Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain,UAE, 1992-2030

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM⁺). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.     

Generation of Vegetation Fraction and Surface Albedo Products Over India from Ocean Colour Monitor (OCM) Data Onboard Oceansat-2

The use of Local Area Coverage (LAC) data from Ocean Color Monitor (OCM) sensor of Oceansat-2 with its high radiometric resolution (12 bits/pixel) and 2-day repeat cycle for rapid monitoring of vegetation growth and estimating surface albedo for the Indian region is demonstrated in this study. For the vegetation monitoring, normalized difference vegetation index (NDVI) and vegetation fraction (VF) products were estimated by maximum value composite approach fortnightly and were resampled to 1 km. The surface albedo products were realized by converting narrow-band eight-band spectral reflectance OCM data to a) visible (300–700 nm) and b) broad band (300–3,000 nm) data. For validation, the derived products were compared with respective MODIS global products and found to be in good agreement.     

Monitoring of salt affected soils — A case study using aerial photographs,salyut‐7 space photographs,and landsat TM data

Soils of part of Ukai‐kakarapar Command area, Gujarat (India) have been mapped at 1:25, 000 scale using aerial photographs of December 1977. It was observed that about 36.3% of the area was affected by soil salinity/alkalinity. The test area has been remapped using Salyut‐7 space photographs taken during Indo‐Soviet joint flight in April, 1984. The area affected by soil salinity/alkalinity was found to be substantially higher (80.3%). The earlier mapping using aerial photographs was done when the soil surface was compartively moist (December 1977) as compared to date of Salyut‐7 photography (April 1984), when the soil surface was likely to be devoid of moisture and the salts moved to the surface. To have easy comparision with the map prepared by using aerial photographs, Landsat TM data of December, 1985 was used in which 45.7% of the total area was mapped as salt affected. The extent of area delineated using Landsat TM was higher than that of 1977 but much lesser than the area delineated using Salyut‐7 (MKF‐6M) photographs. This indicated that the increase in the extent of salt affected area in the map prepared using the MKF‐6M photographs might be partly due to actual increase in the salinity/alkalinity and partly due to the seasonal affects. Among the various bands of MKF‐6M, band ‐4 was found to be the best for delineating the salt affected soils. The boundaries were sharper in the FCC and band No.4 of MKF‐6M than in the aerial photographs.     

Earth observations during space shuttle flight STS‐41: Discovery's mission to planet earth

Abstract

Multi‐temporal ERS‐1 SAR data acquired over a large agricultural region in West Bengal was used to classify kharif crops like rice, jute and sugarcane. Rice crop grown under lowland management practice showed a temporal characteristic. The dynamic range of backscatter was highest for this crop in temporal SAR data. This was used to classify rice using temporal SAR data. Such temporal character was not observed for the other study crops, which may be due to the difference in cultivation practice and crop calendar. Significant increase in backscatter from the ploughed fields was used to derive information on onset and duration of land preparations. Synergistic use of optical remote sensing data and SAR data increased the separability of rice crop from homesteads and permanent vegetation classes.     

GEOLOGIC INTERPRETATION OF ANTARCTICA'S MOUNTAINOUS REGIONS WITH SPACE IMAGERY

This paper demonstrates how ice-surface relief identified on space imagery can be used in mapping selected elements of the geologic structure of the Antarctic Peninsula. The mapping procedure is based on the fact that all major subglacial relief forms appear, albeit in subdued form, on the surface of slow moving and relatively thin ice sheets. Since subglacial bedrock relief reflects geologic structure, particular surface configurations of ice identified on space imagery can be used as indicators of major structural elements. Examples of the procedure, including imagery of ice relief and corresponding cartographic representations of underlying structure are provided for faults and ring structures. Translated from: Antarktika, AN SSSR Mezhduvedomstvennaya komissiya po izucheniyu Antarktiki, Doklady komissii [USSR Academy of Sciences Joint Commission on Antarctic Research, Commission Report], No. 24. Moscow, 1985, pp. 50-55. An article demonstrating the application of these methods in the compilation of a geologic map for a part of the Antarctic Peninsula will appear in a later issue.     

MAPPING ANIMAL HABITATS

The authors propose a series of general methodological guidelines for animal habitat mapping. These include the use of “summary” indicators (of aggregate effects of environmental conditions on animal populations) as indices for mapping, the combination of laboratory and field work at all but the smallest scales of mapping, and explicit recognition (based on extensive studies of ecological characteristics and life cycles of species being mapped) that habitat boundaries in many cases vary dramatically from vegetation and landscape-geological boundaries. Habitat maps of two study areas are compared with vegetation and landscape maps at identical scales to demonstrate these differences. Translated from: Vestnik Moskovskogo Universiteta, geografiya, 1985, No. 3, pp. 95-101.     

EXPERIENCE IN COMPILING LARGE-SCALE MAPS OF EROSION-PRONE AREAS

Specific criteria used in the compilation of large-scale maps of erosion-prone areas are defined and evaluated for tracts of land representing a number of different agricultural cover types. Formulas are derived which facilitate the mapping of such areas based on such readily observable morphometric characteristics as slope length and steepness. Translated by Edward Torrey, Alexandria, VA 22308 from: Vestnik Moskovskogo Universiteta, geografiya, 1988, No. 5, pp. 25-31.     

Remote classification of Cerrado (Savanna) and agricultural land covers in northeastern Brazil

Remote classification of land-use/land-cover (LULC) types in Brazil's Cerrado ecoregion is necessary because knowledge of Cerrado LULC is incomplete, sources of inaccuracy are unknown, and high-resolution data are required for the validation of moderate-resolution LULC maps. The aim of this research is to discriminate between Cerrado and agriculture using high-resolution Landsat 7 ETM⁺ imagery for the western region of Bahia state in northeastern Brazil. The Maximum Likelihood Classification (MLC) and Spectral Angle Mapper (SAM) algorithms were applied to a ～3000 km² subset, yielding comparable classification accuracies. The panchromatic band was reserved for validation. User's and producer's accuracies were highest for non-irrigated agriculture (～94%) but lower for Cerrado Lato Sensu (89%). Classification errors likely resulted from spatial and spectral characteristics of particular classes (e.g. riparian forest and burned) and overestimation of other classes (e.g. Eucalyptus and water). Manual misinterpretation of validation data may have also led to lower reported classification accuracies.