{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:23:15Z","timestamp":1775251395815,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T00:00:00Z","timestamp":1696636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021YFE0117800"],"award-info":[{"award-number":["2021YFE0117800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, the complex topographic and climate factors pose significant challenges to accurately estimating the FVC of mountain forests and grassland. Existing remote sensing products, FVC retrieval methods, and FVC samples may fail to meet the required accuracy standards. In this study, we propose a method based on spatio-temporal transfer learning for the retrieval of FVC in mountain forests and grasslands, using the mountain region of Huzhu County, Qinghai Province, as the study area. The method combines simulated FVC samples, Sentinel-2 images, and mountain topographic factor data to pre-train LSTM and 1DCNN models and subsequently transfer the models to HJ-2A\/B remote sensing images. The results of the study indicated the following: (1) The FVC samples generated by the proposed method (R2 = 0.7536, RMSE = 0.0596) are more accurate than those generated by the dichotomy method (R2 = 0.4997, RMSE = 0.1060) based on validation with ground truth data. (2) The LSTM model performed better than the 1DCNN model: the average R2 of the two models was 0.9275 and 0.8955; the average RMSE was 0.0653 and 0.0735. (3) Topographic features have a significant impact on FVC retrieval results, particularly in relatively high-altitude mountain regions (DEM &gt; 3000 m) or non-growing seasons (May and October). Therefore, the proposed method has better potential in FVC fine spatio-temporal retrieval of high-resolution mountainous remote sensing images.<\/jats:p>","DOI":"10.3390\/rs15194857","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T04:52:36Z","timestamp":1696827156000},"page":"4857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuxuan","family":"Huang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8913-1837","authenticated-orcid":false,"given":"Tingting","family":"Lv","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8369-4452","authenticated-orcid":false,"given":"Zui","family":"Tao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Hongming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0542-7455","authenticated-orcid":false,"given":"Ruoxi","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8338-2494","authenticated-orcid":false,"given":"Mingjian","family":"Zhai","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Houyu","family":"Liang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S23","DOI":"10.1659\/MRD-JOURNAL-D-10-00115.S1","article-title":"Mountain Ecosystem Services: Who Cares?","volume":"32","author":"Brunner","year":"2012","journal-title":"Mt. Res. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1023\/B:NHAZ.0000048468.67886.e5","article-title":"Mountain Protection Forests against Natural Hazards and Risks: New French Developments by Integrating Forests in Risk Zoning","volume":"33","author":"Berger","year":"2004","journal-title":"Nat. Hazards"},{"key":"ref_3","unstructured":"Messerli, B., and Ives, J.D. (1997). Mountains of the World: A Global Priority, Parthenon Publishing."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.isprsjprs.2019.11.018","article-title":"Remote Sensing Algorithms for Estimation of Fractional Vegetation Cover Using Pure Vegetation Index Values: A Review","volume":"14","author":"Gao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Verger, A., Baret, F., and Weiss, M. (2013, January 25\u201327). GEOV2\/VGT: Near real time estimation of global biophysical variables from VEGETATION-P data. Proceedings of the MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Banff, AB, Canada.","DOI":"10.1109\/Multi-Temp.2013.6866023"},{"key":"ref_7","unstructured":"Baret, F., Weiss, M., Verger, A., and Smets, B. (2023, October 01). ATBD for LAI, FAPAR and FCOVER from PROBA-V Products at 300 m Resolution (GEOV3). Available online: http:\/\/www.fp7-imagines.eu\/media\/Documents\/ImagineS_RP2.1_ATBD-LAI300m_I1.73.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1109\/TGRS.2015.2409563","article-title":"Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks from MODIS Surface Reflectance","volume":"53","author":"Jia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tan, L., Wang, G., Sun, X., and Xu, Y. (2022). Study on the Impact of Spatial Resolution on Fractional Vegetation Cover Extraction with Single-Scene and Time-Series Remote Sensing Data. Remote Sens., 14.","DOI":"10.3390\/rs14174165"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/s13351-021-0017-2","article-title":"High Spatial Resolution and High Temporal Frequency (30-m\/15-Day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation","volume":"35","author":"Mu","year":"2021","journal-title":"J. Meteorol. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/JSTARS.2014.2342257","article-title":"Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands","volume":"8","author":"Mu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, D., Jia, K., Wei, X., Xia, M., Zhang, X., Yao, Y., Zhang, X., and Wang, B. (2019). Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products. Remote Sens., 11.","DOI":"10.3390\/rs11212524"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/0341-8162(96)00005-7","article-title":"Influence of Topography on Some Vegetation Cover Properties","volume":"27","author":"Florinsky","year":"1996","journal-title":"CATENA"},{"key":"ref_14","unstructured":"Song, W., Yan, K., Mu, X., and Yan, G. (2016, January 12\u201316). Estimation and Uncertainty Analyses of Fractional Vegetation Cover (FVC) over Mountain Area. Proceedings of the AGU Fall Meeting Abstracts, San Francisco, CA, USA."