Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016-2020.

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

Integrated vegetation cover of typical steppe in China based on mixed decomposing derived from high resolution remote sensing data.

Grasslands represent the largest ecosystem in China, accurate and efficient extraction of its integrated vegetation cover (IVC) plays a crucial role in supporting policy decisions. This study presented a method for grassland monitoring via IVC derived from high-resolution satellite data. Taking the multispectral data of Gaofen-1 (GF-1) and Gaofen-6 (GF-6) with 16 m resolution as the main data source, vegetation cover of six representative regions was assessed based on mixed-pixel decomposition model. Using grassland vegetation cover and ratio of grassland area, the IVC in each site was calculated and verified against ground-measured sample data. The results showed that the IVC of grassland was closely related to vegetation habitat driven by regional hydrothermal regime. Yichang grassland, dominated with warm-temperate shrub tussock type, had the highest IVC (80.06 %) due to its favorable hydrothermal conditions. For the main grassland types in Hulunbuir and Gansu Province (temperate meadow steppe and temperate typical steppe), the IVC was 79.38 % and 58.46 %, respectively. In both Xilin-Gol and Nagqu, vegetation cover decreased gradually from east to west, and the IVC was merely 42.83 % and 42.61 %, respectively. Both regions are endowed with less hydrothermal resources to different degrees. Alxa, with a predominately temperate desert landscape, had the lowest IVC of 15.58 % where precipitation is extremely scarce. Based on the grass species of measured samples, the dominant species and biodiversity of different grassland types in Gansu Province and Hulunbuir Municipality of Inner Mongolia Autonomous Region were analyzed. The results showed that the meadow grassland has the richest biodiversity. The temperate mountain meadows in Gansu Province have a high species diversity, with a total of 90 grass species, and the lowland meadows in Hulunbuir have a total of 49 grass species. This study utilizes high-resolution data to conduct large-scale vegetation monitoring, which is a viable alternative for efficient assessment of steppe ecology.

Analysis of Differences in Phenology Extracted from the Enhanced Vegetation Index and the Leaf Area Index.

Remote-sensing phenology detection can compensate for deficiencies in field observations and has the advantage of capturing the continuous expression of phenology on a large scale. However, there is some variability in the results of remote-sensing phenology detection derived from different vegetation parameters in satellite time-series data. Since the enhanced vegetation index (EVI) and the leaf area index (LAI) are the most widely used vegetation parameters for remote-sensing phenology extraction, this paper aims to assess the differences in phenological information extracted from EVI and LAI time series and to explore whether either index performs well for all vegetation types on a large scale. To this end, a GLASS (Global Land Surface Satellite Product)-LAI-based phenology product (GLP) was generated using the same algorithm as the MODIS (Moderate Resolution Imaging Spectroradiometer)-EVI phenology product (MLCD) over China from 2001 to 2012. The two phenology products were compared in China for different vegetation types and evaluated using ground observations. The results show that the ratio of missing data is 8.3% for the GLP, which is less than the 22.8% for the MLCD. The differences between the GLP and the MLCD become stronger as the latitude decreases, which also vary among different vegetation types. The start of the growing season (SOS) of the GLP is earlier than that of the MLCD in most vegetation types, and the end of the growing season (EOS) of the GLP is generally later than that of the MLCD. Based on ground observations, it can be suggested that the GLP performs better than the MLCD in evergreen needleleaved forests and croplands, while the MLCD performs better than the GLP in shrublands and grasslands.

Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors.

The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors' radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors' application, and as such will promote the development of Chinese satellite data.