Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

Biologically active areas play an extremely important role in the structure of a city and increasing their coverage, especially in large urban centres, is an activity with a number of advantages. This article compares, in terms of green spaces, two European cities of similar size – Warsaw (517.2 km2) and Oslo (454 km2). Both cities are capitals of their respective countries but implement different spatial policies in the scope of the Green Deal. In Warsaw, many industrial and post-industrial areas still exist and simultaneously urban green areas are decreasing year by year. In Oslo, a strategy based on deindustrialisation of the city and possible maximum use of urban greenery and public spaces is implemented. The research described in this article involved analysing the coverage of the analysed cities and their districts with biologically active area and then checking the correlation with other indicators that can be affected by this coverage. These included data on the incidence of the most common diseases among residents, the attractiveness of living for the elderly and families with children, as well as air and soil pollution and the occurrence of negative effects of climate change. The correlation of urban space use in terms of the presence of industrial land in relation to currently existing green spaces in the districts concerned was subsequently determined.

The difficulties of access and detailed measurements of land surface temperature (LST) and water surface temperature (WST) especially in wetlands made the use of remote sensing data as one of the sources and techniques to estimate many climate elements including surface temperature and surface emissivity (ɛ). This study aims to estimate the surface tempera-ture of the wetland of Lake Oubeira located in northeastern Algeria and their spatiotemporal evolution in both land and wa-ter. Landsat OLI-TIRS images in two dates (April and September 2016) obtained from the USGS have been used in this work, and forms the basis of a series of operations to obtain the final LST: development of the normalized difference vegeta-tion index (NDVI), conversion of the digital number (DN) of the thermal infrared band (TIR) into spectral radiance as well as the calculation of the effective luminosity temperature of the sensor from the spectral radiation and surface emissivity (ɛ). The results show that the LST varies in space and time (from 16 to 31°C in April and from 24 to 41°C in September). This implies that the absorption of the equilibrium temperature at land cover depends on the optical properties of the sur-face, which are essentially determined by its water content, colour and morphology. At the same time, the water surface is the lowest land cover temperature, which also has a spatial variation (from 19 to 25°C in April and from 26 to 34.5°C in September) induced by atmospheric temperature, wind direction and speed and the depth of the lake.

The Lamongan Regency is an area in East Java, Indonesia, which often experiences drought, especially in the south. The Corong River basin is located in the southern part of Lamongan, which supplies the irrigation area of the Gondang Reservoir. Drought monitoring in the Corong River basin is very important to ensure the sustainability of the agricultural regions. This study aims to analyse the causal relationship between meteorological and agricultural drought indices represented by standardised precipitation evapotranspiration index ( SPEI) and standard normalisation difference vegetation index ( NDVI), using time series regression. The correlation between NDVI and SPEI lag 4 has the largest correlation test results between NDVI and SPEI lag, which is 0.41. This suggests that the previous four months of meteorological drought impacted the current agricultural drought. A time series regression model strengthens the results, which show a causal relationship between NDVI and SPEI lag. According to the NDVI–SPEI-1 lag 4 time series model, NDVI was influenced by NDVI in the previous 12 periods, and SPEI-1 in the last four periods had a determinant coefficient value of 0.4. This shows that the causal model between SPEI-1 and NDVI shows a fairly strong relationship for drought management in agricultural areas (irrigated areas) and is considered a reliable and effective tool in determining the severity and duration of drought in the study area.

Potato from the Solanaceae family is one of the most important crops in the world and its cultivation is common in many places. The average yield of this crop is 20 Mg·ha ^–1 and it is compatible with climatic conditions in many parts of the world. The experiment studied the possibility of exogenous regulation of the adaptive potential available for four potato cultivars through the use of growth stimulants with different action mechanisms: 24-epibrassinolide (EBL) and chitosan biopolymer (CHT). The results allowed us to establish significant differences in growth parameters, plant height, leaf index, vegetation index, chlorophyll content, and yield structure. Monitoring growth and predicting yields well before harvest are essential to effectively managing potato productivity. Studies have confirmed the empirical relationship between the normalised difference vegetation index ( NDVI) and N-tester vegetation index data at various stages of potato growth with yield data. Statistical linear regression models were used to develop an empirical relationship between the NDVI and N-tester data and yield at different stages of crop growth. The equations have a maximum determination coefficient (R ²) of 0.63 for the N-tester and 0.74 for the NDVI during the flowering phase (BBCH ¹ 65). NDVI and N-tester vegetation index positively correlated with yield data at all growth stages.