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Leaves can be used to assess the health of a plant. The chlorophyll content and nitrogen status of plants might be used to assess plant health. Chlorophyll content of plant leaves is measurable in the laboratory using dimethyl sulfoxide or the Kjeldahl technique. These destructive investigations of plant tissues accurately evaluates chlorophyll concentration and nitrogen status. However, this procedure is costly and time-consuming. Using a SPAD instrument, non-destructive chlorophyll measurements were performed. However, the price of the instrument is high. This research intends to develop a technique for detecting chlorophyll content using a smartphone's built-in camera. The approach used is an advancement of ground-based remote sensing technology. The object of this research is maize. An RGB camera on a mobile phone was used to gather data on the greenness level of the leaf. Concurrently, the SPAD value was measured to determine the chlorophyll content. In this study, there were three primary phases, capturing leaf images with geotags, leaf image processing, and vegetation index (VIs) analysis. This research demonstrated that a mobile camera has potential as a tool for measuring chlorophyll content depending on the level of leaf greenness. Additionally, the GPS function on each data provides the chlorophyll distribution in a field. [ABSTRACT FROM AUTHOR]

A technique known as "image fusion" is used to combine important or useful information from a number of original or related input photos into a single output image that is more accurate and comprehensive than any of the input images. Image enhancement is a technique used in pre-processing to improve the quality of many different input pictures, especially in scientific fields including microscopy, robotics, computer vision, medical imaging, and remote sensing. In this work, we show how picture merging improves image quality. This is accomplished by utilizing DCT technology. Following DCT, many methods—including the zero matrices and positive coefficient are used to extract the distinctive properties. The inverse discrete cosine transform (IDCT) is applied in the next step. The combined picture is then created by putting the average fusion rules by using a simple average between blocks. The end result is an image with more information than any of the input images. To evaluate the fused picture, a scale structure similarity index metric is applied. [ABSTRACT FROM AUTHOR]

"Groundwater is invisible, but its impact is visible everywhere." Around two third of fresh water is constituted by ground water. In India most of the rural and parts urban population mainly depends upon groundwater for human and animal consumption. Groundwater plays a vital role in human consumption, agriculture, industries and for other purposes and any changes in its quality have adverse consequences. The major groundwater affected zones are concentrated as study area it includes parts of Tamil Nadu such as, North - western zone of Tamil Nadu (Salem, Dharmapuri, Nammakal, Krishnagiri and Perambalur). The main objective is to find the groundwater potentialzones of the study area. The factors like like Slope, Lineament density, Drainage density, Soil and Lithology were created using Sentinal-2 satellite data with remote sensing tools and softwares. The groundwater potential zones were mapped by combining the above mentioned factors with Normalized Pairwise Comparison Matrix method. Based on this study groundwater potential status was given to detect the changes in quality and give early warnings of groundwater potential to the farmers, industries, water department and rural population. [ABSTRACT FROM AUTHOR]

One of the areas of computer vision is superpixel segmentation, and the widespread use of superpixels in a wide range of practical applications shows that it has unique properties. A superpixel is created by combining pixels with similar content, which significant reduce the computational complexity of the algorithm at the next stages of image processing. After the superpixel is built, it is possible to solve problems such as classification, reduction of image dimensions, construction of spectral combination, selection of a certain channel, noise reduction, and change detection based on the application of superpixel to spectral images. This paper presents an analysis of SLIC, SEEDS, LSC, DFIC, AHP, DRW, WSS, BACA, and superpixel segmentation approaches on remote sensing images. The analysis of approaches was considered according to the characteristics of the color model, scale, regularity, connection, smoothness, texture, and boundary strength. Approaches were evaluated using the criteria of Undersegmentation error, Achievable segmentation accuracy, and Boundary Recall. The study revealed that superpixel segmentation techniques that employ all of the spectral image channels have not received enough attention; instead, they are typically applied using PCA to shrink the size or convert them to color models. Additionally, it is important to investigate the issue of applying deep learning techniques to the implementation of superpixel segmentation in remotely sensing spectral images. [ABSTRACT FROM AUTHOR]

