Frisia S, Borsato A. Karst Develop Sedimentol. 2010;61:269–318.
Article
Google Scholar
Ford D, Williams PD. Karst hydrogeology and geomorphology. New York: Wiley; 2013.
Google Scholar
Jiang Z, Lian Y, Qin X. Rocky desertification in Southwest China: impacts, causes, and restoration. Earth Sci Rev. 2014;132:1–12.
Article
Google Scholar
Jiang Z, Liu H, Wang H, Peng J, Meersmans J, Green SM, Quine TA, Wu X, Song Z. Bedrock geochemistry influences vegetation growth by regulating the regolith water holding capacity. Nat Commun. 2020;11(1):1–9.
Google Scholar
Wu L, Wang S, Bai X, Tian Y, Luo G, Wang J, Li Q, Chen F, Deng Y, Yang Y. Climate change weakens the positive effect of human activities on karst vegetation productivity restoration in southern China. Ecol Ind. 2020;115:106392.
Article
Google Scholar
Zhao S, Pereira P, Wu X, Zhou J, Cao J, Zhang W. Global karst vegetation regime and its response to climate change and human activities. Ecol Ind. 2020;113:106208.
Article
Google Scholar
Harrington TJ, Mitchell DT. Characterization of dryas octopetala ectomycorrhizas from limestone karst vegetation, western Ireland. Can J Bot. 2002;80(9):970–82.
Article
Google Scholar
Yue Y, Wang K, Zhang B, Liu B, Chen H, Zhang M. Uncertainty of remotely sensed extraction of information of karst rocky desertification. Adv Earth Sci. 2011;26(3):266.
Google Scholar
Blasi C, Di Pietro R, Pelino G. The vegetation of alpine belt karst-tectonic basins in the central apennines (Italy). Plant Biosys Int J Dealing Aspects Plant Biol. 2005;139(3):357–85.
Google Scholar
Bátori Z, Csiky J, Farkas T, Vojtkó EA, Erdős L, Kovács D, Wirth T, Körmöczi L, Vojtkó A. The conservation value of karst dolines for vascular plants in woodland habitats of Hungary: Refugia and climate change. Int J Speleol. 2014;43(1):2.
Article
Google Scholar
Belward AS, Skøien JO. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS J Photogramm Remote Sens. 2015;103:115–28.
Article
Google Scholar
Zhang R, Luo H, Zou Y, Liu G. Discussion on possibility of the identification of karst vegetation communities based on OLI data. In: 2014 the third international conference on agro-geoinformatics; 2014. IEEE. p. 1–7.
Qu L, Han W, Lin H, Zhu Y, Zhang L. Estimating vegetation fraction using hyperspectral pixel unmixing method: a case study of a karst area in China. IEEE J Sel Topics Appl Earth Observ Remote Sens. 2014;7(11):4559–65.
Article
Google Scholar
Zhang X, Shang K, Cen Y, Shuai T, Sun Y. Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method. Int J Appl Earth Obs Geoinf. 2014;31:86–94.
CAS
Google Scholar
Song L, Yulun A, Houqiang H. Automated method based on change detection for extracting karst rock desertification information using remote sensing. Remote Sens Technol Appl. 2012;27(1):149–53.
Google Scholar
Guimarães N, Pádua L, Marques P, Silva N, Peres E, Sousa JJ. Forestry remote sensing from unmanned aerial vehicles: a review focusing on the data, processing and potentialities. Remote Sens. 2020;12(6):1046.
Article
Google Scholar
Zhang Z, Ouyang Z, Xiao Y, Xiao Y, Xu W. Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China. Environ Monit Assess. 2017;189(6):1–19.
Article
Google Scholar
Xiao D, Zhou Z, Li Q, Huang D, Meng Z, Zhang Y. Construction of terrain information extraction model in the karst mountainous terrain fragmentation area based on UAV remote sensing. In: 2022 3rd international conference on geology, mapping and remote sensing (ICGMRS); 2022. IEEE. P. 716–27.
Pádua L, Vanko J, Hruška J, Adão T, Sousa JJ, Peres E, Morais R. UAS, sensors, and data processing in agroforestry: a review towards practical applications. Int J Remote Sens. 2017;38(8–10):2349–91.
