scholarly journals A study on deforestation in hilly areas of Haryana using Remote Sensing and GIS technique

2020 ◽  
pp. 69-77
Author(s):  
Anju Jangra ◽  
Anurag Airon ◽  
Ram Niwas
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Special Focus ◽  
Landsat 8 ◽  
Deforestation Rate ◽  
Gis Technique ◽  
Prime Concern

Forest is an essential part or backbone of the earth ecological system. In a country like India, the people and the economy of nation is mainly relies on the diversity of natural resources. In today's world degradation of forest resources is a prime concern for many of the scientists and environmentalists because the canvas had been transformed from last few decades to cultivated and non-cultivated land. In India, Haryana state has lowest forest cover i.e. 3.59% followed by Punjab 3.65%. Over the several decades, the advancement of Remote Sensing and Geographical Information System (GIS) technique has emerged as an efficient tool to monitor and analyse deforestation rate in hilly areaor over a variety of location. Remote sensing based vegetation indices show better sensitivity than individual band reflectance and hence are more preferred for assessment and monitoring of tress. The aim of the present study was to analyse the deforestation in hilly areas in Haryana State (India) by remote sensing data with a special focus on Panchkula and Yamunanagar. The information was collected through the LANDSAT 8 satellite of NASA. The result revealed that the deforestation rate is high in Hilly areas of Haryana. The study shows that the forest cover in hilly areas of Haryana in 2013 was 50,879.07 hectares and in 2019 it was 44,445.51 hectares of land. Thereby decrease in forest cover of 6,433.56 hectares had been observed in the study period of 2013-2019 i.e. 6 years. Spatial variations in deforestation were also mapped in GIS for the hilly areas in Panchkula and Yamunanagar districts of Haryana.  

scholarly journals МАСШТАБНО-ЗАВИСИМЫЕ ЭФФЕКТЫ В СТРУКТУРЕ ЭКОЛОГИЧЕСКОЙ НИШИ ЛЕБЕДЯ-ШИПУНА CYGNUSOLOR (GMELIN, 1803) В ПЕРИОД ЗИМОВКИ В ПРЕДЕЛАХ ЗАЛИВА СИВАШ

2016 ◽  
Vol 6 (3) ◽  
pp. 234-247
Author(s):  
A. Yu. Andrushenko ◽  
A. V. Zhukov
Keyword(s):  
Remote Sensing ◽  
Ecological Niche ◽  
Random Distribution ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Information Value ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Elevation Model

<p>The assessment of the information value of ecogeographical predictors based on remote sensing data from satellites to reflect features of the ecological niche of the Swan-mute <em>Cygnus up</em> (Gmelina, 1803) in wintering within the Gulf Sivash have been presented. Two groups predictors of ecogeographical landscape data have been considered. The first group is assigned digital elevation model and its derivatives. The second set of classified vegetation indices obtained from Landsat 8 image. Ecological niche has been described using ENFA-procedure. The procedure of random distribution of the pseudo-absent points which range from the presence points restricted by some distance has been applied to assess the role of scale in ecological niche. Ecological niche of Swan mute has been shown to be described in terms of landscape ecogeographical variables. The properties of the ecological niche of the Swan-mute have been found to be depends upon the scale of its consideration. Under various boundary ranges we can get an entirely different, but statistically valid, assess the structure of the ecological niche of the Swan-mute based landscape ecogeographical predictors. The role of the various ecogeographical predictors depending on the scale can vary greatly.</p>

scholarly journals Relationship between remote sensing data and field-observed interril erosion

2018 ◽  
Vol 53 (3) ◽  
pp. 332-341 ◽  
Author(s):  
André Geraldo de Lima Moraes ◽  
Daniel Fonseca de Carvalho ◽  
Mauro Antonio Homem Antunes ◽  
Marcos Bacis Ceddia
Keyword(s):  
Remote Sensing ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Exponential Model ◽  
Landsat 8 ◽  
Sensing Data ◽  
Linear Spectral Mixture Analysis ◽  
Spectral Mixture ◽  
The Relationship

Abstract: The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.

Hybrid Algorithm for Deforestation Detection Using Satellite Data by Using Support Vector Machine (SVM) Algorithm

2018 ◽  
Vol 6 (6) ◽  
pp. 92 ◽  
Author(s):  
J Suresh Babu ◽  
T. Sudha
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Training Data ◽  
Geographical Information ◽  
Support Vector ◽  
Data Sets ◽  
Forest Cover Change ◽  
Svm Algorithm ◽  
Vector Machines

There are many applications of Remote sensing Satellite images like (astronomy, military, forecasting, and geographical information). Using satellite remote sensing data sets we developed the mapping forest area cover change. This kind of multiple improvement and identifying methods have a Training Data Automation algorithm which is used for advanced vector machines procedure. This TDM technique capable of automatically generating exact image enhanced patches. The obtained high resolute training data allow in producing the dependable forest cover change products with the help of SVM. This process was tested in study areas selected from major forest areas across the globe. In each area, a forest cover change map was produced using a pair of real time Land sat images acquired around 1999 and 2015.

scholarly journals Assessment of Irrigation Potentiality over an Area through the Combined Application of Remote Sensing (RS) and Geographic Information System (GIS)