},{"key":"ref_15","first-page":"20","article-title":"The Effect of Topographic Normalization on Fractional Tree Cover Mapping in Tropical Mountains: An Assessment Based on Seasonal Landsat Time Series","volume":"52","author":"Adhikari","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0034-4257(98)00022-4","article-title":"An Investigation of Terrain Effects on the Inversion of a Forest Reflectance Model","volume":"65","author":"Gemmell","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TGRS.2005.852480","article-title":"SCS+C: A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain. IEEE Trans. Geosci","volume":"43","author":"Soenen","year":"2005","journal-title":"Remote Sens."},{"key":"ref_18","first-page":"551","article-title":"Estimating Surface Reflectance and Albedo from Landsat-5 Thematic Mapper over Rugged Terrain","volume":"58","author":"Duguay","year":"1992","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0034-4257(95)00136-O","article-title":"The Robustness of Canopy Gap Fraction Estimates from Red and Near-Infrared Reflectances: A Comparison of Approaches","volume":"54","author":"Baret","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/BF01245391","article-title":"Area-Averaged Vegetative Cover Fraction Estimated from Satellite Data","volume":"38","author":"Wittich","year":"1995","journal-title":"Int. J. Biometeorol."},{"key":"ref_21","first-page":"1","article-title":"Comparative Study of Fractional Vegetation Cover Estimation Methods Based on Fine Spatial Resolution Images for Three Vegetation Types","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1016\/j.rse.2007.09.007","article-title":"The Impact of Soil Reflectance on the Quantification of the Green Vegetation Fraction from NDVI","volume":"112","author":"Montandon","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, R., Chen, J., Feng, Z., Yang, Y., You, H., and Han, X. (2023). Fitness for Purpose of Several Fractional Vegetation Cover Products on Monitoring Vegetation Cover Dynamic Change\u2014A Case Study of an Alpine Grassland Ecosystem. Remote Sens., 15.","DOI":"10.3390\/rs15051312"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Maurya, A.K., Nadeem, M., Singh, D., Singh, K.P., and Rajput, N.S. (2021, January 11\u201316). Critical Analysis of Machine Learning Approaches for Vegetation Fractional Cover Estimation Using Drone and Sentinel-2 Data. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554422"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100058","DOI":"10.1016\/j.srs.2022.100058","article-title":"Estimation and Validation of 30 m Fractional Vegetation Cover over China through Integrated Use of Landsat 8 and Gaofen 2 Data","volume":"6","author":"Song","year":"2022","journal-title":"Sci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1080\/01431161.2016.1165884","article-title":"Improving Estimates of Fractional Vegetation Cover Based on UAV in Alpine Grassland on the Qinghai\u2013Tibetan Plateau","volume":"37","author":"Chen","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A Survey of Transfer Learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112826","DOI":"10.1016\/j.rse.2021.112826","article-title":"Combining Transfer Learning and Hyperspectral Reflectance Analysis to Assess Leaf Nitrogen Concentration across Different Plant Species Datasets","volume":"269","author":"Wan","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"11132","DOI":"10.1038\/s41598-021-89779-z","article-title":"Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning","volume":"11","author":"Khaki","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yli-Heikkila, M., Wittke, S., Luotamo, M., Puttonen, E., Sulkava, M., Pellikka, P., Heiskanen, J., and Klami, A. (2022). Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network. Remote Sens., 14.","DOI":"10.3390\/rs14174193"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, A.X., Tran, C., Desai, N., Lobell, D., and Ermon, S. (2018, January 20\u201322). Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, San Jose, CA, USA.","DOI":"10.1145\/3209811.3212707"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Astola, H., Seitsonen, L., Halme, E., Molinier, M., and L\u00f6nnqvist, A. (2021). Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest. Remote Sens., 13.","DOI":"10.3390\/rs13122392"},{"key":"ref_35","first-page":"1","article-title":"A Deep Transfer Learning Method for Estimating Fractional Vegetation Cover of Sentinel-2 Multispectral Images","volume":"19","author":"Yu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/01431161.2018.1528017","article-title":"Estimation of Vegetation Fraction Using RGB and Multispectral Images from UAV","volume":"40","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/essd-13-2753-2021","article-title":"GLC_FCS30: Global Land-Cover Product with Fine Classification System at 30 m Using Time-Series Landsat Imagery","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1111\/gcb.14919","article-title":"Vegetation Expansion in the Subnival Hindu Kush Himalaya","volume":"26","author":"Anderson","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT+SAIL Models: A Review of Use for Vegetation Characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0034-4257(92)90065-R","article-title":"A New Forest Light Interaction Model in Support of Forest Monitoring","volume":"42","author":"Rosema","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/0034-4257(95)00253-7","article-title":"Modeling Radiative Transfer in Heterogeneous 3-D Vegetation Canopies","volume":"58","author":"Demarez","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_46","first-page":"1541","article-title":"Distinguishing Vegetation from Soil Background Information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mazza, A., Gargiulo, M., Scarpa, G., and