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

Yi Li,Fei Chen,Kai Ao,Mengqi Shi,Bo Zhong,Xiangyuan Ding,Bin Sun,Kunpeng Xu,Xinshuang Wang,Aixia Yang,Qinhuo Liu,Xiaoliang Shi,Junjun Wu

doi:10.3390/rs14051203

Abstract

Accurate identification of sandy land plays an important role in sandy land prevention and control. It is difficult to identify the nature of sandy land due to vegetation covering the soil in the sandy area. Therefore, HJ-2A hyperspectral data and GF-3 Synthetic Aperture Radar (SAR) data were used as the main data sources in this article. The advantages of the spectral characteristics of a hyperspectral image and the penetration characteristics of SAR data were used synthetically to carry out mixed-pixel decomposition in the “horizontal” direction and polarization decomposition in the “vertical” direction. The results showed that in the study area of the Otingdag Sandy Land, in China, the accuracy of sandy land detection based on feature-level fusion and single GF-3 data was verified to be 92% in both cases by field data; the accuracy of sandy land detection based on feature-level fusion was verified to be 88.74% by the data collected from Google high-resolution imagery, which was higher than that based on single HJ-2A (74.17%) and single GF-3 data (88.08%). To further verify the universality of the feature-level fusion method for sandy land detection, Alxa sandy land was also used as a verification area and the accuracy of sandy land detection was verified to be as high as 88.74%. The method proposed in this paper made full use of the horizontal and vertical structural information of remote sensing data. The problem of mixed pixels in sparse-vegetation scenes in the horizontal direction and the problem of vegetation covering sandy soil in the vertical direction were both well solved. Accurate identification of sandy land can be realized effectively, which can provide technical support for sandy land prevention and control.

Highlights

An HJ-2A hyperspectral image was used for mixed-pixel decomposition to detect sandy land horizontally and GF-3 data were used for polarization decomposition to detect sandy land vertically, while the fusion image of HJ-2A and GF-3 data was used to detect sandy land horizontally and vertically
The results of sandy land detection were evaluated for accuracy based on the distribution of vegetation coverage, field data, and sample points based on Google Maps
The results showed that when the exposed sandy land is widely distributed in the study area, the pixel-level fusion based on GS can be used to detect sandy land

Summary

Introduction

In 2015, the United Nations published Transforming Our World: The 2030 Agenda for Sustainable Development Goals (SDGs). Therein, SDG 15 states explicitly to, “By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world”. The expansion of sandy land poses a serious threat to the sustainable development of human life [1–3] and is a global problem related to biodiversity loss, deforestation, and soil degradation [4–6]. Desertification refers to the degradation process of the natural environment in arid, semi-arid, and even sub-humid areas due to the combined effects of Remote Sens.

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Conclusion