2020 ◽  
Vol 11 (2) ◽  
pp. 141-148
Author(s):  
Md Abdus Salam ◽  
Farhana Tazneen ◽  
Md Shafiqul Islam ◽  
SM Noman Chy
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Ground Water ◽  
Water Level ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Ground Water Level ◽  
Secondary Source ◽  
Landsat 8 ◽  
Sensing Data

There is a great influence of irrigated land of an area to the acreage and productions of agricultural crops and thus maintain the food security. Recent awareness about climate change and its impacts on global environmental challenges has drawn the great attention on rational and sustainable handling of irrigation resources and its networks. As one of the cutting-edge technologies remote sensing data and geographical information system (GIS) are very much useful for efficient management of irrigation networks and optimum utilization of irrigation schemes for the sustainable agricultural development. Irrigation potentiality is the total area which can be irrigated from a project on its full utilization. This implies that an area where water is available for irrigation in each season during a complete irrigation year. In the present study an attempt has been made to investigate the irrigation potentiality of an area using remote sensing data as primary source and field data and as well as ground water level data from secondary source. Landsat 8 OLI (Operational Land Imager) data of 2016 and 2017 have been used for this purpose. Existing irrigation system has also been identified through the investigation of natural and artificial sources of irrigation water of the study area. Seasonal irrigated area was also monitored during the crop growing season. Ground water level fluctuation was also studied using ancillary data. Journal of Engineering Science 11(2), 2020, 141-148

scholarly journals Estimating Crop Yields at the Field Level Using Landsat and Modis Products

2018 ◽  
Vol 66 (5) ◽  
pp. 1141-1150 ◽  
Author(s):  
František Jurečka ◽  
Vojtěch Lukas ◽  
Petr Hlavinka ◽  
Daniela Semerádová ◽  
Zdeněk Žalud ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Spring Barley ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Yield Data ◽  
Field Level

Remote sensing can be used for yield estimation prior to harvest at the field level to provide helpful information for agricultural decision making. This study was undertaken in Polkovice, located at low elevations in the Czech Republic. From 2014–2016, two datasets of satellite imagery were used: the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 datasets. Satellite data were compared with yields and other observations at the level of land blocks. Winter oilseed rape, winter wheat and spring barley yield data, representing the crops planted over the analyzed period, were used for comparison. In 2016, a more detailed analysis was conducted. We tested a relationship between remote sensing data and the spatial yield variability measured by a yield monitor from a combine harvester. Correlations varied from approximately r = 0.4 to r = 0.7 with the highest correlation (r = 0.74) between yield and the Green Normalized Difference Vegetation Index collected from a drone. Vegetation indices from both Landsat 8 and the MODIS showed a positive relationship with yields for the compared period. The highest correlation was between yield and the Enhanced Vegetation Index (r = 0.8) while the lowest was between yield and the Normalized Difference Vegetation Index from MODIS (r = 0.1).

scholarly journals An Overview of Remote Sensing Data Applications in Peatland Research Based on Works from the Period 2010–2021

Land ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Sebastian Czapiewski ◽  
Danuta Szumińska
Keyword(s):  
Remote Sensing ◽  
Open Source Software ◽  
Rapid Development ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Field Research ◽  
Landsat 8 ◽  
Data Sets ◽  
Climatic Zones ◽  
Sensing Data

In the 21st century, remote sensing (RS) has become increasingly employed in many environmental studies. This paper constitutes an overview of works utilising RS methods in studies on peatlands and investigates publications from the period 2010–2021. Based on fifty-nine case studies from different climatic zones (from subarctic to subtropical), we can indicate an increase in the use of RS methods in peatland research during the last decade, which is likely a result of the greater availability of new remote sensing data sets (Sentinel 1 and 2; Landsat 8; SPOT 6 and 7) paired with the rapid development of open-source software (ESA SNAP; QGIS and SAGA GIS). In the studied works, satellite data analyses typically encompassed the following elements: land classification/identification of peatlands, changes in water conditions in peatlands, monitoring of peatland state, peatland vegetation mapping, Gross Primary Productivity (GPP), and the estimation of carbon resources in peatlands. The most frequently employed research methods, on the other hand, included: vegetation indices, soil moisture indices, water indices, supervised classification and machine learning. Remote sensing data combined with field research is deemed helpful for peatland monitoring and multi-proxy studies, and they may offer new perspectives on research at a regional level.

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

Worldview-2 and Landsat 8 Satellite Data for Seaweed Mapping along Karachi Coast

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z Zaidi
Keyword(s):  
Satellite Data ◽  
Spatial Scales ◽  
Economic Value ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Geographical Information ◽  
Landsat 8 ◽  
Coverage Area ◽  
Breeding Sites ◽  
Marine Plant

<span>Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of <span>this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite <span>data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastal<br /><span>waters of Karachi. The continuous monitoring and mapping of this precious marine plant and their <span>breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote <span>Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient <span>solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both <span>temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image <span>enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of <span>seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation <span>wasachieved with WV-2 data that shows 15.5Ha (0.155 Km<span>2<span>)of seaweed cover along Karachi coast that is <span>more representative of the field observed data. A much larger area wasestimated with Landsat 8 image <span>(71.28Ha or 0.7128 Km<span>2<span>) that was mainly due to the mixing of seaweed pixels with water pixels. The <span>WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat<br /><span>8 in mapping seaweed patches</span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span>

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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