Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province
LIU Mingxing1,2(), LIU Jianhong1,2(), MA Minfei1,2, JIANG Ya1, ZENG Jingchao1,2
1. College of Urban and Environmental Science, Northwest University, Xi’an 710127, China 2. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
Major crops tend to receive far more attention in current remote sensing (RS) monitoring of vegetation than minor tree species with ecological and economic benefits. Zanthoxylum bungeanum Maxim (ZBM) is an important but niche ecological tree, and its fruits are common oil and medicinal materials. It is vital for the sustainable development of local economy, ecology, and society to obtain accurate information of planting area and spatial distribution ZBM in time. Using the GF-2 PMS images and the random forest algorithm, this study discussed the feasibility of RS monitoring of ZBM planting. Three classification schemes were designed using four classification features, namely spectral bands, normalized difference vegetation index (NDVI), textural features, and digital elevation model (DEM). Furthermore, this study explored the role of different classification features in identifying ZBM by analyzing the classification accuracy of the schemes. Results show that it is difficult to obtain satisfactory classification accuracy when only spectral band characteristics were used (overall accuracy: 65.90%). Combining NDVI and DEM with the spectral band characteristics can slightly improve the classification effect (overall accuracy: 67.67%). After textural features were further combined, the overall accuracy was greatly increased (74.43%). This indicates that textural features play an important role in monitoring ZBM planting. As revealed by the results of the optimal classification scheme, ZBM in Linxia, Gansu Province is mainly distributed along the Yellow River and around the Liujiaxia Reservoir, with a total area of 231.59 km2, which accounts for 22.56% of the total area of the study area. The area of ZBM planted in the patterns of single cropping and mixed cropping is 189.06 km2 and 42.53 km2, respectively. More than 90% of ZBM grows at an elevation of [1 683, 2 300) m and its number tends to decrease, increase, and decrease successively with an increase in the elevation. Moreover, 58% of ZBM are planted in regions with a slope of [8, 25)°. Overall, GF-2 PMS images have great potential in monitoring ZBM planting. The development of RS-based identification methods of ZBM will assist in the regulation of the local ecological industry and the layout of subsequent ecological engineering. Furthermore, it will provide a strong reference for the remote sensing monitoring of ecological tree species or a minority of vegetation species in other regions.
柳明星, 刘建红, 马敏飞, 蒋娅, 曾靖超. 基于GF-2 PMS影像和随机森林的甘肃临夏花椒树种植监测[J]. 自然资源遥感, 2022, 34(1): 218-229.
LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
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2022, Vol. 34 
), 刘建红1,2(
