scholarly journals Habitat Suitability Index Modelling for Bluebull (Boselaphus tragocamelus) in Pench Tiger Reserve, M.P. India

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Abdul Haleem ◽  
Orus Ilyas
Keyword(s):  
Wildlife Conservation ◽  
Indian Subcontinent ◽  
Satellite Image ◽  
Damage Model ◽  
Additive Model ◽  
Crop Damage ◽  
Landsat 8 ◽  
Farming Community ◽  
Tiger Reserve ◽  
Erdas Imagine

The habitats for the wild animals are shrinking due to the clearance of forests for agriculture and industrialization. The idea of wildlife conservation begins with the identification of their acceptable habitat. Since this crucial information helps in the development and maintenance of the protected areas. The requirement of habitat varies with different landscapes.The bluebull (Boselaphus tragocamelus) is Asia’s largest antelope,widespread throughout the northern Indian subcontinent. Peter Simon Pallasin (1766) described it as the only member of the genus Boselaphus.The Wildlife (Protection) Act of 1972 lists it as a Schedule III animal, while the IUCN lists it as Least Concern (LC). Our goal was to design a habitat appropriateness model for blue bull so that it could reduce the conflict with farming community due to crop damage. Model will be develop using RS & GIS technique to protect the species inside the Pench Tiger Reserve (77° 55’ W to 79° 35’ E and 21° 08’ S to 22° 00’ N) the central highlands of India. The satellite data from LANDSAT-8 of 4th April 2015, Path- 144,Row- 45, with a ground resolution of 30 meters, were collected from the USGS website. This satellite image was then transferred in image format to ERDAS IMAGINE 2013 for further analysis. The data from satellites were gathered and analysed. The purpose of the field survey was to gather information about the presence of various ungulates. A ground truthing exercise was also carried out. For data processing and GIS analysis,ERDAS IMAGINE 13 and Arc GIS 10 were used. Analytical Hierarchy Process (AHP) was used Factors were identified who were influencing the spatial distribution of the species for conservation planning. The linear additive model was used for HSI. The results show that 242 km2 (29.48 percent) of Pench Tiger Reserve forest was recognized to be highly suitable for bluebull, while 196 km2 (23.87 percent) was moderately suitable,231 km2 (28.14 percent) was suitable, 109 km2 (13.28 percent) was least suitable, and about 43 km2 (5.249 percent) of PTR was completely avoided by bluebull.

scholarly journals MONITORING NATURAL ENVIRONMENTAL INFRINGEMENTS BY TECHNOGENOUS PROCESSES DECLINING COSMOSES

2019 ◽  
Vol 4 (2) ◽  
pp. 64-72
Author(s):  
Aida Kalieva ◽  
Arslanbek Bayshuakov ◽  
Alyona Ermienko
Keyword(s):  
Spectral Analysis ◽  
Software Package ◽  
Satellite Image ◽  
Landsat 8 ◽  
Environmental Disturbances ◽  
Mining Sites ◽  
Industrial Enterprises ◽  
Erdas Imagine ◽  
Spectral Analysis Method

The tasks are given for the interpretation of environmental disturbances by technogenic processes: the selection on a satellite image of areas of natural complexes transformed by various types of economic activity; identification and characterization of sources of anthropogenic environmental impact; construction of the on-board version of the map illustrating the conclusions obtained during the interpretation of the satellite image. To perform the interpretation, images of Landsat 8 satellite images were used in two versions: in natural and false colors. Using the processes of automated decoding, in the ERDAS IMAGINE software package, images with different colors were obtained, allowing to divide objects into classes. The method "Spectral analysis" divided objects into 5 classes. According to the results of the interpretive work in MapInfo Professional, two raster images were superimposed on each other, one of which is a picture in natural colors, the other is an image obtained by the Spectral Analysis method. The imposition of raster images as a substrate allowed us to determine the boundaries of the areas that were subjected to industrial and man-made processes. As a result, a scheme was created illustrating the positions of sites subjected to industrial and man-made processes. Created in the MapInfo Professional software package, the scheme contains mining sites for coal and limestone, as well as industrial enterprises, indicated by numbers in the scheme. As a result, a scheme of environmental disturbances by industrial and man-made processes was obtained.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Soil Salinity Detection Using Salinity Indices from Landsat 8 Satellite Image at Rampal, Bangladesh

2021 ◽  
Author(s):  
Rubaid Hassan ◽  
Zia Ahmed ◽  
Md. Tariqul Islam ◽  
Rafiul Alam ◽  
Zhixiao Xie
Keyword(s):  
Soil Salinity ◽  
Satellite Image ◽  
Landsat 8

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

2018 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Image Data ◽  
Research Area ◽  
Rice Production ◽  
Landsat 8 ◽  
Estimation Model ◽  
Accuracy Test ◽  
Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

scholarly journals Lineament automatic extraction analysis for Galal Badra river basin using Landsat 8 satellite image