Article
Google Scholar
de Castro AI, Shi Y, Maja JM, Peña JM. UAVs for vegetation monitoring: overview and recent scientific contributions. Remote Sens. 2021;13(11):2139.
Article
Google Scholar
Dainelli R, Toscano P, Di Gennaro SF, Matese A. Recent advances in unmanned aerial vehicle forest remote sensing—a systematic review. Part I: a general framework. Forests. 2021;12(3):327.
Article
Google Scholar
Riihimäki H, Luoto M, Heiskanen J. Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data. Remote Sens Environ. 2019;224:119–32.
Article
Google Scholar
Moreno JL, Ortega JF, Moreno MÁ, Ballesteros R. Using an unmanned aerial vehicle (UAV) for lake management: ecological status, lake regime shift and stratification processes in a small Mediterranean karstic lake. Limnetica. 2022;41(2):000–000.
Article
Google Scholar
Zhou R, Yang C, Li E, Cai X, Yang J, Xia Y. Object-based wetland vegetation classification using multi-feature selection of unoccupied aerial vehicle RGB imagery. Remote Sens. 2021;13(23):4910.
Article
Google Scholar
Kampen M, Lederbauer S, Mund J, Immitzer M. Uav-based multispectral data for tree species classification and tree vitality analysis. Dreiländertagung der DGPF der OVG und der SGPF in Wien sterreich Publikationen der DGPF. 2019;28:01.
Google Scholar
Tmušić G, Manfreda S, Aasen H, James MR, Gonçalves G, Ben-Dor E, Brook A, Polinova M, Arranz JJ, Mészáros J. Current practices in UAS-based environmental monitoring. Remote Sens. 2020;12(6):1001.
Article
Google Scholar
Dai L, Zhang G, Gong J, Zhang R. Autonomous learning interactive features for hyperspectral remotely sensed data. Appl Sci. 2021;11(21):10502.
Article
CAS
Google Scholar
Puliti S, Breidenbach J, Astrup R. Estimation of forest growing stock volume with UAV laser scanning data: can it be done without field data? Remote Sens. 2020;12(8):1245.
Article
Google Scholar
Chen G, Weng Q, Hay GJ, He Y. Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities. GI Sci Remote Sens. 2018;55(2):159–82.
Article
Google Scholar
Pádua L, Adão T, Hruška J, Guimarães N, Marques P, Peres E, Sousa JJ. Vineyard classification using machine learning techniques applied to RGB-UAV imagery. In: IGARSS 2020–2020 IEEE international geoscience and remote sensing symposium; 2020. IEEE. p. 6309–12.
Fu B, Liu M, He H, Lan F, He X, Liu L, Huang L, Fan D, Zhao M, Jia Z. Comparison of optimized object-based rf-dt algorithm and segnet algorithm for classifying karst wetland vegetation communities using ultra-high spatial resolution uav data. Int J Appl Earth Obs Geoinf. 2021;104:102553.
Google Scholar
Zhang N, Wang Y, Zhang X. Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images. Plant Methods. 2020;16(1):1–19.
Article
Google Scholar
Mäyrä J, Keski-Saari S, Kivinen S, Tanhuanpää T, Hurskainen P, Kullberg P, Poikolainen L, Viinikka A, Tuominen S, Kumpula T. Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks. Remote Sens Environ. 2021;256:112322.
Article
Google Scholar
Li S-L, Liu C-Q, Chen J-A, Wang S-J. Karst ecosystem and environment: characteristics, evolution processes, and sustainable development. Agr Ecosyst Environ. 2021;306:107173.
Article
Google Scholar
Ma S, Zhang K. Low-altitude photogrammetry and remote sensing in UAV for improving mapping accuracy. Mobile Inform Sys 2022; 2022.
Iglhaut J, Cabo C, Puliti S, Piermattei L, O’Connor J, Rosette J. Structure from motion photogrammetry in forestry: a review. Current Forestry Rep. 2019;5(3):155–68.
Article
Google Scholar
Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
Article
Google Scholar
Du P, Samat A, Waske B, Liu S, Li Z. Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS J Photogramm Remote Sens. 2015;105:38–53.
Article
Google Scholar
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens. 2012;67:93–104.
Article
Google Scholar
Dalponte M, Ørka HO, Gobakken T, Gianelle D, Næsset E. Tree species classification in boreal forests with hyperspectral data. IEEE Trans Geosci Remote Sens. 2012;51(5):2632–45.
