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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 218-229     DOI: 10.6046/zrzyyg.2021112
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
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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.

Keywords GF-2 PMS images      Zanthoxylum bungeanum Maxim      random forest      ecological engineering      planting monitoring     
ZTFLH:  P23  
Corresponding Authors: LIU Jianhong     E-mail:;
Issue Date: 14 March 2022
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Mingxing LIU
Jianhong LIU
Minfei MA
Jingchao ZENG
Cite this article:   
Mingxing LIU,Jianhong LIU,Minfei MA, et al. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
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Fig.1  Location and geographic extent of the study area
参数 相机 波段
全色 多光谱
光谱范围/μm 0.45~0.90 0.45~0.52 蓝光(B1)
0.52~0.59 绿光(B2)
0.63~0.69 红光(B3)
0.77~0.89 近红外(B4)
空间分辨率/m 1 4
幅宽/km 45(2台相机组合)
重访周期/d 5
覆盖周期/d 69
Tab.1  GF-2 PMS satellite sensor specifications
类型 样本数 类型 样本数
纯花椒 318 稀疏草地 325
混合花椒 321 浑浊水体 294
玉米 329 清澈水体 204
树林 168 人工地表 226
茂密草地 453 裸地 262
Tab.2  Field samples collected in the study
Fig.2  Characteristics of ten land cover types on the pan-sharpened GF-2 PMS image
Fig.3  Texture characteristic of land cover types based on different window sizes
Fig.4  Reflectance of land cover types on the pan-sharpened image of GF-2 PMS
Fig.5  Separability of classes based on JM distance
Fig.6  Classification accuracies of S1, S2 and S3 classification schemes
Fig.7  Distribution of ZBM in the study area
Fig.8  Zooming in on random forest classified results
Fig.9  Distributions of ZBM in altitude and slope
Fig.10  Consistency analysis of land cover types results based on the optimal classification scheme (S3)
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