Delineation of Cocoa Agroforests Using Multi-Season Sentinel-1 SAR Images versus RapidEye multi-spectral image
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The data is based on field inventory from 2015 to 2017 to map cocoa agroforests and other vegetation and non-vegetation land cover. The dataset consists of geospatial data (shapefiles) of the land cover and land use in the study landscape in Bokito -  within the forest-savannah transition zone of Centre Cameroon. The main aim of the study was to discriminate cocoa agroforest from transition forest; we compare the performance of texture-based classification of SAR (Synthetic Aperture Radar) images of Sentinel-1 with the result of 'business as usual' multi-spectral optical image classification using a RapidEye image mosaic for the study landscape' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Agroforestry","GLCM Textures","Mapping","Perennials","Random Forest Algorithm","RapidEye Multi-spectral Image","Sentinel-1 SAR"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The data is based on field inventory from 2015 to 2017 to map cocoa agroforests and other vegetation and non-vegetation land cover. The dataset consists of geospatial data (shapefiles) of the land cover and land use in the study landscape in Bokito -  within the forest-savannah transition zone of Centre Cameroon. The main aim of the study was to discriminate cocoa agroforest from transition forest; we compare the performance of texture-based classification of SAR (Synthetic Aperture Radar) images of Sentinel-1 with the result of 'business as usual' multi-spectral optical image classification using a RapidEye image mosaic for the study landscape' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The data is based on field inventory from 2015 to 2017 to map cocoa agroforests and other vegetation and non-vegetation land cover. The dataset consists of geospatial data (shapefiles) of the land cover and land use in the study landscape in Bokito -  within the forest-savannah transition zone of Centre Cameroon. The main aim of the study was to discriminate cocoa agroforest from transition forest; we compare the performance of texture-based classification of SAR (Synthetic Aperture Radar) images of Sentinel-1 with the result of 'business as usual' multi-spectral optical image classification using a RapidEye image mosaic for the study landscape
Agroforestry; GLCM Textures; Mapping; Perennials; Random Forest Algorithm; RapidEye Multi-spectral Image; Sentinel-1 SAR