Grassland degradation is a complex process and cannot be thoroughly measured by a single indicator, such as fractional vegetation cover (FVC), aboveground biomass (AGB), or net primary production (NPP), or by a simple combination of these indicators. In this research, we combined measured data with vegetation and soil characteristics to establish a set of standards applicable to the monitoring of regional grassland degradation by remote sensing. We selected indicators and set their thresholds with full consideration given to vegetation structure and function. We optimized the indicator simulation, based on which grassland degradation in the study area during 2014–2018 was comprehensively evaluated. We used the feeding intensity of herbivores to represent the grazing intensity. We analyzed the effects of climate and grazing activities on grassland degradation using the constraint line method. The results showed degradation in approximately 69% of the grassland in the study area and an overall continued recovery of the degraded grassland from 2014 to 2018. We did not identify any significant correlation between temperature and grassland degradation. The increase in precipitation promoted the recovery of degraded grassland, whereas increased grazing may have aggravated degradation. Our findings can not only improve the scientific quality and accuracy of grassland degradation monitoring by remote sensing but also provide clear spatial information and decision-making help in sustainable management of grassland regions.

Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase.

Grassland degradation seriously threatens the ability of grassland to provide ecosystem services. Grassland ecological restoration and sustainable management decision making depends on an accurate understanding of the impacts of grassland degradation on ecosystem services. Based on the assessment of grassland degradation and four key ecosystem services, including the net primary production (NPP), ecosystem carbon pool (EC), soil conservation (SC), and soil loss by wind (SL), the impacts of grassland degradation on ecosystem services and their relationships were analyzed. The impacts of climate change and grazing pressure on the relationship between grassland degradation and ecosystem services were revealed. Based on the “climate change and grazing pressure-grassland degradation-ecosystem services” network, the study puts forward specific suggestions on grassland ecological restoration and sustainable management under the premise of fully balancing ecological restoration and stakeholder relationships. The results showed that grassland degradation had a significant impact on ecosystem services and their relationships, but it varied with the types of ecosystem services. Although the degraded grassland in the study area has been in a state of recovery and ecosystem services have been improving in the past 20 years, the degradation of grassland in some areas has intensified, and there are still ecological risks, so it is necessary to continue to carry out ecological restoration work. On this basis, taking the local conditions into consideration, grassland ecological restoration and sustainable management policy suggestions were proposed. The study can provide a scientific reference for ecological protection and sustainable development in arid and semi-arid areas, and help to improve human well-being.

This paper focuses on the problems of inaccurate extraction of winter wheat edges from high-resolution images, misclassification and omission due to intraclass differences as well as the large number of network parameters and long training time of existing classical semantic segmentation models. This paper proposes a lightweight winter wheat planting area extraction model that combines the DeepLabv3+ model and a dual-attention mechanism. The model uses the lightweight network MobileNetv2 to replace the backbone network Xception of DeepLabv3+ to reduce the number of parameters and improve the training speed. It also introduces the lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism to extract winter wheat feature information more accurately and efficiently. Finally, the model is used to complete the dataset creation, model training, winter wheat plantation extraction, and accuracy evaluation. The results show that the improved lightweight DeepLabv3+ model in this paper has high reliability in the recognition extraction of winter wheat, and its recognition results of OA, mPA, and mIoU reach 95.28%, 94.40%, and 89.79%, respectively, which are 1.52%, 1.51%, and 2.99% higher than those for the original DeepLabv3+ model. Meanwhile, the model’s recognition accuracy was much higher than that of the three classical semantic segmentation models of UNet, ResUNet and PSPNet. The improved lightweight DeepLabv3+ also has far fewer model parameters and training time than the other four models. The model has been tested in other regions, and the results show that it has good generalization ability. The model in general ensures the extraction accuracy while significantly reducing the number of parameters and satisfying the timeliness, which can achieve the fast and accurate extraction of winter wheat planting sites and has good application prospects.

Accurate mapping of crop distribution on Earth's surface aids in predicting grain production. Pattern classification along with remote sensing imagery can facilitate traditional manual field measurement techniques using machine learning. With the rapid increase in satellite sensor resolution, the object-based classification paradigm has increasingly been applied. However, scale parameter selection is always the difficult part of object-based classification. Based on ensemble learning, this study proposes a classification method using the multiscale object-based weighted method which includes manual digitizing of crop distribution in the southern region of Jishan County, Shanxi Province, China, applying Gaofen-2 (GF-2) images. This method initially uses estimations of the scale parameter (ESP) tool to select “good” scales, defined here as “preferred” scales, after which feature subsets are screened by each preferred scale as the input of multiple classifiers and classifies. Finally, all classification results are then fused. Our research results indicate that: 1) Feature importance values are sorted differently at different preferred scales; 2) accuracy differences become clear when different preferred scales are combined with different classifiers, and determining the “best” single appropriate scale is generally difficult; 3) accuracy of the multiscale weighted classification method is higher compared to the single preferred scale approach. Furthermore, ensemble learning can be achieved using this method on multiple scales and on multiple classifiers. With this method, procedures that necessitate the selection of segmentation scales and the selection and optimization of classifiers can be skipped altogether.