Land use or land cover (LU/LC) mapping serves as a kind of basic information for land resource study. Detecting and analyzing the quantitative changes along the earth’s surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, economic and industrial growth. The LU/LC pattern in Madurai City, Tamil Nadu, has undergone a significant change over the past two decades due to accelerated urbanization. In this study, LU/LC change dynamics were investigated by the combined use of satellite remote sensing and geographical information system. To understand the LU/LC change in Madurai City, different land use categories and their spatial as well as temporal variability have been studied over a period of seven years (1999-2006), by analyzing Landsat images for the years 1999 and 2006 respectively with the help of ArcGIS 9.3 and ERDAS Imagine 9.1 software. This results show that geospatial technology is able to effectively capture the spatio-temporal trend of the landscape patterns associated with urbanization in this region.

It increased the demands on ground-water supplies that prolonged drought and improper maintenance of water resources. So it is necessary to evaluate ground-water resources in the hard rock terrain. In recent years, Remote-Sensing methods have been increasingly recognized as a means of obtaining crucial geoscientific data for both regional and site-specific investigations. This work aims to develop and apply integrated methods combining the information obtained by geo-hydrological field mapping and those obtained by analyzing multi-source remotely sensed data in a GIS environment for better understanding the Groundwater condition in hard rock terrain. In this study, digitally enhanced Landsat ETM+ data was used to extract information on geology, geomorphology. The Hill-Shading techniques are applied to SRTM DEM data to enhance terrain perspective views, and extract Geomorphological features and morphologically defined structures through the means of lineament analysis. A combination of Spectral information from Landsat ETM+ data plus spatial information from SRTM-DEM data is used to address the groundwater potential of alluvium, colluvium, and fractured crystalline rocks in the study area. The spatial distribution of groundwater potential zones shows regional patterns related to lithologies, lineaments, drainage systems, and landforms. High-yielding wells and springs are often related to large lineaments and corresponding structural features such as dykes. The results show that the combination of remote sensing, GIS, traditional fieldwork, and models provide a powerful tool for water resources assessment and management, and groundwater exploration planning.

This article explored mineral resources and their relation to structural settings in the Central Eastern Desert (CED) of Egypt. Integration of remote sensing (RS) with aeromagnetic (AMG) data was conducted to generate a mineral predictive map. Several image transformation and enhancement techniques were performed to Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) data. Using band ratios and oriented principal component analysis (PCA) on OLI data allowed delineating hydrothermal alteration zones (HAZs) and highlighted structural discontinuity. Moreover, processing of the AMG using Standard Euler deconvolution and residual magnetic anomalies successfully revealed the subsurface structural features. Zones of hydrothermal alteration and surface/subsurface geologic structural density maps were combined through GIS technique. The results showed a mineral predictive map that ranked from very low to very high probability. Field validation allowed verifying the prepared map and revealed several mineralized sites including talc, talc-schist, gold mines and quartz veins associated with hematite. Overall, integration of RS and AMG data is a powerful technique in revealing areas of potential mineralization involved with hydrothermal processes.

Forests have ecological functions in water conservation, climate regulation, environmental purification, soil and water conservation, biodiversity protection and so on. Carrying out forest ecological quality assessment is of great significance to understand the global carbon cycle, energy cycle and climate change. Based on the introduction of the concept and research methods of forest ecological quality, this paper analyzes and summarizes the evaluation of forest ecological quality from three comprehensive indicators: forest biomass, forest productivity and forest structure. This paper focuses on the construction of evaluation index system, the acquisition of evaluation data and the estimation of key ecological parameters, discusses the main problems existing in the current forest ecological quality evaluation, and looks forward to its development prospects, including the unified standardization of evaluation indexes, high-quality data, the impact of forest living environment, the acquisition of forest level from multi-source remote sensing data, the application of vertical structural parameters and the interaction between forest ecological quality and ecological function.

In order to evaluate the temporal changes in tree diversity of forest vegetation in Xishuangbanna, Yunnan Province, the study collected tree diversity data from four main forest vegetation in the region through a quadrat survey including tropical rainforest (TRF), tropical coniferous forest (COF), tropical lower mountain evergreen broad-leaved forest (TEBF), tropical seasonal moist forest (TSMF). We extracted the distribution of four forest vegetation in the region in four periods of 1992, 2000, 2009, and 2016 in combination with remote sensing images, using simp son Shannon Wiener and scaling species diversity indexes compare to the differences of tree evenness of four forest vegetation and use the scaling ecological diversity index and grey correlation evaluation model to evaluate the temporal changes of forest tree diversity in the region in four periods. The results show that: (1) The proportion of forest area has a trend of decreasing first and then increasing, which is shown by the reduction from 65.5% in 1992 to 53.42% in 2000, to 52.49% in 2009, and then to 54.73% in 2016. However, the tropical rainforest shows a continuous decreasing trend. (2) There are obvious differences in the contributions of the four kinds of forest vegetation to tree diversity. The order of evenness is tropical rainforest > tropical mountain (low mountain) evergreen broad-leaved forest > warm coniferous forest > tropical seasonal humid forest, and the order of richness is tropical rainforest > tropical mountain (low mountain) evergreen broad-leaved forest > tropical seasonal humid forest > warm coniferous forest, The order of contribution to tree diversity in tropical rainforest > tropical mountain (low mountain) evergreen broad-leaved forest > tropical seasonal humid forest > warm tropical coniferous forest. (3) The tree diversity of tropical rainforests and tropical seasonal humid forests showed a continuous decreasing trend. The tree diversity of forest vegetation in Xishuangbanna in four periods was 1992 > 2009 > 2016 > 2000. The above results show that economic activities are an important factor affecting the biodivesity of Xishuangbanna, and the protection of tropical rainforest is of great significance to maintain the biodiversity of the region.

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.