2019 ◽  
Vol 12 (25) ◽  
pp. 44-55 ◽  
Author(s):  
Safaa Sabah Adhab
Keyword(s):  
Program Analysis ◽  
Satellite Image ◽  
Landsat 8 ◽  
Automatic Extraction ◽  
Lineament Density ◽  
Density Analysis ◽  
Lineament Analysis ◽  
Intersection Density ◽  
Surrounding Areas ◽  
The Relationship

This research including lineament automated extraction by using PCI Geomatica program, depending on satellite image and lineament analysis by using GIS program. Analysis included density analysis, length density analysis and intersection density analysis. When calculate the slope map for the study area, found the relationship between the slope and lineament density.The lineament density increases in the regions that have high values for the slope, show that lineament play an important role in the classification process as it isolates the class for the other were observed in Iranian territory, clearly, also show that one of the lineament hit shoulders of Galal Badra dam and the surrounding areas dam. So should take into consideration the lineaments because its plays an important role in the study area.

scholarly journals Atmospheric correction of multispectral satellite imagery

2020 ◽  
pp. 4-11
Author(s):  
Leonid Katkovsky
Keyword(s):  
Land Surface ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Sensor Data ◽  
Physical Parameters ◽  
Landsat 8 ◽  
Optical Parameters ◽  
Outgoing Radiation ◽  
Correction Technique

Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point of view of radiation transfer, the atmosphere is considered homogeneous within a satellite image. To solve the system of equations containing the measured radiance of the outgoing radiation in the bands of the satellite sensor, the number of which is less than the number of unknowns of the model, it is proposed to use various additional relations, including regression relations between the optical parameters of the atmosphere. For a particular image pixel selected in a special way, unknown atmospheric parameters are found, which are then used to calculate the reflectance for all other pixels.Testing the proposed technique on OLI sensor data of Landsat 8 satellite showed higher accuracy in comparison with the FLAASH and QUAC methods implemented in the well-known ENVI image processing software. The technique is fast and there is using no additional information about the atmosphere or land surface except images under correction.

scholarly journals Pansformers: Transformer-Based Self-Attention Network for Pansharpening

2021 ◽  
Author(s):  
Nithin G R ◽  
Nitish Kumar M ◽  
Venkateswaran Narasimhan ◽  
Rajanikanth Kakani ◽  
Ujjwal Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Performance Metrics ◽  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Learning Models ◽  
Attention Network ◽  
Panchromatic Image ◽  
Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

scholarly journals Factors Influencing People's Response Toward Tiger Translocation in Satkosia Tiger Reserve, Eastern India

2021 ◽  
Vol 2 ◽  
Author(s):  
Vaishali Vasudeva ◽  
Pitchai Ramasamy ◽  
Rabi Sankar Pal ◽  
Gatikrishna Behera ◽  
Pradeep Raj Karat ◽  
...  
Keyword(s):  
Wildlife Conservation ◽  
Resource Dependence ◽  
Local Community ◽  
Well Being ◽  
Negative Influence ◽  
Local Communities ◽  
Forest Department ◽  
Household Level ◽  
Tiger Reserve ◽  
Tiger Conservation

Local communities are an important stakeholder in any carnivore translocation programme and therefore, their acceptance of the translocation and support are essential to ensure its viability. Recent tiger augmentation efforts in Satkosia Tiger Reserve, India received mixed responses from the local communities, causing a stalemate in its progress. As a part of the adaptive management strategy, it was required to assess the concerns and issues to provide a practical solution. Hence, we analyzed the attitude of the people toward conservation in general and tiger specifically. We used structured questionnaire surveys and interviewed 1,932 households from 43 villages located in and around the reserve. We tested the influence of several variables representing four categories- (1) socio-economic, (2) ecosystem values and dependence, (3) relationship with the forest department and (4) losses and fear, on the attitude toward tiger conservation. The villages were clustered based on the responses received under these categories. While conserving forest was important to 91% of respondents, 71% of respondents supported wildlife conservation and only 35% felt important to conserve tiger. The logistic binary regression predicted that at the household level attitude toward tiger conservation is influenced positively by economic well-being, sense of forest ecosystem services, resource dependence and negatively influenced by restrictions from the forest department, and previous experience of loss due to wildlife. At the village level, literacy, resource dependence, access to clean cooking fuel and cooperation from the forest department predicted a positive attitude toward tiger conservation. Restriction from the forest department, fear for livestock, and experience of losses due to wildlife had a negative influence on attitude. We recommend that the villages in the landscape are prioritized based on their needs and accordingly, specific interventions are made to address their concerns. Future augmentation programme must give importance to intangible factors such as fear and perceived restrictions and opt for the involvement of the local community in the decision-making process.

Sign in / Sign up

Export Citation Format

Share Document