Spatial-Temporal Evolution and Driving Forces of Drying Trends on the Qinghai-Tibet Plateau Based on Geomorphological Division

Abstract

The Qinghai–Tibet Plateau (QTP) is a sensor of global climate change and regional human activities, and drought monitoring will help to achieve its ecological protection and sustainable development. In order to effectively control the geospatial scale effect, we divided the study area into eight geomorphological sub-regions, and calculated the Temperature-Vegetation Drought Index (TVDI) of each geomorphological sub-region based on MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data, and synthesized the TVDI of the whole region. We employed partial and multiple correlation analyses to identify the relationship between TVDI and temperature and precipitation. The random forest model was further used to study the driving mechanism of TVDI in each geomorphological division. The results of the study were as follows: (1) From 2000 to 2019, the QTP showed a drought trend, with the most significant drought trend in the central region. The spatial pattern of TVDI changes of QTP was consistent with the gradient changes of precipitation and temperature, both showing a gradual trend from southeast to northwest. (2) There was a risk of drought in the four seasons of the QTP, and the seasonal variation of TVDI was significant, which was characterized by being relatively dry in spring and summer and relatively humid in autumn and winter. (3) Drought in the QTP was mainly driven by natural factors, supplemented by human factors. The driving effect of temperature and precipitation factors on TVDI was stable and significant, which mainly determined the spatial distribution and variation of TVDI of the QTP. Geomorphological factors led to regional intensification and local differentiation effects of drought, especially in high mountains, flat slopes, sunny slopes and other places, which had a more significant impact on TVDI. Human activities had local point-like and linear impacts, and grass-land and cultivated land that were closely related to the relatively high impacts on TVDI of human grazing and farming activities. In view of the spatial-temporal patterns of change in TVDI in the study area, it is important to strengthen the monitoring and early warning of changes in natural factors, optimize the spatial distribution of human activities, and scientifically promote ecological protection and restoration.

Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru

“…LST, which is sensitive to surface soil moisture in bare soil or sparsely vegetated conditions, varies depending on the vegetation (chlorophyll), which is reflected in the VIs. The scatter diagrams of LST and VIs are usually triangular [15] or trapezoidal [34] in shape (Figure 4). In the case of the triangle, the lower edge represents the maximum evaporation from the soil, while the upper edge indicates no evaporation at all.…”

“…These conditions are due to being a semi-arid zone with minimal or no rainfall, in addition to the overexploitation of aquifers [33]. In sandy areas, it is necessary to optimise the irrigation system with highly efficient and water-saving agricultural methods [12,15]. Drought affects the state of vegetation, the water cycle and human life [23].…”

“…The advancement of remote sensing technology with thermal and hyperspectral sensors coupled to unmanned aerial vehicles (UAVs) with high spectral resolution has advantages in orchard or plot level monitoring and drought application that traditional remote sensing does not possess. Imagery can be obtained at any time necessary for crop monitoring [15,18]. In future research, a modified TVDI should be developed that integrates a triangular feature space with temperature data from the thermal sensor and hyperspectral imagery captured by UAVs.…”

“…Over the last two decades, several drought indices have been developed in fields such as meteorology, hydrology, agriculture, remote sensing and water resource management [2,[12][13][14]. These include the Palmer Drought Severity Index (PDSI) [15], the Standardised Precipitation Index (SPI) [15,16], and the Standardised Precipitation Evapotranspiration Index (SPEI) [15][16][17], calculated from meteorological data from ground stations. In addition, the Temperature Vegetation Dryness Index (TVDI) [12,18] is based on an empirical parameterisation of the spacing between land surface temperature (LST) and Vegetation Indices (VIs) such as the Normalised Difference Vegetation Index (NDVI) [19], the Enhanced Vegetation Index (EVI2) [20] and the Soil Adjusted Vegetation Index (SAVI) [21], affected by water stress due to the relationship between canopy temperature, soil moisture and transpiration.…”