Road information plays a fundamental role in many applications. However, at present, it is difficult to extract road information from the traditional nighttime light images in view of their low spatial and spectral resolutions. To fill the gap in high-resolution nighttime light (NTL) data, the Sustainable Development Goals Satellite-1(SDGSAT-1) developed by the Chinese Academy of Sciences (CAS) was successfully launched on 5 November 2021. With 40 m spatial resolution, NTL data acquired by the Glimmer Imager Usual (GIU) sensor on the SDGSAT-1 provide a new data source for road extraction. To evaluate the ability of SDGSAT-1 NTL data to extract road information, we proposed a new road extraction method named Band Operation and Marker-based Watershed Segmentation Algorithm (BO-MWSA). Comparing with support vector machine (SVM) and optimum threshold (OT) algorithms, the results showed that: (1) the F1 scores of the roads in the test area extracted by SVM, OT, and BO-MWSA were all over 70%, indicating that SDGSAT-1/GIU data could be used as a data source for road extraction. (2) The F1 score of road extraction by BO-MWSA is 84.65%, which is 11.02% and 9.43% higher than those of SVM and OT, respectively. In addition, the F1 scores of BO-MWSA road extraction in Beijing and Wuhan are both more than 84%, indicating that BO-MWSA is an effective method for road extraction using NTL imagery. (3) In road extraction experiments for Lhasa, Beijing, and Wuhan, the results showed that the greater the traffic flow was, the lower the accuracy of the extracted roads became. Therefore, BO-MWSA is an effective method for road extraction using SDGSAT-1 NTL data.

Nighttime light (NTL) data have been widely used in the research of urban built-up areas extraction. Due to the low resolution and the overflow characteristics of NTL data, it is difficult to extract urban built-up areas in small cities with undeveloped economy and small areas. How to extract them accurately becomes an important problem. Based on normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized land surface temperature (NLST), we proposed an improved night light index, named vegetation, building, and LST-adjusted NTL urban index (VBTANUI) to accurately extract built-up areas in small cities by the marker-based watershed segmentation algorithm. Taking Jiayuguan, Ezhou, and Wuzhishan, China, as three examples, we extracted their urban built-up areas. Compared with the four traditional NTL indexes, such as vegetation-adjusted NTL urban index (VANUI), enhanced vegetation index (EVI)-adjusted NTL index (EANI), NDBI by bands 6 and 5 of Landsat 8 (BANUI65), and NDBI by bands 4 and 3 of Landsat 8 (BANUI43), the results showed that: 1) the VBTANUI could effectively enhance the light intensity of the built-up boundaries; 2) F1 scores of VBTANUI in three cities are of 89.60%, 95.70%, and 82.75%, respectively, which are 6.04%, 7.12%, 8.10%, and 8.47% higher in average than that of the traditional four indexes; and 3) VBTANUI is feasible and effective in the extraction of built-up areas in small cities with undeveloped economy and small areas.

Fine sediments in debris-flow gullies are quaternary sediments with a particle size of less than 2 mm. Since they are easy to suspend in flowing water, their stability plays a key “probe” role in early debris-flow warning. The permeability coefficient is the main internal control factor of fine sediment stability in debris flow. However, there is no quantitative model between the permeability coefficient and its influencing factors, which seriously affects the quantitative evaluation of debris flow sediments. Taking the debris-flow gullies in Laobeichuan County, Sichuan Province, China as the research area, we carried out experiments on the permeability coefficient and its influencing factors. A model between the permeability coefficient and its influencing factors was established by the least-squares multivariate statistical analysis method. The results showed that cohesion was the closest factor to the permeability coefficient, followed by porosity and density. Each factor passed the t-test and significantly correlated with the model in 99.99% probability. With a correlation coefficient of 0.72, the model had a good prediction ability. Therefore, the model not only provides a theoretical basis for analyzing the stability of fine sediments in Laobeichuan County, but also points out the direction for detecting the fine sediment stability in debris-flow gullies.

Cohesion is the attraction between adjacent particles within the same material, which is the main inter-controlled factor of fine-grained sediment stability, and thus plays an important role in debris flow hazard early warning. However, there is no quantitative model of cohesion and its inter-controlled factors, including effective internal friction angle, permeability coefficient and density. Therefore, establishing a quantitative model of cohesion and its inter-controlled factors is of considerable significance in debris flow hazard early warning. Taking Beichuan county in southwestern China as the study area, we carried out a series of experiments on cohesion and its inter-controlled factors. Using the value of cohesion as the dependent variable and values of normalized density, normalized logarithm of permeability coefficient and normalized effective internal friction angle as the independent variables, we established a quantitative model of cohesion and its inter-controlled factors by the least-squares multivariate statistical method. Fitting of the model showed that its determination coefficient (R²) was 0.61, indicating that the corresponding correlation coefficient (R) was 0.78. Furthermore, t-tests of the model showed that except for the p value of density, which was 0.05, those of other factors were less than 0.01, indicating that cohesion was significantly correlated to its inter-controlled factors, providing a scientific basis for debris flow hazard early warning.

Soil salinization is a widespread and important environmental problem. We propose a high-precision remote sensing identification method for saline-alkaline areas using multi-source data, a method which is of some significance for improving ecological and environmental problems on a global scale which have been caused by soil salinization. Its principle is to identify saline-alkaline areas from remote sensing imagery by a decision tree model combining four spectral indices named NDSI₃₄ (Normalized Difference Spectral Index of Band 3 and Band 4), NDSI₂₅ (Normalized Difference Spectral Index of Band 2 and Band 5), NDSI₂₃₇ (Normalized Difference Spectral Index of Band 3 and Band 4) and NDSI_new (New Normalized Difference Salt Index) that can distinguish saline-alkaline areas from other features. In this method, the complementary information within the multi-source data is used to improve classification accuracy. The main steps of the method include multi-source data acquisition, adaptive feature fusion of multi-source data, feature identification and integrated expression of the saline-alkaline area from multi-source data, fine classification of the saline-alkaline area, and accuracy verification. Taking Minqin County, Gansu Province, China as the study area, we use the method to identify saline-alkaline areas based on GF-2, GF-6/WFV and DEM data. The results show that the overall accuracy of the method is 88.11%, which is 7.69% higher than that of the traditional methods, indicating that it could effectively identify the distribution of saline-alkaline areas, and thus provide a scientific technique for the quick identification of saline-alkaline areas in large regions.

Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical value and practical significance to carry out accurate identification and monitoring of land desertification and its influencing factors in ecologically fragile areas of China. This is conducive to curbing land desertification and ensuring regional ecological security. Minqin County, Gansu Province, located in northwestern China, is one of the most serious areas of land desertification, which is also one of the four sandstorm sources in China. Based on ENVINet5, this paper constructs a high-precision land desertification identification method with an accuracy of 93.71%, which analyzes the trend and reasons of land desertification in this area, provides suggestions for disaster prevention in Minqin County. and provides a reference for other similar areas to make corresponding desertification control policies.