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• W4317738896 abstract "Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features.In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes.In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China." @default.
• W4317738896 created "2023-01-23" @default.
• W4317738896 creator A5024792434 @default.
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• W4317738896 date "2023-01-23" @default.
• W4317738896 modified "2023-09-26" @default.
• W4317738896 title "Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm" @default.
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• W4317738896 cites W1678356000 @default.
• W4317738896 cites W1920908187 @default.
• W4317738896 cites W1964217023 @default.
• W4317738896 cites W1991492083 @default.
• W4317738896 cites W1996061706 @default.
• W4317738896 cites W2000102737 @default.
• W4317738896 cites W2000613913 @default.
• W4317738896 cites W2008801755 @default.
• W4317738896 cites W2012686349 @default.
• W4317738896 cites W2012908927 @default.
• W4317738896 cites W202007184 @default.
• W4317738896 cites W2020789313 @default.
• W4317738896 cites W2029316659 @default.
• W4317738896 cites W2059217921 @default.
• W4317738896 cites W2070493638 @default.
• W4317738896 cites W2072895218 @default.
• W4317738896 cites W2075818603 @default.
• W4317738896 cites W2094899764 @default.
• W4317738896 cites W2101424567 @default.
• W4317738896 cites W2111838090 @default.
• W4317738896 cites W2122400352 @default.
• W4317738896 cites W2125230412 @default.
• W4317738896 cites W2129552470 @default.
• W4317738896 cites W2132424470 @default.
• W4317738896 cites W2132522214 @default.
• W4317738896 cites W2138499468 @default.
• W4317738896 cites W2155622525 @default.
• W4317738896 cites W2159961845 @default.
• W4317738896 cites W2207083369 @default.
• W4317738896 cites W2432085345 @default.
• W4317738896 cites W2475228785 @default.
• W4317738896 cites W2519535984 @default.
• W4317738896 cites W2528491735 @default.
• W4317738896 cites W2593778105 @default.
• W4317738896 cites W2614921098 @default.
• W4317738896 cites W2766438525 @default.
• W4317738896 cites W2781487732 @default.
• W4317738896 cites W2782934949 @default.
• W4317738896 cites W2810045919 @default.
• W4317738896 cites W2899043065 @default.
• W4317738896 cites W2911964244 @default.
• W4317738896 cites W2913229076 @default.
• W4317738896 cites W2919115771 @default.
• W4317738896 cites W2944954104 @default.
• W4317738896 cites W2957974561 @default.
• W4317738896 cites W2998840182 @default.
• W4317738896 cites W3008008384 @default.
• W4317738896 cites W3011189840 @default.
• W4317738896 cites W3013471216 @default.
• W4317738896 cites W3017035390 @default.
• W4317738896 cites W3017278801 @default.
• W4317738896 cites W3024050948 @default.
• W4317738896 cites W3026063078 @default.
• W4317738896 cites W3092153688 @default.
• W4317738896 cites W3093346560 @default.
• W4317738896 cites W3121969053 @default.
• W4317738896 cites W3129657922 @default.
• W4317738896 cites W3131663006 @default.
• W4317738896 cites W3132859298 @default.
• W4317738896 cites W3133703492 @default.
• W4317738896 cites W3168709703 @default.
• W4317738896 cites W3204822487 @default.
• W4317738896 cites W3213426491 @default.
• W4317738896 cites W4200238103 @default.
• W4317738896 cites W4200429143 @default.
• W4317738896 cites W4206160416 @default.
• W4317738896 cites W4207023547 @default.
• W4317738896 cites W4220831458 @default.
• W4317738896 cites W4221015213 @default.
• W4317738896 cites W4224246631 @default.
• W4317738896 cites W4225275451 @default.
• W4317738896 cites W4248268077 @default.
• W4317738896 cites W4281788964 @default.
• W4317738896 cites W4282583475 @default.
• W4317738896 cites W4283643107 @default.
• W4317738896 cites W4283689243 @default.
• W4317738896 cites W4285082704 @default.
• W4317738896 cites W4285494792 @default.
• W4317738896 cites W4287219741 @default.
• W4317738896 cites W4289524775 @default.
• W4317738896 cites W4293093801 @default.
• W4317738896 cites W4293117342 @default.
• W4317738896 doi "https://doi.org/10.1186/s13007-023-00982-7" @default.
• W4317738896 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36691062" @default.
• W4317738896 hasPublicationYear "2023" @default.
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