The use of remote sensing to evaluate and detect desert regions

Die Fernerkundung spielt eine signifikante Rolle bei der Bereitstellung von aktuellen Daten zur Schätzung von empirischen Indizes bei Untersuchungen der Umwelt, insbesondere in Trockengebieten. Spektral- und thermische Kanäle in Satellitenbildern werden auch zur Berechnung von Indizes verwendet, um natürliche Phänomene in Trockengebieten – wie etwa Bodendegradation und Desertifikation – aufzuspüren, zu bestimmen und zu evaluieren. In dieser Arbeit wurden zur Identifikation von Desertifikation in der Kashan-Qom Region im Zentraliran fünf Desertifikationsindikatoren verwendet: Vegetation, Oberflächentemperatur, Erosion, Trockenheit und Überflutungen. Diese Indikatoren wurden dargestellt mit Hilfe von: Vegetationsindex (VCI), Temperaturindex (TCI), Revidierte Universelle Bodenverlustgleichung (RUSLE), standardisierter Niederschlagsindex (SPI) und Abfluss. Multispektrale Bilder des MODIS Satelliten wurden für die Berechnung von VCI und TCI herangezogen. Des Weiteren wurden RUSLE, SPI und Abfluss bestimmt. Schließlich wurden mehrere Desertifikationskarten anhand von zwei Modellen – einem konventionellen Modell und einem unscharfen Modell – erstellt. Die Ergebnisse der Modelle wurden mit Hilfe von Feldproben und der Erstellung einer Fehlermatrix analysiert. Im unscharfen Modell wurde ein regelbasiertes System aufgrund von Expertenwissen und einer induktiven datengetriebenen Methode erstellt. Obwohl das unscharfe Modell weniger genau als die konventionelle Methode ist, zeigt es die Unbestimmtheit in den Desertifikationsklassen der erstellten Karten.Remote sensing plays a significant role in providing up-to-date data for the estimating of empirical indices in studying the environment, especially in drylands. The spectral and thermal bands in satellite images are also applied to calculate the indices to detect, identify, and evaluate the natural phenomena in drylands such as land degradation and desertification. In this project, for the identification of desertification in the Kashan-Qom region in Central Iran, five main indicators of desertification are used as follows: vegetation, land surface temperature, erosion, drought, and flooding; therefore, these indices are selected as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), respectively. The multi-spectral satellite images of MODIS are used for the calculation of remotely sensed indices such as Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Furthermore, the ancillary data-based indices, Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), are also estimated. Then several desertification maps are produced in two models: conventional method and fuzzy model. The result of each model is also evaluated, that is, the results are assessed by the supplying of field sampling as ground truth references and the defining of error matrix. In the fuzzy modelling, a rule-based system is built by expert knowledge and data-induction method. According to the obtained results, even though the accuracy of the fuzzy model is lower than the conventional method, the fuzzy model represents the uncertainty in the classes of resulted desertification by providing a map for each class

Redefining the Border line of the Neka River's Watershed with Comparing ASTER, SRTM, Digital Topography DEM, and Topographic Map by GIS and Remote Sensing Techniques

The accurate and precise calculation of the area for land features has a key role in the estimating the change detection of land uses and the classification of geomorphologic units as well as in the evaluating of land use. In particular, the delineation of borders between watersheds is a base in hydrologic analysis. Recent advances in spatial tools of GIS environment and the availability of various remotely-sensed data make the reliable determining of topographical boundaries possible. So an integrated approach of data analysis and modeling can accomplish the task of delineation. The main aim in this research is to evaluate the delineation method of watershed boundary by using four different digital elevation models (DEM) including ASTER, SRTM, Digital Topography, and Topographic maps. In order to determine a true reference of boundary of watershed, sample data were also obtained by field survey and using GPS. The comparison reference points and the results of these data showed the average distance difference between reference boundary and the result of ASTER data was 43 meters. However the average distance between GPS reference and the other data was high; the difference between the reference data and SRTM was 307m, and for Digital Topographic map, it was 269m. The average distance between Topographic map and the GPS points differed 304 meters as well. For the statistical analysis of comparison, the coordinates of 230 points were determined; the paired comparisons were also performed to measure the coefficient of determination, R2, as well as the analysis of variance (ANOVA) in SPSS. As a result, the R2 values for the ASTER data with the Digital Topography and Topographic map were 0.0157 and 0.171, respectively. The results showed that there were statistically significant differences in distances among the four means of the selected models. Therefore, considering other three methods, the ASTER DEM is the most suitable applicable data to delineate the borders of watersheds, especially in rugged terrains. In addition, the calculated flow directions of stream based on ASTER are close to natural tributaries as well as real positions of streams

Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation