IKONOS Image-Based Extraction of the Distribution Area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China

Classification of major species in the sericite-Artemisia desert grassland using hyperspectral images and spectral feature identification

Background

The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring.

Methods

Ground hyperspectral images of a sericite-Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of variance was used to extract the main species, namely, Seriphidium transiliense (Poljak), Ceratocarpus arenarius L., Petrosimonia sibirica (Pall), bare land and the spectral characteristic parameters and vegetation indices in different seasons. On this basis, Fisher discriminant analysis was used to divide the samples into a training set and a test set at a ratio of 7:3. The spectral characteristic parameters and vegetation indices were used to identify the three main plants and bare land.

Results

The selection of parameters with significant differences (P < 0.05) between the recognition objects effectively distinguished different land features, and the identification parameters also differed due to differences in growth period and species. The overall accuracy of the recognition model established by the vegetation index decreased in the following order: June (98.87%) > September (91.53%) > April (90.37%). The overall accuracy of the recognition model established by the feature parameters decreased in the following order: September (89.77%) > June (88.48%) > April (85.98%).

Conclusions

The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in sericite-Artemisia desert grassland, providing a basis for further quantitative classification of the species in community images.

Impacts of climate change and human activities on different degraded grassland based on NDVI

Grassland degradation has emerged as a serious socio-economic and ecological problem, endangering both long-term usage and the regional biogeochemical cycle. Climate change and human activities are the two leading factors leading to grassland degradation. However, it is unclear what the degradation level caused by these two factors is. Using the normalized difference vegetation index (NDVI) and coefficient of variation of NDVI (CV_NDVI), the spatial distribution features of grassland degradation or restoration were analyzed in Qilian County in the northeast of the Qinghai–Tibet Plateau. The dominant climate variables affecting NDVI variation were selected through the combination of random forest model and stepwise regression method to improve the residual trend analysis, and on this basis, twelve possible scenarios were established to evaluate the driving factors of different degraded grasslands. Finally, used the Hurst index to forecast the trend of grassland degradation or restoration. The results showed that approximately 55.0% of the grassland had been degraded between 2000 and 2019, and the area of slight degradation (NDVI_slope > 0; CV_{NDVI (slope)} > 0; NDVI_value > 0.2) accounted for 48.6%. These regions were centered in the northwest of Qilian County. Climate and human activities had a joint impact on grassland restoration or degradation. Human activities played a leading role in grassland restoration, while climate change was primarily a driver of grassland degradation. The regions with slight degradation or re-growing (NDVI_slope > 0; CV_{NDVI (slope)} > 0), moderate degradation (NDVI_slope < 0; CV_{NDVI (slope)} > 0), and severe degradation or desertification (NDVI_slope < 0; CV_{NDVI (slope)} < 0) were dominated by the joint effects of climate and anthropogenic activity accounted for 34.3%, 3.3%, and 1.3%, respectively, of the total grassland area. Grasslands in most areas of Qilian County are forecasted to continue to degrade, including the previously degraded areas, with continuous degradation areas accounting for 54.78%. Accurately identifying the driving factors of different degraded grassland and predicting the dynamic change trend of grassland in the future is the key to understand the mechanism of grassland degradation and prevent grassland degradation. The findings offer a reference for accurately identifying the driving forces in grassland degradation, as well as providing a scientific basis for the policy-making of grassland ecological management.

Don't judge toxic weeds on whether they are native but on their ecological effects

The sharp rise in anthropogenic activities and climate change has caused the extensive degradation of grasslands worldwide, jeopardizing ecosystem function, and threatening human well-being. Toxic weeds have been constantly spreading in recent decades; indeed, their occurrence is considered to provide an early sign of land degeneration. Policymakers and scientific researchers often focus on the negative effects of toxic weeds, such as how they inhibit forage growth, kill livestock, and cause economic losses. However, toxic weeds can have several potentially positive ecological impacts on grasslands, such as promoting soil and water conservation, improving nutrient cycling and biodiversity conservation, and protecting pastures from excessive damage by livestock. We reviewed the literature to detail the adaptive mechanisms underlying toxic weeds and to provide new insight into their roles in degraded grassland ecosystems. The findings highlight that the establishment of toxic weeds may provide a self-protective strategy of degenerated pastures that do not require special interventions. Consequently, policymakers, managers, and other personnel responsible for managing grasslands need to take appropriate actions to assess the long-term trade-offs between the development of animal husbandry and the maintenance of ecological services provided by grasslands.

Alpine vegetation phenology dynamic over 16years and its covariation with climate in a semi-arid region of China

Vegetation phenology is a sensitive indicator of ecosystem response to climate change, and plays an important role in the terrestrial biosphere. Improving our understanding of alpine vegetation phenology dynamics and the correlation with climate and grazing is crucial for high mountains in arid areas subject to climatic warming. Using a time series of SPOT Normalized Difference Vegetation Index (NDVI) data from 1998 to 2013, the start of the growing season (SOS), end of the growing season (EOS), growing season length (GSL), and maximum NDVI (MNDVI) were extracted using a threshold-based method for six vegetation groups in the Heihe River headwaters. Spatial and temporal patterns of SOS, EOS, GSL, MNDVI, and correlations with climatic factors and livestock production were analyzed. The MNDVI increased significantly in 58% of the study region, whereas SOS, EOS, and GSL changed significantly in <5% of the region. The MNDVI in five vegetation groups increased significantly by a range from 0.045 to 0.075. No significant correlation between SOS and EOS was observed in any vegetation group. The SOS and GSL were highly correlated with temperature in May and April-May, whereas MNDVI was correlated with temperature in August and July-August. The EOS of different vegetation groups was correlated with different climatic variables. Maximum and minimum temperature, accumulated temperature, and effective accumulated temperature showed stronger correlations with phenological metrics compared with those of mean temperature, and should receive greater attention in phenology modeling in the future. Meat and milk production were significantly correlated with the MNDVI of scrub, steppe, and meadow. Although the MNDVI increased in recent years, ongoing monitoring for rangeland degradation is recommended.