Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (f_pv) and NPV (f_npv) using multispectral information. The purpose of this study is to evaluate several spectral unmixing approaches for retrieval of f_pv and f_npv in the Otindag Sandy Land using GF-1 wide-field view (WFV) data. To deal with endmember variability, pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, and Automated Monte Carlo Unmixing Analysis, AutoMCU) endmember selection approaches were applied. Observed fractional cover data from 104 field sites were used for comparison. For f_pv, all methods show statistically significant correlations with observed data, among which AutoMCU had the highest performance (R² = 0.49, RMSE = 0.17), followed by MESMA (R² = 0.48, RMSE = 0.21), and SMA (R² = 0.47, RMSE = 0.27). For f_npv, MESMA had the lowest performance (R² = 0.11, RMSE = 0.24) because of coupling effects of the NPV and bare soil endmembers, SMA overestimates f_npv (R² = 0.41, RMSE = 0.20), but is significantly correlated with observed data, and AutoMCU provides the most accurate predictions of f_npv (R² = 0.49, RMSE = 0.09). Thus, the AutoMCU approach is proven to be more effective than SMA and MESMA, and GF-1 WFV data are capable of distinguishing NPV from bare soil in the Otindag Sandy Land.

It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (f_PV and f_NPV) using multispectral satellite sensors because estimations of f_PV and f_NPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10–30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract f_PV and f_NPV through the novel method of spectral-mixture analysis.

Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals (IAEG-SDGs) as key indicators for monitoring SDG 15.3.1. Multiple vegetation parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales. However, there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales. This study proposes a fusion framework that incorporates the trends and consistencies within the four commonly used remote sensing-based vegetation indicators. We analyzed the differences among the four vegetation parameters in different land cover and climate zones, finally producing a new global land productivity dynamics (LPD) product with confidence level degrees. The LPD classes indicated by the four vegetation indicators(VIs) showed that all three levels (low, medium, and high confidence) of increasing area account for 23.99% of the global vegetated area and declining area account for 7.00%. The Increase high-confidence(HC) area accounted for 2.77% of the total area, and the Decline-HC accounted for 0.35% of the total area. This study demonstrates the accuracy of the high-confidence (HC) area for the evaluation of land productivity decline and increase. The “forest” landcover type and “humid” climate zone had the largest increasing and declining area but had the lowest high-confidence proportion. The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target. The “Global Land Productivity Dynamic dataset” is available in the Science Data Bank at http://www.doi.org/10.11922/sciencedb.j00076.00084.

Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development.

Land productivity is one of the sub-indicators for measuring SDG 15.3.1. Land Productivity Dynamics (LPD) is the most popular approach for reporting this indicator at the global scale. A major limitation of existing products of LPD is the coarse spatial resolution caused by remote sensing data input, which cannot meet the requirement of fine-scale land degradation assessment. To resolve this problem, this study developed a tool (HiLPD-GEE) to calculate 30 m LPD by fusing Landsat and MODIS data based on Google Earth Engine (GEE). The tool generates high-quality fused Normalized Difference Vegetation Index (NDVI) dataset for LPD calculation through gap filling and Savitzky–Golay filtering (GF-SG) and then uses the method recommended by the European Commission Joint Research Centre (JRC) to calculate LPD. The tool can calculate 30 m LPD in any spatial range within any time window after 2013, supporting global land degradation monitoring. To demonstrate the applicability of this tool, the LPD product was produced for African Great Green Wall (GGW) countries. The analysis proves that the 30 m LPD product generated by HiLPD-GEE could reflect the land productivity change effectively and reflect more spatial details. The results also provide an important insight for the GGW initiative.

The green vegetation fraction Fg, which represents the horizontal density of live vegetation, is an important parameter for the study of global energy, carbon, hydrological, and biogeochemical cycling. A common method of calculating Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI, , and full vegetation NDVI, . However, many uncertainties exist for the determination of these parameters at large scales. The present study investigates how and determination can impact Fg calculations for all of China, based on different land-cover datasets, hyperspectral data, and soil type classification maps. The results show the following: 1) The regional ChinaCover dataset, with higher accuracy and more detailed classification, is preferable for calculating Fg in China, compared with the most commonly used MOD12Q1 dataset, although it would not lead to too much difference in values. 2) The soil NDVI from Hyperion datasets shows that soils have highly variable NDVI values (0.006–0.2), and 79.36% of the area studied has a much larger NDVI value than the commonly used value of 0.05. Therefore, the dynamic values with different soil types are much better for Fg calculation than the invariant value (0.05), which would yield a significant overestimation of Fg, especially for areas with low vegetation coverage. 3) A high-quality Fg dataset for China from 2000 to 2010 was established with and parameters based on MOD13Q1 250-m NDVI data.