In remote sensing images, numerous image fusion methods have been developed to produce high-resolution multispectral images from multispectral (MS) and panchromatic (PAN) image. This work suggests suitable algorithm that maintains the spectral and spatial information content and to overcome the spectral degradation of satellite images. Three statistical methods are used to implement the fusion process based on Discrete wavelet transform (DWT). Absolute Max, Mean and Standard Deviation and Simple Linear Regression (SLR). The DWT are applied on both MS bands and PAN images to produce high and low frequency components, then the statistical methods are applied on high components as a fusion method. Lastly the inverse DWT is implemented to produce the final fused image. Three metrics (Standard Deviation SD, Peak signal-to-noise ratio PSNR and Correlation Coefficient CC) are implemented to evaluate the obtain results quantitatively in addition to visual assessment. Good qualitative and quantitative results were obtained with a Absolute Max and SLR over Mean and Standard Deviation. [ABSTRACT FROM AUTHOR]

To manage the populations of hunting animals, to make managerial decisions in the hunting economy, it is necessary to know not only the actual, but also the effective (economically expedient) number. The purpose of the study is to determine the effective (economically feasible) number of hunting animals based on geographic information systems (GIS) and Earth remote sensing data. GIS "Kuzbass", specialized cartographic software, analysis of the normalized vegetation index were used. It is shown that the current documents incorrectly determine the effective number due to inaccurate boundaries and incorrect definition of classes and categories of hunting grounds. For all hunting grounds of the Kemerovo municipal district (Kemerovo region – Kuzbass, Russia), classes and categories of grounds have been specified. The effective number of hunting animals was re-evaluated. It is shown that some of the lands are erroneously classified as highly productive lands for bears and ungulates, so the estimate of their abundance has been adjusted downward. There are more swamps and clump forests in the study area than it is assumed by the current documents. Therefore, the effective abundance of sable and birds is higher than currently established. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced all industries to close their doors, and other human activities came to a stop. But the lockdown's effects on the hydrosphere, atmosphere and all life forms were profound. Government officials and environmentalists have been working to clean up the entire artificial facade for several years, but the lockdown has completely altered the situation. As before and during the lockdown water quality measures revealed considerable variations, the COVID-19 epidemic proved to be beneficial for the Yamuna River. The Yamuna is the second-largest Ganges tributary in India and is the country's longest river. Haryana, Uttar Pradesh, Himachal Pradesh, and Uttar Pradesh are the four states and a union territory through which the Yamuna flows (Delhi). For the study, a stretch of river was defined as the distance from Palla, upstream on the Yamuna, to Okhla Barrage, downstream in Delhi. When comparing water quality parameters between before the lockdown (January and February 2020) and during the lockdown (March andApril 2020), visual interpretation using remote sensing and geographic information systems was performed, demonstratingthat there was a considerable drop in turbidity value. In a conclusion, the application of remote sensing and GIS technologies is highly anticipated and beneficial for data processing. [ABSTRACT FROM AUTHOR]

Water, the most significant element of natural resource available on earth supports liquidity to the life cycle. Their changing status enables the hydrophilic and hydrophobic polarity among the elementary ecosystem which further explains the peculiarity by components, pollutants concentration, and increasing anthropogenic load. In terrestrial/ or inland water bodies, the cyclic affection of rapid urbanization and rising cumulate demands pose a continuous and direct threat to fresh water and its nutritional status. Continuous affection of these resources starts degradation in terms of water quality and it leads to the eutrophication processes. The eutrophication process starts with the enrichment of nutrients as plants and algae bloom to grow in the hydro-ecosystem and then turn the water unfit for aquatic organisms and consumption by humans and other living organisms. The present study concerns the degrading state of High-altitude lakes (HAL)'s and their compromising conditions in the region of Nainital, Uttarakhand. It is critically reviewed that, the regional hydro- ecosystem is under stress. The continuous over-stress affects the aesthetic importance and is primarily controlled by the surrounding 'Architecture with nature'. Observation depicts, the amalgamation does not fit and support its sensitive topography and impact in terms of abnormal responses including enrichment of algal (cyanobacterial) bloom, microbial biomass, and changing geochemical characteristics. The bibliography indicates the practices along the lake environment in the last many decades but is inconsistent in terms of time and position-based analysis. Recent advancement in remote sensing and GIS and directional development in such areas gives space for their continuous monitoring and mapping efficiently and cost-effectively. With the current issue, an urgent need is to monitor the reservoirs and lakes of the territory to check the processes whether rapidly changing or any influence affecting them. Landuse-Landcover (LULC) and their impact on water bodies, Spectral indices like Normalized difference water index (NDWI), Maximum chlorophyll index (M-Chl), Green normalized difference vegetation index (G-NDVI), Normalized Difference turbidity index (NDTI), Topographic State Index (TSI), etc. are some extensively used indices for the water quality estimation. However, recent time developments in merging techniques with drone and unmanned aerial vehicles can attribute the research to a more precise and finer scale. The present review article describes the use of remote sensing techniques to retrieve information on water quality and using land and space-based derived quality indicators and their co-relationships for model development. The retrieval strategies for various specific water quality indicators, such as chlorophyll-a, total suspended matter (TSM), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus, are summarized in this article. (TP). Considerable difficulties associated with the resolution and retrieval models in the contexts or focusing on specific geographic bound, temporal, and water complexity have also been discussed. Finally, potential answers to these problems have been suggested. The review can offer thorough references for upcoming work in retrieving water quality development and research. [ABSTRACT FROM AUTHOR]

