GIS and Remote Sensing

Volume XIX |

Estimation of changes in land surface temperature using multi-temporal Landsat data of Ghaziabad District, India

Abstract: The rapid growth in urban population in India is seen to create an essential for the development of more urban infrastructures. Land surface temperature (LST) is a significant factor in many areas like climate change, urban land use/land cover (LULC), heat balance studies and a key input for climate models. The main objective of this paper is to examine multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Gaziabad district in Uttar Pradesh, India using LANDSAT satellite data in GIS platform. To compute the changes and relationship between Land Surface Temperature (LST) and Land Use Land Cover (LULC), Landsat LST data for the months of September of year 2000, 2011 and 2018 were used in this study. The LST has been estimated with respect to Normalized Difference Vegetation Index (NDVI) values determined from the Red and Near Infrared bands. The Land Surface Emissivity (LSE) is retrieved directly from the Thermal Infrared bands. The present study focuses on ArcGIS Raster functions and Raster calculation using the LANDSAT in September, thermal Bands (10, 11 & 6). The output of this paper shows that the surface temperature was high in the barren and built up area whereas it is comparatively low in the thick vegetation and agriculture land. It is also recommended that in order to reduce the land surface temperature of urban areas, sustainable urban planning strategies that include increasing the vegetated areas and embracing other green initiatives such as urban forestry should be adopted.

Volume XIX |

Assessment of spatial changes of land use/land cover dynamics, using multi-temporal Landsat data in Dadri Block, Gautam Buddh Nagar, India

Abstract: The present work aims at presenting certain important ob-servations on food and water security of the peri-urban environment, by considering field data and satellite image classification to understand the spatial change pattern of natural resources and its impact on agriculture and water resources. Gautam Buddh Nagar is considered one of the important urban areas of the National Capital Region (NCR), being associated with multi-functional activity; it continues to grow in terms of infrastructure and other urban activities and the numerous infrastructural projects and other anthro-pogenic actions in the area cause a rising pressure on water, agriculture, and human health. The Landsat satellite images from 2000, 2005, 2010, and 2016 were classified and used to obtain the Land use / Land cover maps of the area, in order to estimate and to understand the rate of change during the last 16 years. There are mapped the important land use classes, such as the agricultural land, the vegetation surfac-es, the built-up areas, the open land, and the water bodies. The results indicate the fact that during the 16 years taken into study, vegetation (2.26%), water bodies (1.65%), and agriculture (3.5%) undergone a major decline, while the built-up land displayed values increased around four times (from 3.39% to 12.26%). The results of the present work clearly showed that the large-scale changes in natural land cover affected the agriculture, as well as the surface and groundwater resources of the area.

Volume XVIII |

How much open water do waterbirds have in the Banat Plain? The first permanent inland water bodies inventory at 10-m resolution using Sentinel-2 imagery at regional – scale

Abstract: In the Banat Plain were recorded more than half of the number of bird species in Romania. There are eleven Nature 2000 Bird Protection Sites (SPAs) and in all of them have been registered aquatic bird species. The water surface areas were greatly reduced since the starting of the hydro-technical works 300 years ago. Nowadays, the anthropic pressure associated with the more frequent drought periods continues to threaten the water bodies, on whose existence the numerous aquatic birds depend. For the mapping and evaluation of open water surfaces in the Banat Plain, in the context of a lack of precise, consistent maps and data, the first water bodies inventory at 10-m resolution was made based on Sentinel-2 multispectral satellite images. The heterogeneous and fragmented landscape, with a great diversity of aquatic units, conducted us to develop an object-oriented approach, which allowed a multi-scale classification process. Both spectral bands and normalized differentiation water indices were used in the rule-based classification algorithm for water detection. The accuracy assessment indicated a very good overall accuracy of 96%, with a Kappa coefficient of 0.91. Also in our dataset, the small water bodies were mapped more accurately than in the other six water bodies datasets at global or European scale we compared with. The results drawn from both qualitative and quantitative assessments indicated that the water dataset developed in this study could be used as an inventory and a reference map for the permanent open water areas from the Banat Plain. It may represent the starting point for better aquatic management and elaboration of ecological strategies to support bird necessities and to counteract the increasing vulnerability and environmental threatening of waterbird species.

Volume XVIII |

New application of fuzzy logic algorithm in GIS for land classification

Abstract: Population growth and disorganization of urban planning have led to unsuitable city development in the center of Iran. Suitable region recognition for urban land develop-ment is an important step towards future planning. In the present study, fuzzy logic algorithms (OR, And, Sum, Prod-uct and Gamma) were used within GIS in order to identify valuable land for appropriate residential development. Moreover, effective factors of urban land development (elevation, slope, aspect, geology, land use, drainage net-work, main and bypass roads, distribution of urban and rural areas, and fault line layer) were examined on fuzzy analyses to find the most effective ones. The results showed that, by considering the regional priorities and constraints, the best operator was Gamma, with a power of 0.9. According to this, 74 percent of the total regions are located between less and the least valuable lands and the remaining surfaces (i.e. 26% of the region) were classified from valuable to the most valuable lands. The sensitivity measurement of the layers used in the study showed that fault and distribution of urban and rural layers were the most and the least effective layers on region recognition (i.e. by 25.83% and 3.29%, respectively).