Net model［D］. Harbin： Harbin Normal University， 2021.

[30] [30] PARKERB M， LEWIST， SRIVASTAVAS K. Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors［J］. Remote Sensing of Environment， 2015， 170： 340-349. DOI： 10.1016/j.rse.2015.09.014

[31] [31] ALJAHDALIM O， MUNAWARS， KHANW R. Monitoring mangrove forest degradation and regeneration： Landsat time series analysis of moisture and vegetation indices at rabigh lagoon，red sea［J］.Forests， 2021，12（1）：52. DOI：10. 3390/f12010052

[32] [32] ZHANGLianhua， PANGYong， YUECairong， et al. Forest disturbance automatic identification method based on time series Landsat image of tasseled cap transformation［J］. Forest Inventory and Planning， 2013， 38（2）： 6-12.

[33] [33] CHENLi， LINHui. Vegetation information extraction based on K-T transform and principal component transform［J］. Journal of Central South University of Forestry & Technology， 2014， 34（6）： 81-84.

[34] [34] WANGF G， XUY J. Comparison of remote sensing change detection techniques for assessing hurricane damage to forests［J］. Environmental Monitoring and Assessment， 2010， 162（1）： 311-326. DOI： 10.1007/s10661-009-0798-8

[35] [35] WULiwei. Study on information extraction of small class land type change based on RS technology［D］. Nanjing： Nanjing Forestry University， 2014.

[36] [36] GRIFFITHSP， KUEMMERLET， BAUMANNM， et al. Forest disturbances， forest recovery， and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites［J］. Remote Sensing of Environment， 2014， 151： 72-88. DOI： 10.1016/j.rse.2013.04.022

[37] [37] LUOHao， SUNHua， HUMan， et al. Study on forest change detection of Huangfengqiao forest farm using Landsat data［J］. Journal of Central South University of Forestry & Technology， 2017， 37（12）： 65-71.

[38] [38] BULLOCKE L， WOODCOCKC E， HOLDENC E. Improved change monitoring using an ensemble of time series algorithms［J］. Remote Sensing of Environment，2020，238： 111165. DOI： 10.1016/j.rse.2019.04.018

[39] [39] ZHAOZhongming， MENGYu， YUEAnzhi， et al. Review of remotely sensed time series data for change detection［J］. Journal of Remote Sensing， 2016， 20（5）： 1110-1125.

[40] [40] DE JONGS M， SHENY C， DE VRIESJ， et al. Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm［J］. International Journal of Applied Earth Observation and Geoinformation，2021，97：102293. DOI：10.1016/j.jag. 2020. 102293

[41] [41] HUAJianwen. Spatial-temporal pattern of forest disturbance and restoration based on LandTrendr algorithm and machine learning［D］. Hangzhou： Zhejiang A & F University， 2021.

[42] [42] DEVRIESB， VERBESSELTJ， KOOISTRAL， et al. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series［J］. Remote Sensing of Environment， 2015， 161： 107-121. DOI： 10.1016/j.rse. 2015.02.012

[43] [43] SMITHV， PORTILLO-QUINTEROC， SANCHEZ-AZOFEIFAA， et al. Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica［J］. Remote Sensing of Environment， 2019， 221： 707-721. DOI： 10.1016/j.rse.2018.12.020

[44] [44] WUL， LIZ L， LIUX N， et al. Multi-type forest change detection using BFAST and monthly Landsat time series for monitoring spatiotemporal dynamics of forests in subtropical wetland［J］.Remote Sensing，2020，12（2）：341. DOI：10.3390/rs12020341

[45] [45] YES， ROGANJ， ZHUZ， et al. A near-real-time approach for monitoring forest disturbance using Landsat time series： Stochastic continuous change detection［J］. Remote Sensing of Environment，2021，252：112167. DOI： 10.1016/j.rse. 2020. 112167

[46] [46] CHENS J， WOODCOCKC E， BULLOCKE L， et al. Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis［J］. Remote Sensing of Environment，2021，265：112648. DOI：10.1016/j.rse. 2021. 112648

[47] [47] GIANNETTIF， PECCHIM， TRAVAGLINID， et al. Estimating VAIA windstorm damaged forest area in Italy using time series Sentinel-2 imagery and continuous change detection algorithms［J］. Forests，2021，12（6）：680. DOI：10.3390/f12060680

[48] [48] SHIKaiyuan. Dynamic monitoring of forest resources in central and eastern europe countries based on random forest and change detection and update［D］. Beijing： Beijing Forestry University， 2022.

[49] [49] OTEROV，VAN DE KERCHOVER，SATYANARAYANAB， et al. An analysis of the early regeneration of mangrove forests using Landsat time series in the Matang mangrove forest reserve， peninsular Malaysia［J］. Remote Sensing， 2019， 11（7）： 774. DOI： 10.3390/rs11070774

[50] [50] HUShengyuan，PANGYong，MENGShili，et al. Annual forest disturbance detection using time series Landsat 8 OLI data［J］. Forest Research， 2020， 33（6）： 65-72.

[51] [51] CARDILLEJ A， PEREZE， CROWLEYM A， et al. Multi-sensor change detection for within-year capture and labelling of forest disturbance［J］. Remote Sensing of Environment， 2022， 268： 112741. DOI： 10.1016/j.rse.2021.112741

[52] [52] RODMANK C， ANDRUSR A， VEBLENT T， et al. Disturbance detection in Landsat time series is influenced by tree mortality agent and severity， not by prior disturbance［J］. Remote Sensing of Environment， 2021， 254： 112244. DOI： 10.1016/j.rse.2020.112244

[53] [53] HEALEYS P， COHENW B， YANGZ Q， et al. Mapping forest change using stacked generalization： An ensemble approach［J］. Remote Sensing of Environment， 2018， 204： 717-728. DOI： 10.1016/j.rse.2017.09.029

[54] [54] COHENW B， YANGZ Q， HEALEYS P， et al. A LandTrendr multispectral ensemble for forest disturbance detection［J］. Remote Sensing of Environment， 2018， 205： 131-140. DOI： 10.1016/j.rse.2017.11.015

[55] [55] HISLOPS， JONESS， SOTO-BERELOVM， et al. A fusion approach to forest disturbance mapping using time series ensemble techniques［J］. Remote Sensing of Environment， 2019， 221： 188-197. DOI： 10.1016/j.rse.2018.11.025

[56] [56] COHENW B， HEALEYS P， YANGZ Q， et al. Diversity of algorithm and spectral band inputs improves Landsat monitoring of forest disturbance［J］.Remote Sensing，2020，12（10）：1673. DOI：10.3390/rs12101673

[57] [57] ZHANGLifu， WANGSa， LIUHualiang， et al. From spectrum to spectrotemporal： Research on time series change detection of remote sensing［J］. Geomatics and Information Science of Wuhan University， 2021， 46（4）： 451-468.