Article
Google Scholar
Millard K, Richardson M. On the importance of training data sample selection in random forest image classification: a case study in peatland ecosystem mapping. Remote Sens. 2015;7(7):8489–515.
Article
Google Scholar
Corcoran JM, Knight JF, Gallant AL. Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in Northern Minnesota. Remote Sens. 2013;5(7):3212–38.
Article
Google Scholar
Mammone A, Turchi M, Cristianini N. Support vector machines. Wiley Interdiscip Rev Comput Stat. 2009;1(3):283–9.
Article
Google Scholar
Sluiter R, Pebesma E. Comparing techniques for vegetation classification using multi-and hyperspectral images and ancillary environmental data. Int J Remote Sens. 2010;31(23):6143–61.
Article
Google Scholar
Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29(5):1189–232.
Article
Google Scholar
Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 2002;38(4):367–78.
Article
Google Scholar
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
Article
CAS
Google Scholar
Le Roux N, Bengio Y. Deep belief networks are compact universal approximators. Neural Comput. 2010;22(8):2192–207.
Article
Google Scholar
Chen Y, Lin Z, Zhao X, Wang G, Gu Y. Deep learning-based classification of hyperspectral data. IEEE J Select Topics Appl Earth Observ Remote Sens. 2014;7(6):2094–107.
Article
Google Scholar
Tao X, Li Y, Yan W, Wang M, Tan Z, Jiang J, Luan Q. Heritable variation in tree growth and needle vegetation indices of slash pine (Pinus elliottii) using unmanned aerial vehicles (UAVs). Ind Crops Prod. 2021;173:114073.
Article
CAS
Google Scholar
Castellaneta M, Rita A, Camarero JJ, Colangelo M, Ripullone F. Declines in canopy greenness and tree growth are caused by combined climate extremes during drought-induced dieback. Sci Total Environ. 2022;813:152666.
Article
CAS
Google Scholar
Leolini L, Moriondo M, Rossi R, Bellini E, Brilli L, López-Bernal Á, Santos JA, Fraga H, Bindi M, Dibari C. Use of sentinel-2 derived vegetation indices for estimating fPAR in olive groves. Agronomy. 2022;12(7):1540.
Article
Google Scholar
Mangewa LJ, Ndakidemi PA, Alward RD, Kija HK, Bukombe JK, Nasolwa ER, Munishi LK. Comparative assessment of UAV and sentinel-2 NDVI and GNDVI for preliminary diagnosis of habitat conditions in Burunge wildlife management area, Tanzania. Earth. 2022;3(3):769–87.
Article
Google Scholar
de Melo MVN, de Oliveira MEG, de Almeida GLP, Gomes NF, Morales KRM, Santana TC, Silva PC, Moraes AS, Pandorfi H, da Silva MV. Spatiotemporal characterization of land cover and degradation in the agreste region of Pernambuco, Brazil, using cloud geoprocessing on google earth engine. Remote Sens Appl Soc Environ. 2022;26:100756.
Google Scholar
Lamaamri M, Lghabi N, Ghazi A, El Harchaoui N, Adnan MSG, Shakiul Islam M. Evaluation of desertification in the middle Moulouya basin (north-east morocco) using sentinel-2 images and spectral index techniques. Earth Syst Environ. 2022;1:1–20.
Google Scholar
Li Q, Zhang C, Shen Y, Jia W, Li J. Quantitative assessment of the relative roles of climate change and human activities in desertification processes on the Qinghai-Tibet Plateau based on net primary productivity. CATENA. 2016;147:789–96.
Article
Google Scholar
Nadjla B, Assia S, Ahmed Z. Contribution of spectral indices of chlorophyll (RECl and GCI) in the analysis of multi-temporal mutations of cultivated land in the Mostaganem plateau. In: 2022 7th international conference on image and signal processing and their applications (ISPA); 2022. IEEE. p. 1–6.
Jiang F, Sun H, Ma K, Fu L, Tang J. Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms. Ecol Ind. 2022;143:109365.
Article
CAS
Google Scholar
Della-Silva JL, da Silva Junior CA, Lima M, da Silva RR, Shiratsuchi LS, Rossi FS, Teodoro LPR, Teodoro PE. Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches. Remote Sens Appl Soc Environ. 2022;26:100742.