Wildfires in Indonesia reached their peak during the drought of 2015, with estimated burnt forest and land of up to 2.6 million hectares. During the dry season of 2020, high temperatures and high winds were responsible for losing 659.02 hectares from forest fires in Lombok Island. The fires on the Island caused economic hardship, disrupted commerce, and led to short and long-term health problems for most of the population. This study aimed to use Geographic Information Systems (GIS) and Remote Sensing (RS) to map forest fire hotspots, establish fire brigades, and suppress forest recovery programs. Wildfire annual data were acquired from Satu Data NTB website, DEM from ASTER Global Elevation Model, and NDVI from Sentinel2B. DEM and NDVI were calculated and projected into 3D objects. The Wildfire geolocations were then added to 3D objects and the scale according to wildfire level. The performance of forest recovery is measured using the development of NDVI (Normalized Difference Vegetation Index) in affected fire areas. Results show the overall vegetation index increases in the area where fire forest has occurred. Combined RS and GIS efforts show the prospective application to evaluate forest fire control and recovery programs' performance. [ABSTRACT FROM AUTHOR]

The integration of remote sensing and GIS applications has provided researchers with advanced tools to manage the environment through the aid of data. And it shows their quantitative and qualitative characteristics using remote sensing techniques and geographic information systems. The research problem is producing thematic maps from high-resolution images and knowing their accuracy after production. The experimental approach was chosen since it was the ideal method for investigating throughout the research phase. This, of course, is contingent on the kind of research being conducted, the main objective of the study, the quantity and quality of the readily available data, and the area of research currently being looked at. The purpose of this study is to investigate the viability of employing geographic information systems in the direct production of thematic maps directly from satellite images, as well as making use of aerial survey data and field data collected using measuring tapes and Global Positioning System (GPS), and analyzing the quantitative and qualitative differences between the two types of maps. (Positional data file) Putting up a database of location information using topographic maps. The final product was generated using two different ways of cartographic processing. It uses the practical application of making a map via geographic information systems and then contrasting that map with the one found in the ArcGIS software that ESRI developed. The horizontal resolution of high-resolution satellite images is shown in this article. The images have a scale of 1:1000 and have a solution of (1*1) meters. The findings showed that the maps were accurate, with an average error of (-0.718 E,-0.714 N) meters for points, (-0.622) meters for lines, and (-320.033) meters for areas. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) techniques are intensively used for monitoring and mapping the land use and land cover. The integration between spatial and temporal capabilities of remote sensing applications with the spatial capacity of geographical information system (GIS) can be exploited as a powerful tool to manage and assess the land use and land cover of earth's surface. These techniques can be used for mapping land use land cover (LULC) and to detect, plan, and manage environmental variations on Al-Najaf city southern of Iraq as depending on an attempt to track changes in satellite images over the years 2007-2011 and 2011-2018 based on Landsat-5 Thematic Mapper (TM) and Landsat 8 (OLI) data. Supervised classification with the Maximum Likelihood Classifier (MLC) used to classify environmental variations in the study area. There are six types of ground cover were identified as the wet land, water bodies, vegetation, urban areas, bare land and agriculture. The urban areas, water bodies, vegetation and wet land results have increased by 21.16% (191.82km2), 7.75% (70.26km2), 6.95% (62.98km2) and 0.05% (0.49 km2) respectively. While bare land and agriculture have decreased by-26.95% (-244.35km2) and -8.95% (-81.17km2) respectively. The overall accuracy on satellite images during years 2007, 2011 and 2018 are as 77.78%, 94.4% and 92.2% with a Kappa coefficient as 0.73, 0.93 and 0.90 respectively. There are fundamental changes, which were observed in LULC between satellite images in 2007 and 2018.The accuracy results on the satellite images during years 2011 and 2018 were significantly better and higher than the values in 2007 images. [ABSTRACT FROM AUTHOR]