Grassland Recognition with the Usage of Thermal Weights

In this paper we apply the usage of thermal weights, a new variable for geostatistical analysis and we present the method for their determination. In the case study we tested a data fusion between Sentinel-2 and Landsat 7/8 data, to incorporate also the thermal factor in the detection of land cover changes. The process distinguishes grasslands from other crops with similar vegetative appearance and offers us the possibility to create a new statistical sample with just grasslands. The data fusion is incorporated in the calculation of Land Surface Temperature (LSTFU) by combining the Sentinel-2 derived Normalized Difference Vegetation Index (NDVI), and from it derived land surface emissivity, with the Landsat 7/8 derived Top of Atmosphere Brightness Temperature (TOABT). The experimental LSTFU is modified into a normalized assessment variable by a time-series analysis. The result is a thermal weight layer which can help us in further object-based image analyses and classification. The thermal weight is calculated from Sentinel-2 and Landsat 7/8 datasets that has small acquisition time gaps between them. The accuracy assessment due to time gaps and sensor differences was evaluated with Cohens’s kappa (κ) and correlation matrix validation. The data fusion is made to test if a Sentinel-2 fusion approach could improve the Thermal Weight created just by Landsat imagery. The purpose was to evaluate the importance of thermal bands for LU/LC cover

Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa.

Wetlands in drylands have high inter- and intra-annual ecohydrological variations that are driven to a great extent by climate variability and anthropogenic influences. The Ramsar Convention on Wetlands encourages the development of frameworks for national action and international cooperation for ensuring conservation and wise use of wetlands and their resources at local, national and regional scales. However, the implementation of these frameworks remains a challenge. This is mainly due to limited availability of high-resolution data and suitable big data processing techniques for assessing and monitoring wetland ecohydrological dynamics at large spatial scales, particularly in the sub-Saharan African region. The availability of cloud computing platforms such as Google Earth Engine (GEE) offers unique big data handling and processing opportunities to address some of these challenges. In this study, we applied the GEE cloud computing platform to monitor the long-term ecohydrological dynamics of a seasonally flooded part of the Nylsvley floodplain wetland complex in north-eastern South Africa over a 20-year period (2000–2020)

Evaluating the effect of soil moisture, surface temperature, and humidity variations on MODIS-derived NDVI values

The capability to observe soil moisture frequently and over large regions could significantly enhance our ability to monitor vegetation conditions over time and space. The purpose of this project is to evaluate the effects of soil moisture, temperature, and humidity variations on vegetation conditions in the UAE. Visible and near-infrared channels of MODIS instrument on board of aqua satellite were used in this study. The Normalized Difference Vegetation Index (NDVI) was applied to map the extent of vegetation coverage. It was found in this study that the vegetation areas with NDVI values between 0 and 0.2 have significant correlation with average soil moisture, minimum humidity and maximum temperature and the humidity has the maximum effect on these vegetated areas. However, much lower correlation was found with high NDVI areas