Spatio-temporal Variations in Drought with Remote Sensing from the Mongolian Plateau During 1982–2018

The Mongolian Plateau is one of the regions most sensitive to climate change,the more obvious increase of temperature in 21 st century here has been considered as one of the important causes of drought and desertification.It is very important to understand the multi-year variation and occurrence characteristics of drought in the Mongolian Plateau to explore the ecological environment and the response mechanism of surface materials to climate change.This study examines the spatio-temporal variations in drought and its frequency of occurrence in the Mongolian Plateau based on the Advanced Very High Resolution Radiometer(AVHRR)Normalized Difference Vegetation Index(NDVI)(1982–1999)and the Moderate-resolution Imaging Spectroradiometer(MODIS)(2000–2018)datasets;the Temperature Vegetation Dryness Index(TVDI)was used as a drought evaluation index.The results indicate that drought was widespread across the Mongolian Plateau between1982 and 2018,and aridification incremented in the 21 st century.Between 1982 and 2018,an area of 164.38×10^4 km^2/yr suffered from drought,accounting for approximately 55.28%of the total study area.An area of approximately 150.06×10^4 km^2(51.43%)was subject to more than 160 droughts during 259 months of the growing seasons between 1982 and 2018.We observed variable frequencies of drought occurrence depending on land cover/land use types.Drought predominantly occurred in bare land and grassland,both of which accounting for approximately 79.47%of the total study area.These terrains were characterized by low vegetation and scarce precipitation,which led to frequent and extreme drought events.We also noted significant differences between the areal distribution of drought,drought frequency,and degree of drought depending on the seasons.In spring,droughts were widespread,occurred with a high frequency,and were severe;in autumn,they were localized,frequent,and severe;whereas,in summer,droughts were the most widespread and frequent,but less severe.The increase in temperature,decrease in precipitation,continuous depletion of snow cover,and intensification of human activities have resulted in a water deficit.More severe droughts and aridification have affected the distribution and functioning of terrestrial ecosystems,causing changes in the composition and distribution of plants,animals,microorganisms,conversion between carbon sinks and carbon sources,and biodiversity.We conclude that regional drought events have to be accurately monitored,whereas their occurrence mechanisms need further exploration,taking into account nature,climate,society and other influencing factors.

Comprehensive drought monitoring in Yunnan Province, China using multisource remote sensing data

Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index(TRCI), vegetation condition index(VCI), temperature condition index(TCI), and elevation factors. Principal component analysis(PCA) and analytic hierarchy process(AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that:(1) The comprehensive drought index(CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS(China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring.(2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four droughtprone centres.(3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period(61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend(slope > 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province.

Spatial Distribution of Soil Moisture Content and Tree Volume Estimation in International Institute of Tropical Agriculture Forest, Ibadan, Nigeria

The role of soil moisture in the survival and growth of trees cannot be over-emphasized and it contributes to the net productivity of the forest. However, information on the spatial distribution of the soil moisture content regarding the tree volume in forest ecosystems especially in Nigeria is limited. Therefore, this study combined spatial and ground data to determine soil moisture distribution and tree volume in the International Institute of Tropical Agriculture (IITA) forest, Ibadan. Satellite images of 1989, 1999, 2009 and 2019 were obtained and processed using topographic and vegetation-based models to examine the soil moisture status of the forest. Satellite-based soil moisture obtained was validated with ground soil moisture data collected in 2019. Tree growth variables were obtained for tree volume computation using Newton’s formular. Forest soil moisture models employed in this study include Topographic Wetness Index (TWI), Temperature Dryness Vegetation Index (TDVI) and Modified Normalized Difference Wetness Index (MNDWI). Relationships between index-based and ground base Soil Moisture Content (SMC), as well as the correlation between soil moisture and tree volume, were examined. The study revealed strong relationships between tree volume and TDVI, SMC, TWI with R2 values of 0.91, 0.85, and 0.75, respectively. The regression values of 0.89 between in-situ soil data and TWI and 0.83 with TDVI ascertain the reliability of satellite data in soil moisture mapping. The decision of which index to apply between TWI and TDVI, therefore, depends on available data since both proved to be reliable. The TWI surface is considered to be a more suitable soil moisture prediction index, while MNDWI exhibited a weak relationship (R2 = 0.03) with ground data. The strong relationships between soil moisture and tree volume suggest tree volume can be predicted based on available soil moisture content. Any slight undesirable change in soil moisture could lead to severe forest conditions.