Extraction of citrus planting plots based on medium-high different images

【Objective】Using multi-source medium and high score images to explore the remote sensing monitoring method of citrus planting plots in Fuchuan，Guangxi，and provide technical reference for the realization of accurate monitoring of citrus plot information in the whole region.【Method】Based on high-resolution images and two-tone data，the morphological boundary of the patch was updated to generate complete and stable plot data. For crops，orchards，and woodland plot objects，the spectral information，texture information，time series image features of the mid-high score image were integrated，and the support vector machine classifier was used to iteratively identify the citrus information. DEM was calculated based on a true citrus land area.【Result】Analysis of the characteristic curve indicated that the traditional indexes NDVI （January，April，and December），RVI（September to December），LSWI （January，October，and December） and the red edge indexes NDRE1（January to February，October to December），MTCI（January，February，October，and December），PSSRa（September to December），MCARI2（January，September to December）were sensitive to the identification of citrus crops. The accuracy of extracting citrus information in this study was 93.4%，which was higher than the existing citrus crop extraction results and had a finer scale. The citrus plot in the study area accounted for 20.26% of the sloping land area. Under the action of DEM，the citrus planting area was calculated to be 18643.15 ha，which was an increase of 148.49 ha compared with the projection area measurement method. To a certain extent，the influence of terrain on area statistics was eliminated. The extraction results showed that the citrus in Fuchuan County were mainly concentrated in the towns of Mailing，Fuyang，Gepo，Fuli，Chaodong，while the towns of Lianshan，Baisha and Gucheng had less citrus planting.【Suggestion】In order to solve the problem of site conditions in Guangxi，it is recommended to update the plot boundaries with low-frequency and high-resolution images，and comprehensively use multi-star and multi-temporal mid- and high-resolution images（especially images with red edge bands）to extract crop information based on the plot data. For areas with large terrain fluctuations，it is recommended to use high-precision DEM to calculate the crop area.