Human impact plays an increasing role on vegetation change even on the Tibetan Plateau, an area that is commonly regarded as an ideal place to study climate change. We evaluate the nature and extent of human impact on vegetation dynamics by the comparison of two areas: the relative highly populated Lhasa area and a nearby less populated Lhari County. Our results indicate that human impact has mainly decreased vegetation greenness within 20 km of the urban area and major constructions during 1999–2013. However, the impact of human activities in a relatively large area is still minor and does not reverse the major trends of vegetation dynamics caused by the warming temperature in recent decades. It seems that the impact of anthropogenic factors on the normalized difference vegetation index (NDVI) trend is more apparent in the Lhasa area than in Lhari County. The major anthropogenic driving factor for vegetation browning in the Lhasa area is livestock number, while the factors, including the number of rural laborers and artificial forest areas, are positively correlated with the annual NDVI increase. The similar relationship between the annual NDVI and driving factors appeared in Lhari County, except for artificial forest areas. The warming temperature and the change in precipitation played dominant roles on vegetation greening in Lhari County than that in the Lhasa area.

Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

Climate change and increasing human activities have induced lake expansion or shrinkage, posing a serious threat to the ecological security on the Inner Mongolian Plateau, China. However, the pattern of lake changes and how it responds to climate change and revegetation have rarely been reported. We investigated the pattern of lake-area changes in the Dalinor National Nature Reserve (DNR) using Landsat imagery during 1976–2015, and examined its relationship with changes in climate and vegetation factors. The total lake-area in the DNR has decreased by 11.6% from 1976 to 2015 with a rate of −0.55 km² year⁻¹. The largest Dalinor Lake reduced the most (by 32.7 km²) with a rate of −0.79 km² year⁻¹. The air temperature has increased significantly since 1976, with a rate of 0.03 °C year⁻¹ (p < 0.05), while the precipitation slightly decreased during 1976–2015, with a rate of −0.86 mm year⁻¹. The Normalized Difference Vegetation Index (NDVI) increased by 27.7% from 1976 to 2015, especially after 2001 when vegetation has been promoted greatly as a result of the successful ecological protection and restoration in the Dalinor basin. The decrease in lake-areas for the DNR exhibited a negative correlation with NDVI (r = −0.397, p < 0.05) during 1976–2015. It seems that decreasing precipitation drives the reduction in lake-area, while rising temperature and vegetation greenness accelerated this decreasing trend by increasing evapotranspiration. The continuous lake shrinkage increases the ecological risks to the habitat of birds, causing a challenge to the management in the DNR.

Ecological land change is an important indicator of eco-environment quality when balancing urbanization and regional ecological safety. Nantong, located in the Yangtze River’s lower reaches, has experienced rapid urbanization since the reform and opening-up policy was implemented in China in 1978. To ensure the regional ecological conservation and restoration of the Yangtze River and the city’s sustainable development, we used remote sensing technology and statistical yearbook data as well as land use dynamic degree (LUDD) and Geodetector methods to determine the spatiotemporal dynamics of ecological land in the Nantong riverine area from 1980 to 2020 and further discussed the potential driving factors. We found that (1) from 1980 to 2020, the major types of ecological land changed from cropland (82.08%), water (17.19%), and grassland (0.69%) to cropland (70.11%), water (26.98%), and forestland (2.25%), and the ecological land area decreased by 4091.36 km² during the same period with a significantly increased dynamic degree of land use. (2) Spatial heterogeneity existed in the distribution and variation of ecological land. Water was the dominant ecological land use in the Yangtze River levee’s inner area, with transitions to cropland and impervious surfaces as the primary conversion types; cropland was the primary land use in the levee’s external area, with transitions from cropland and water to impervious surface as the primary conversion types. In addition, in cities with an early start and a high level of urbanization, most of the ecological land had been converted to impervious surfaces by urban development, whereas cities without those characteristics had retained more of their ecological land. (3) Ecological land change was influenced by a combination of natural and socio-economic factors, and there were enhanced-bi and enhanced-nonlinear interactions between them. (4) The dominant factors influencing ecological land changes during the three stages of urbanization (1980–2000, 2000–2010, and 2010–2020) were the distance to the Yangtze River, the population, and the GDP (Gross Domestic Product) of secondary industry, respectively. The role of environmental policies has gradually increased in recent years, which has played a positive role in ecological land use restoration. The findings of this study can assist policymakers in optimizing land use and restoring ecological space to conserve biodiversity.

Accelerating urbanization leads to the scarcity and fragmentation of greenspaces. Keeping biodiversity alive, i.e., enhancing greenspaces’ impacts on plant diversity in and around urban areas, is essential. This study evaluated greenspace patterns (GSPs) using landscape metrics, and calculated plant α- and β-diversity using field surveys. Bivariate correlation analysis was used to analyze the correlations among plant α- and β-diversity and landscape metrics from 2009 to 2022. Significant models were selected using stepwise regression analysis and verified by comparing fitted and field values. The results indicate that α-diversity was primarily influenced by the number of patches, wetland landscape shape index and patch richness density, imperviousness of surfaces, and forest and grassland at the 100–1000 m scale. The correlation between GSPs and α-diversity weakened with an increase in scale. Current patch richness density, Shannon’s diversity index, Shannon’s evenness index, and percentage of impervious surface and wetland significantly influenced β-diversity at the 100–300 m scale. By contrast, β-diversity was influenced by greenspace patterns at the 300–1000 m scale. There was an observed positive correlation between GSPGSPs and β-diversity that strengthened as the scale increased. These findings highlight the scale-dependent legacy effects of GSPs on plant diversity, primarily driven by the landscape pattern characteristics of urban greenspaces and the diversity of plant groups. Therefore, prioritizing the protection of large green patches and establishing designated protected areas or points for on-site conservation are crucial strategies for urban plant diversity conservation.