Gaetano, R. (2018, January 22\u201327). Estimating the NDVI from SAR by Convolutional Neural Networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519459"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","article-title":"Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks","volume":"63","author":"Kiranyaz","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_51","first-page":"551","article-title":"Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks. The International Archives of the Photogrammetry","volume":"42","year":"2017","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_53","first-page":"115","article-title":"Learning Precise Timing with LSTM Recurrent Networks","volume":"3","author":"Gers","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0925-2312(01)00706-8","article-title":"Recurrent Neural Networks for Time Series Classification","volume":"50","author":"Stagge","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"126204","DOI":"10.1016\/j.eja.2020.126204","article-title":"Wheat Yield Predictions at a County and Field Scale with Deep Learning, Machine Learning, and Google Earth Engine","volume":"123","author":"Cao","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_56","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable AI for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1080\/17538947.2020.1794064","article-title":"Machine Learning Methods\u2019 Performance in Radiative Transfer Model Inversion to Retrieve Plant Traits from Sentinel-2 Data of a Mixed Mountain Forest","volume":"14","author":"Ali","year":"2021","journal-title":"Int. J. Digit. Earth"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1139\/b90-026","article-title":"Gradient Analysis of Forests of the Sangre de Cristo Range, Colorado","volume":"68","author":"Allen","year":"1990","journal-title":"Can. J. Bot."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1139\/b93-107","article-title":"Gradient Analysis of Old Spruce\u2014Fir Forests of the Great Smoky Mountains circa 1935","volume":"71","author":"Busing","year":"1993","journal-title":"Can. J. Bot."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10106049.2015.1041557","article-title":"Application of Topo-Edaphic Factors and Remotely Sensed Vegetation Indices to Enhance Biomass Estimation in a Heterogeneous Landscape in the Eastern Arc Mountains of Tanzania","volume":"31","author":"Ojoyi","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"118307","DOI":"10.1016\/j.foreco.2020.118307","article-title":"Strong Influences of Stand Age and Topography on Post-Fire Understory Recovery in a Chinese Boreal Forest","volume":"473","author":"Liu","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Shen, B., Ding, L., Ma, L., Li, Z., Pulatov, A., Kulenbekov, Z., Chen, J., Mambetova, S., Hou, L., and Xu, D. (2022). Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge. Remote Sens., 14.","DOI":"10.3390\/rs14174196"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.foreco.2006.06.024","article-title":"Variation in Aboveground Tree Live Biomass in a Central Amazonian Forest: Effects of Soil and Topography","volume":"234","author":"Magnusson","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s11258-008-9406-1","article-title":"Mechanisms Driving Understory Evergreen Herb Distributions across Slope Aspects: As Derived from Landscape Position","volume":"198","author":"Warren","year":"2008","journal-title":"Plant Ecol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s40808-015-0038-x","article-title":"Modeling the Relationship between Elevation, Aspect and Spatial Distribution of Vegetation in the Darab Mountain, Iran Using Remote Sensing Data","volume":"1","author":"Mokarram","year":"2015","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_67","unstructured":"Jin, X., Zhang, Y., Schaepman, M.E., Clevers, J., Su, Z., Cheng, J., Jiang, J., and van Genderen, J. (2008, January 3\u201311). Impact of elevation and aspect on the spatial distribution of vegetation in the Qilian mountain area with remote sensing data. Proceedings of the XXI Congress: Silk Road for Information from Imagery and Remote Sensing (ISPRS 2008), Beijing, China."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1111\/1365-2745.12861","article-title":"Linking Resource Availability and Heterogeneity to Understorey Species Diversity through Succession in Boreal Forest of Canada","volume":"106","author":"Kumar","year":"2018","journal-title":"J. Ecol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1038\/242344a0","article-title":"Competitive Exclusion in Herbaceous Vegetation","volume":"242","author":"Grime","year":"1973","journal-title":"Nature"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1080\/014311698213795","article-title":"Relationships between Percent Vegetation Cover and Vegetation Indices","volume":"19","author":"Purevdorj","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"An, S., Zhang, X., Chen, X., Yan, D., and Henebry, G. (2018). An Exploration of Terrain Effects on Land Surface Phenology across the Qinghai\u2013Tibet Plateau Using Landsat ETM+ and OLI Data. Remote Sens., 10.","DOI":"10.3390\/rs10071069"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ma, X., Lu, L., Ding, J., Zhang, F., and He, B. (2021). Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image. Remote Sens., 13.","DOI":"10.3390\/rs13193874"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4857\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:02:38Z","timestamp":1760130158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,7]]},"references-count":72,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194857"],"URL":"https:\/\/doi.org\/10.3390\/rs15194857","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,7]]}}}