Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Abstract

Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.

Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

“…In the future, more attention should be paid on the mixture pixel problem by using the linear spectral unmixing method to further improve the performance of the rocky desertification mapping. With the rapid development of high resolution remote sensing, a high resolution optical and SAR image can be integrated in the future research, and more state-of-the-art methods, such as deep learning algorithm, can be used to further improve the spatial resolution and accuracy of the rocky desertification mapping [69,70]. In addition, the future study will explore the temporal and spatial pattern change of rocky desertification from the aspects of transferring area and transferring rate [15].…”

“…In addition, the future study will explore the temporal and spatial pattern change of rocky desertification from the aspects of transferring area and transferring rate [15]. The driving-force analysis of the rocky desertification level will also be conducted, so as to provide scientific decision support for the making of ecological protection planning [69][70][71][72].…”

A stratified environmental reference system for better understanding of the relationship between remote sensing observations and ground monitoring of karst rocky desertification

“…The relative coverage of vegetation, soil, and exposed bedrock using remote sensing is the basis for KRD classification (Xu et al, 2015). Visually interpreting remote sensing images has become an important way of mapping KRD (Bai et al, 2013); however, the involved techniques are time‐consuming and laborious and automatic and semi‐automatic mapping techniques are being increasingly used (Pu et al, 2021). Mapping methods such as supervised classification methods, machine learning algorithms, dimidiate pixel methods, and spectral hybrid decomposition, have been applied at a regional scale (Huang & Cai, 2009; Li & Wu, 2015; Qi et al, 2019; Xu et al, 2015).…”

Methods of Sandy Land Detection in a Sparse-Vegetation Scene Based on the Fusion of HJ-2A Hyperspectral and GF-3 SAR Data

“…The characteristics of shifting sandy land are obvious and easy to identify, but the influence of vegetation cover makes it more difficult to identify the sandy soil in fixed sandy land and semi-fixed sandy land. Remote sensing technology has provided a more objective and accurate data basis for the monitoring and evaluation of sandy land due to its wide observation range, the fact that it provides real-time information, and its dynamics [18][19][20][21], and it has become one of the indispensable methods of monitoring sandy land on a regional and even global scale [22]. A variety of remote sensing methods of monitoring sandy land have been proposed by different scholars.…”