Remote Sensing Technology and Application, Volume. 40, Issue 4, 1002(2025)

Optimal Feature Selection for Forest Disturbance Monitoring

Junying SONG1,2, Xiufang ZHU1,2,3、*, Mingxiu TANG1,2, and Rui GUO1,2
Author Affiliations
  • 1State Key Laboratory of Remote Sensing and Digital Earth, Beijing Normal University, Beijing100875, China
  • 2Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing100875, China
  • 3Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
  • show less
    References(37)

    [1] [1] CAOY,FENGW,QUANY,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] AZIZG, MINALLAHN, SAEEDA, 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] CURTISA, KYLEP. 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

    [4] [4] ZHANGBaojun,MAYuling,LIYi.Standardization of natural disaster classification in China [J]. Journal of Natural Disasters, 2013, 22(5): 8-12.

    [5] [5] ZHUL, LIW, CIAISP, 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] TSEGAYG, MENGX 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] VILHARU,KERMAVNARJ,KOZAMERNIKE,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] MCDOWELLN G, ALLENC D, ANDERSON-TEIXEIRAK, et al. Pervasive shifts in forest dynamics in a changing world[J].Science,2020,368(6494):eaaz9463. DOI:10.1126/science.aaz9463

    [9] [9] PENGLing, XUSuning, MEIJunjun, et al. Earthquake-induced landslide recognition using high-resolution remote sensing images[J]. Journal of Remote Sensing, 2017, 21(4):509-518.

    [10] [10] CHENJinpeng, SUNLin, XIEFeifei, et al. Research on fire detection method based on deep neural network MODIS data[J]. Remote Sensing Technology and Application, 2024, 39(4): 905-916.

    [11] [11] DE LUCAG, SILVAJ M N, MODICAG. 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

    [12] [12] WANGNa, LIQiangzi, DUXin, et al. Identification of main crops based on the univariate feature selection in Subei[J]. Journal of Remote Sensing, 2017, 21(4):519-530.

    [13] [13] DINGHui, ZHANGMaosheng, ZHUWeihong, et al. High resolution remote sensing for the identification of Loess landslides: Example from Yan’an City [J]. Northwestern Geology, 2019, 52(3):231-239.

    [14] [14] YANMing, PANGYong, HEYunling, et al. Remote sensing based land cover classification of Pu′er City using GEE cloud platform and Sentinel-2 data[J]. Remote Sensing Technology and Application,2023,38(2):432-442.

    [15] [15] ORYNBAIKYZYA, GESSNERU, MACKB, 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] XUJ, ZENGQ, ZHANGZ. 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] MULLISSAA, VOLLRATHA, ODONGO-BRAUNC, 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

    [18] [18] KANGXiaoyan, ZHANGAiwu, HUShaoxing, et al. Hyperspectral images adaptive dimensionality reduction optimized by JM transform[J]. Journal of Remote Sensing, 2020, 24(1): 67-75.

    [19] [19] NINGXiaogang, CHANGWentao, WANGHao, et al. Extraction of marsh wetland in Heilongjiang Basin based on GEE and multi-source remote sensing data[J]. National Remote Sensing Bulletin, 2022, 26(2): 386-396.

    [20] [20] XIEYi, WANGJianan, LIUYu. Research on winter wheat planting area identification method basedon Sentinel-1/2 data feature optimization[J]. Transactions of the Chinese Society for Agricultural Machinery, 2024, 55(2): 231-241.

    [21] [21] NAVARROG, CABALLEROI, SILVAG, 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] VERAVERBEKES,GITASI,KATAGIST,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] BIRTHG S, MCVEYG 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] RICHARDSONA J, WIEGANDC 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 RAON,GARGP K,GHOSHS K,et al. Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery[J].The Journal of Agricultural Sci-ence,2008,146(1):65-75.DOI:10.1017/s0021859607007514

    [26] [26] AGAPIOUA. 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/app 10144764

    [27] [27] ÇOLAKE,CHANDRAM,SUNARF.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,Re-mote Sensing and Spatial Information Sciences,2021,XLIII-B3-2021:701-706. DOI:isprs-archives-xliii-b3-2021-701-2021

    [28] [28] FLORESA,HERNDONK,THAPAR,et al.The SAR Hand-book:Comprehensive Methodologies for Forest Monitoring and Biomass Estimation[M].2019, DOI:10.25966/nr2c-s697

    [29] [29] CHANGJ G, SHOSHANYM, 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

    [30] [30] HEYun, HUANGChong, LIHe, et al. Land-cover classification of random forest based on Sentinel-2A image feature optimization[J]. Resources Science, 2019, 41(5):992-1001.

    [31] [31] LIZhifeng, ZHUGuchang, DONGTaifeng. Application of GLCM-based texture features to remote sensing image classification[J]. Geology and Exploration, 2011, 47(3):456-461.

    [32] [32] HUYufu, DENGLiangji, KUANGXianhui, et al. Study on land use classification of high resolution remote sensing image based on texture feature[J]. Geography and Geo-Information Science, 2011, 27(5):42-45,68.

    [33] [33] LIP, XUH. 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] DOBRINIĆD, GAŠPAROVIĆM, MEDAKD. 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] LESTARIA I, RIZKINIAM, SUDIANAD. 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] ATKINSJ W, BOND-LAMBERTYB, FAHEYR 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] SENFC, SEIDLR. Mapping the forest disturbance regimes of Europe[J].Nature Sustainability,2020,4(1):63-70. DOI:10.1038/s41893-020-00609-y

    Tools

    Get Citation

    Copy Citation Text

    Junying SONG, Xiufang ZHU, Mingxiu TANG, Rui GUO. Optimal Feature Selection for Forest Disturbance Monitoring[J]. Remote Sensing Technology and Application, 2025, 40(4): 1002

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Apr. 11, 2024

    Accepted: --

    Published Online: Aug. 26, 2025

    The Author Email: Xiufang ZHU (zhuxiufang@bnu.edu.cn)

    DOI:10.11873/j.issn.1004-0323.2025.4.1002

    Topics