Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax (the maximum NDVI during the growing season) for desert vegetation in Hexi region increased at the rate of 0.65×10 -3 /a (P<0.05) from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin (an extremely arid area), indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.
It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (fPV and fNPV) using multispectral satellite sensors because estimations of fPV and fNPV 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 fPV and fNPV through the novel method of spectral-mixture analysis.
Desert plants survive harsh environment using a variety of drought-resistant structural modifications and physio-ecological systems. Rolled-leaf plants roll up their leaves during periods of drought, making it difficult to distinguish between the external structures of various types of plants, it is therefore necessary to carry out spectral characteristics analysis for species identification of these rolled-leaf plants. Based on hyper-spectral data measured in the field, we analyzed the spectral characteristics of seven types of typical temperate zone rolled-leaf desert plants in the Hexi Corridor, China using a variety of mathematical transformation methods. The results show that: (1) during the vigorous growth period in July and August, the locations of the red valleys, green peaks, and three-edge parameters, namely, the red edge, the blue edge, and the yellow edge of well-developed rolled-leaf desert plants are essentially consistent with those of the majority of terrestrial vegetation types; (2) the absorption regions of liquid water, i.e., 1400-1500 and 1600-1700 nm, are the optimal bands for distinguishing various types of rolled-leaf desert plants; (3) in the leaf reflectance regions of 700-1250 nm, which is controlled by cellular structure, it is difficult to select the characteristic bands for differentiation rolled-leaf desert vegetation; and (4) after processing the spectral reflectance curves using a first-order differential, the envelope removal method, and the normalized differential ratio, we identify the other characteristic bands and parameters that can be used for identifying various types of temperate zone rolled-leaf desert plants, i.e., the 510-560, 650-700 and 1330-1380 nm regions, and the red edge amplitude. In general, the mathematical transformation methods in the study are effective tools to capture useful spectral information for species identification of rolled-leaf plants in the Hexi Corridor.
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