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Farmers utilize pesticides extensively on their farms to control weeds and insects, as well as increase crop productivity. Despite these advantages, their excessive use poses a serious threat, particularly to the population living at the nexus of urban and rural areas. Exposure to pesticide drift can be investigated using geospatial tools. Remote sensing technology and Geographic Information Systems (GIS) techniques have been used intensively and constitute trusted tools in different sectors, especially in agriculture. Remote sensing depends on processing the electromagnetic radiation reflected and emitted from the ground target and can be used to identify the main units of Land Use Land Cover (LULC), in addition to measuring crop areas exposed to pesticides. GIS has powerful tools for building a spatial geo-database of pesticide exposure drift. Therefore, the major objective of the research was to explore the effectiveness of using remote sensing and GIS techniques to estimate the exposure organophosphate pesticides drift over Macon County, Alabama. To achieve this objective, the Cropland Data Layer (CDL) dataset, the available pesticide usage data, and gridded population data were used to estimate the potential pesticide drift on the Macon County level. In addition, the AgDRIFT model was used to estimate the potential drift of pesticides from their intended targets at the field level. The results indicated that 6.6% of Macon County's residents are considered potentially severely exposed, and the potentially affected population resides primarily in rural areas. In comparison, 23% of residents of the urban-rural interface are considered to have potentially medium to high exposure. In addition, 38% of residents living in suburban areas are considered to have potentially low-to-medium exposure. The results indicated that both GIS and remote sensing could play an effective role in estimating pesticide exposure drift at the State or County level. In addition, the AgDRIFT model was more appropriate for estimating pesticide drift at the field level. [ABSTRACT FROM AUTHOR]

Limited to fourth percent or less of the country’s total land area, Egypt’s agricultural landscape is threatened by the repercussions of climate change, desertification, soil depletion, and looming water scarcity. Outside of the Nile river valley and scattered fertile pockets in the desert oases, the vast majority of land is desert: rocky, parched and unable to support conventional farming. According to Egyptian National Action Program 2005 (ENAP), Egypt covers an area of about one million km2 ~ 100 million hectares, out of which about of 76.5 thousands km2 ~ 7.6% of the total area are inhabited, and the remaining (92.4%) area is desert. Desertification is a very complex process governed by several variables which influence each other. It is thus not possible to conclude for the general picture from a single factor alone. This process has a high rate in arid and hyper-arid countries such as Egypt. The main objective of this research was to evaluation the present-day climate-induced desertification in El-Dakhla Oasis, so in this study, the newest method for evaluating and mapping of desertification was used. The mathematic method was carried out by European Commission (EC), (MEditerranean Desertification And Land Use) at the MEDALUS project and booked as ESAs in 1999 integrated with remote sensing and GIS. All indices of the model were revised before using, and regarding to the region condition these indices were defined as key indices which were: Temperature, precipitation, wind, albedo, ground water and soil benchmark, and each benchmark has some sub-layers getting from their geometric mean. Based on the MEDALUS model, each sub-benchmark was quantified according to its quality and given a weighting of between 1.0 and 2.0. All benchmarks should be reinvestigated and adjusted to local conditions. Ultimately, desertification severity was classified in four level including low, moderate, Severe and high Severe. ArcGIS 10 was used to analysis and prepares the layers of quality maps using the geometric mean to integrate the individual sub-indicator maps. In turn the geometric mean of six quality maps was used to generate a single desertification status map. Remote sensing data have great potential to improve models mapping spatial variability of temperature and precipitation since they are available as time worldwide, and have high spatial resolution. The HYDRA visualization software was used to measure the present surface albedo from MODIS product (MOD43C1). Results showed that 60% of the area is classified as Severe, 14 % as moderate and 12%, 16% as low and none affected by desertification respectively. In addition the climatic variations including rainfall, temperature, sunlight, wind indicators were the most important factors affecting desertification process in El-Dakhla Oasis.