Google Scholar
Gerardo R, de Lima IP. Monitoring duckweeds (Lemna minor) in small rivers using sentinel-2 satellite imagery: application of vegetation and water indices to the Lis River (Portugal). Water. 2022;14(15):2284.
Article
Google Scholar
Motohka T, Nasahara KN, Oguma H, Tsuchida S. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens. 2010;2(10):2369–87.
Article
Google Scholar
Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Obs Geoinf. 2015;39:79–87.
Google Scholar
Wang N, Guo Y, Wei X, Zhou M, Wang H, Bai Y. UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert. Ecol Ind. 2022;141:109155.
Article
Google Scholar
Ding J, Li Z, Zhang H, Zhang P, Cao X, Feng Y. Quantifying the aboveground biomass (AGB) of Gobi Desert Shrub communities in Northwestern China based on unmanned aerial vehicle (UAV) RGB images. Land. 2022;11(4):543.
Article
Google Scholar
Nasiri V, Darvishsefat AA, Arefi H, Griess VC, Sadeghi SMM, Borz SA. Modeling forest canopy cover: a synergistic use of Sentinel-2, aerial photogrammetry data, and machine learning. Remote Sensing. 2022;14(6):1453.
Article
Google Scholar
Steele MR, Gitelson AA, Rundquist DC, Merzlyak MN. Nondestructive estimation of anthocyanin content in grapevine leaves. Am J Enol Vitic. 2009;60(1):87–92.
Article
CAS
Google Scholar
Hati JP, Chaube NR, Hazra S, Goswami S, Pramanick N, Samanta S, Chanda A, Mitra D, Mukhopadhyay A. Mangrove monitoring in Lothian Island using airborne hyperspectral AVIRIS-NG data. Adv Space Res. 2022;1:1.
Google Scholar
Silva GD, Roberts DA, McFadden JP, King JY. Shifts in salt marsh vegetation landcover after debris flow deposition. Remote Sens. 2022;14(12):2819.
Article
Google Scholar
Geng X, Wang X, Fang H, Ye J, Han L, Gong Y, Cai D. Vegetation coverage of desert ecosystems in the Qinghai-Tibet Plateau is underestimated. Ecol Ind. 2022;137:108780.
Article
Google Scholar
Myneni RB, Hall FG, Sellers PJ, Marshak AL. The interpretation of spectral vegetation indexes. IEEE Trans Geosci Remote Sens. 1995;33(2):481–6.
Article
Google Scholar
Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sens Environ. 1996;55(2):95–107.
Article
Google Scholar
Gitelson AA, Kaufman YJ, Merzlyak MN. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ. 1996;58(3):289–98.
Article
Google Scholar
Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 1988;25(3):295–309.
Article
Google Scholar
Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. A modified soil adjusted vegetation index. Remote Sens Environ. 1994;48(2):119–26.
Article
Google Scholar
Gitelson AA, Gritz Y, Merzlyak MN. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol. 2003;160(3):271–82.
Article
CAS
Google Scholar
Pu R, Gong P, Yu Q. Comparative analysis of EO-1 ALI and Hyperion, and Landsat ETM+ data for mapping forest crown closure and leaf area index. Sensors. 2008;8(6):3744–66.
Article
Google Scholar
Sripada RP, Heiniger RW, White JG, Meijer AD. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agron J. 2006;98(4):968–77.
Article
Google Scholar
Bareth G, Bolten A, Gnyp M, Reusch S, Jasper J. Comparison of uncalibrated RGBVI with spectrometer-based NDVI derived from UAV sensing systems on field scale. Int Arch Photogr Remote Sens Spatial Inform Sci. 2016;41:837–43.
Article
Google Scholar
Barnes E, Clarke T, Richards S, Colaizzi P, Haberland J, Kostrzewski M, Waller P, Choi C, Riley E, Thompson T. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In: Proceedings of the fifth international conference on precision agriculture, Bloomington, USA; 2000.
van den Berg AK, Perkins TD. Nondestructive estimation of anthocyanin content in autumn sugar maple leaves. HortScience. 2005;40(3):685–6.
Article
Google Scholar
Gitelson AA, Keydan GP, Merzlyak MN. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys Res Lett. 2006;33(11):L11402.
