The application of remote sensing, GIS, geostatistics, and ecological modeling in rangelands assessment and improvement
by Seyed Zeynalabedin Hosseini
Date of Examination:2013-08-06
Date of issue:2013-08-19
Advisor:Prof. Dr. Martin Kappas
Referee:Prof. Dr. Martin Kappas
Referee:Prof. Dr. Gerhard Gerold
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Format:PDFDescription:The application of remote sensing, GIS, geostatistics, and ecological modeling in rangelands assessment and improvement, PhD thesis, Seyed Zeynalabedin Hosseini, Department of Cartography, GIS and Remote Sensing, Goettingen University, Germany
EnglishHuman-caused ove rgrazing and drought periods have led to the land degradation which might cause an eventual loss of biodiversity in rangeland ecosystems of Iran. Therefore, assessment of the current condition of rangelands and suggesting efficient strategies for conservation, rehabilitation, improvement, and consequently sustainable management of rangelands are essential. To reach the mentiond purposes, creating the environmental variable (e.g. topography, climate, and soil) maps, monitoring vegetation dynamics, and determining the relations between the vegetation and environmental variables are the firs steps. This research was conducted in rangelands of Poshtkouh area of the Yazd province in central Iran. The main aims were assessment of the current condition and suggesting efficient strategies for conservation, rehabilitation, improvement, and consequently sustainable management of the rangelands. In addition, evaluating the capability of remote sensing, GIS, geostatistics, and ecological modeling in rangeland assessment and improvement. In the first step, available data such as topography, geology, and vegetation type maps as well as satellite images were collected and then soil and vegetation samples were taken in the study area. As the first part of the data analyses, three geostatistical methods were applied for soil mapping and the satellite and environmental data were considered as ancillary data. In the next stage, the relationship between precipitation variation and vegetation dynamic was determined using NOAA AVHRR NDVI and climatic maps, as well as the effect of environmental factors on the strength of the relations between the precipitation and NDVI was determined. Then, vegetation cover percentage of the study area was created and the best time interval of the satellite images for vegetation studies was determined. In the last part of the data analyses, using the Maxent model, habitat distribution of A. sieberi and A. aucheri species were assessed and mapped. In addition, the most effective environmental variables on these habitats were determined. The results have shown that, taking the ancillary data (satellite images and environmental variables) into account in geostatistical estimations (cokriging and regression kriging methods) has increased the accuracy of the created maps. Selecting the suitable time interval of satellite images to study the vegetation during its growth period has prominent effect on the results. The best satellite data to study the vegetation cover in the arid rangelands of the study area can be taken from the images recorded in the month May. NDVI derived from NOAA AVHRR satellite images is a prominent tool for monitoring the effect of precipitation variation on vegetation dynamic. The strength of the relationship between the precipitation and NDVI depends on species’ composition, and some environmental variables like soil available moisture. Successful modeling of A. sieberi and A. aucheri has proven that Maxent is a powerful model for species distributions mapping. Furtheremore, this model can efficiently find the environmental variables correlation with the geographic distribution of species. Moreover, the results of this research have demonstrated that using the soil data in addition to the climatic and topographic data can improve the predictive capability for habitat distribution mapping of plant species using the Maxent model. Finally, it can be concluded that remote sensing, GIS, geostatistics, and ecological modeling are the efficient tools for rangelands assessment and sustainable management. Furthoremore, as the overgrazing and climate change are the main threats of Iran’s rangelands, monitoring the relations of soil, topography, and climate with vegetation as well as the impact of climate change on rangelands represents basic information for finding the proper strategies of rangeland improvement. Moreover, implementing conservation plans together with planting the suitable endemic species based on the results of the ecological modeling would be of tremendous value in rangeland rehabilitation.
Keywords: Remote sensing, GIS, geostatistics, ecological modeling, rangeland assessment and improvement, environmental variables, soil mapping, precipitation-vegetation relations, habitat distribution.