Article
Google Scholar
Xiaoqin W, Miaomiao W, Shaoqiang W, Yundong W. Extraction of vegetation information from visible unmanned aerial vehicle images. Trans Chin Soc Agricul Eng. 2015;31(5):1.
Google Scholar
Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F, Chang C-C, Lin C-C, Meyer MD. Package ‘e1071.’ R J. 2019;1:1.
Google Scholar
Wickham H, Wickham MH. Package tidyverse. Easily Install Load ‘Tidyverse; 2017.
Heermann PD, Khazenie N. Classification of multispectral remote sensing data using a back-propagation neural network. IEEE Trans Geosci Remote Sens. 1992;30(1):81–8.
Article
Google Scholar
RColorBrewer S, Liaw MA. Package ‘randomforest.’ Berkeley: University of California, Berkeley; 2018.
Google Scholar
Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, Kenkel B, Team RC. Package ‘caret.’ R J. 2020;223:7.
Google Scholar
Candel A, Parmar V, LeDell E, Arora A. Deep learning with H2O. H2O ai Inc; 2016. p. 1–21.
Visa S, Ramsay B, Ralescu AL, Van Der Knaap E. Confusion matrix-based feature selection. MAICS. 2011;710(1):120–7.
Google Scholar
Zhang W, Liu H, Wu W, Zhan L, Wei J. Mapping rice paddy based on machine learning with Sentinel-2 multi-temporal data: model comparison and transferability. Remote Sens. 2020;12(10):1620.
Article
Google Scholar
Li Y, Al-Sarayreh M, Irie K, Hackell D, Bourdot G, Reis MM, Ghamkhar K. Identification of weeds based on hyperspectral imaging and machine learning. Front Plant Sci. 2021;11:2324.
Article
Google Scholar
Speiser JL, Miller ME, Tooze J, Ip E. A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst Appl. 2019;134:93–101.
Article
Google Scholar
Degenhardt F, Seifert S, Szymczak S. Evaluation of variable selection methods for random forests and omics data sets. Brief Bioinform. 2019;20(2):492–503.
Article
Google Scholar
Aiello S, Kraljevic T, Maj P. Package ‘h2o.’ Dim. 2015;2:12.
Google Scholar
Fu B, Liu M, He H, Fan D, Liu L, Huang L, Gao E. Comparison of multi-class and fusion of single-class SegNet model for classifying karst wetland vegetation using UAV images; 2021.
Rasmussen J, Ntakos G, Nielsen J, Svensgaard J, Poulsen RN, Christensen S. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur J Agron. 2016;74:75–92.
Article
Google Scholar
Mohamad N, Ahmad A, Khanan MFA, Din AHM. Surface elevation changes estimation underneath mangrove canopy using SNERL filtering algorithm and DoD technique on UAV-derived DSM data. ISPRS Int J Geo Inf. 2021;11(1):32.
Article
Google Scholar
Larrinaga AR, Brotons L. Greenness indices from a low-cost UAV imagery as tools for monitoring post-fire forest recovery. Drones. 2019;3(1):6.
Article
Google Scholar
Reichmuth A, Henning L, Pinnel N, Bachmann M, Rogge D. Early detection of vitality changes of multi-temporal Norway spruce laboratory needle measurements—the ring-barking experiment. Remote Sens. 2018;10(1):57.
Article
Google Scholar
Zhang X, Zhang F, Qi Y, Deng L, Wang X, Yang S. New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV). Int J Appl Earth Obs Geoinf. 2019;78:215–26.
Google Scholar
Huete AR, Liu H, van Leeuwen WJ. The use of vegetation indices in forested regions: issues of linearity and saturation. In: IGARSS'97 1997 IEEE international geoscience and remote sensing symposium proceedings remote sensing-a scientific vision for sustainable development; 1997. IEEE. p. 1966–8.
Fern RR, Foxley EA, Bruno A, Morrison ML. Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecol Ind. 2018;94:16–21.
Article
Google Scholar
Li F, Bai J, Zhang M, Zhang R. Yield estimation of high-density cotton fields using low-altitude UAV imaging and deep learning. Plant Methods. 2022;18(1):1–11.
Article
Google Scholar
Castelvecchi D. Can we open the black box of AI? Nature News. 2016;538(7623):20.
Article
CAS
Google Scholar