scholarly journals VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA USING REMOTE SENSIGN AND GIS TECHNIQUES

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 121-125 ◽  
Author(s):  
A. G. Koppad ◽  
B. S. Janagoudar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Anthropogenic Activities ◽  
Ground Truth ◽  
Vegetation Pattern ◽  
Land Use Land Cover ◽  
Forest Plantation ◽  
Ground Truth Data ◽  
Dense Forest ◽  
Open Land

The study was conducted in Uttara Kannada districts during the year 2012&amp;ndash;2014. The study area lies between 13.92&amp;deg;&amp;thinsp;N to 15.52&amp;deg;&amp;thinsp;N latitude and 74.08&amp;deg;&amp;thinsp;E to 75.09&amp;deg;&amp;thinsp;E longitude with an area of 10,215&amp;thinsp;km<sup>2</sup>. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32&amp;thinsp;% (6468.70&amp;thinsp;sq&amp;thinsp;km) followed by agriculture 12.88&amp;thinsp;% (1315.31&amp;thinsp;sq.&amp;thinsp;km), sparse forest 10.59&amp;thinsp;% (1081.37&amp;thinsp;sq.&amp;thinsp;km), open land 6.09&amp;thinsp;% (622.37&amp;thinsp;sq.&amp;thinsp;km), horticulture plantation and least was forest plantation (1.07&amp;thinsp;%). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non- vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

scholarly journals VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA THROUGH GEO-INFORMATICS APPROACH

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 219-223 ◽  
Author(s):  
A. G. Koppad ◽  
B. S. Janagoudar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Anthropogenic Activities ◽  
Ground Truth ◽  
Vegetation Pattern ◽  
Land Use Land Cover ◽  
Forest Plantation ◽  
Ground Truth Data ◽  
Dense Forest ◽  
Open Land

The study was conducted in Uttara Kannada districts during the year 2012&amp;ndash;2014. The study area lies between 13.92&amp;deg;&amp;thinsp;N to 15.52&amp;deg;&amp;thinsp;N latitude and 74.08&amp;deg;&amp;thinsp;E to 75.09&amp;deg;&amp;thinsp;E longitude with an area of 10,215&amp;thinsp;km<sup>2</sup>. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32&amp;thinsp;% (6468.70&amp;thinsp;sq&amp;thinsp;km) followed by agriculture 12.88&amp;thinsp;% (1315.31&amp;thinsp;sq.&amp;thinsp;km), sparse forest 10.59&amp;thinsp;% (1081.37&amp;thinsp;sq.&amp;thinsp;km), open land 6.09&amp;thinsp;% (622.37&amp;thinsp;sq.&amp;thinsp;km), horticulture plantation and least was forest plantation (1.07&amp;thinsp;%). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non-vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

scholarly journals A study on landuse and landcover classification using microwave data in Joida taluk of Uttara Kannada district, Karnataka

2020 ◽  
Vol 12 (1) ◽  
pp. 9-12
Author(s):  
Arjun G. Koppad ◽  
Syeda Sarfin ◽  
Anup Kumar Das
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Data Sets ◽  
Radiometric Calibration ◽  
Ground Truth Data ◽  
Dense Forest ◽  
Sar Data

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha).   Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76.  Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

scholarly journals Clasificación de usos y cubiertas del suelo y análisis de cambios en los alrededores de la Reserva Ecológica Manglares Churute (Ecuador) mediante una serie de imágenes Sentinel-1

2020 ◽  
pp. 131
Author(s):  
D.A. Vélez-Alvarado ◽  
J. Álvarez-Mozos
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Management Practices ◽  
Performance Metrics ◽  
Ground Truth ◽  
Training Dataset ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Ground Truth Data ◽  
Buffer Area

<p class="p1">Management practices adopted in protected natural areas often ignore the relevance of the territory surrounding the actual protected land (buffer area). These areas can be the source of impacts that threaten the protected ecosystems. This paper reports a case study where a time series of Sentinel-1 imagery was used to classify the land-use/land-cover and to evaluate its change between 2015 and 2018 in the buffer area around the Manglares Churute Ecological Reserve (REMCh) in Ecuador. Sentinel-1 scenes were processed and ground-truth data were collected consisting of samples of the main land-use/land-cover classes in the region. Then, a Random Forests (RF) classification algorithm was built and optimized, following a five-fold cross validation scheme using the training dataset (70% of the ground truth). The remaining 30% was used for validation, achieving an Overall Accuracy of 84%, a Kappa coefficient of 0.8 and successful class performance metrics for the main crops and land use classes. Results were poorer for heterogeneous and minor classes, nevertheless the performance of the classification was deemed sufficient for the targeted change analysis. Between 2015 and 2018, an increase in the area covered by intensive land uses was evidenced, such as shrimp farms and sugarcane, which replaced traditional crops (mainly rice and banana). Even though such changes only affected the land area around the natural reserve, they might affect its water quality due to the use of fertilizers and pesticides that easily. Therefore, it is recommended that these buffer areas around natural protected areas be taken into account when designing adequate environmental protection measures and polices.</p>

scholarly journals The Assessment of land use land cover and carbon sequestration in forests of Joida Taluk of Uttar Kannada district using Remote sensing technique

2020 ◽  
Vol 12 (3) ◽  
pp. 344-348
Author(s):  
A. G. Koppad ◽  
Pallavi. P Banavasi ◽  
Syeda Sarfin
Keyword(s):  
Land Use ◽  
Carbon Sequestration ◽  
Land Cover ◽  
Growth Parameters ◽  
Tree Height ◽  
Total Biomass ◽  
Land Use Land Cover ◽  
Ground Truth Data ◽  
Dense Forest ◽  
Scrub Forest

The study was conducted in Joida Taluk of Uttar Kannada district, Karnataka to assess the land use land cover (LULC) and carbon sequestration of the forest during the year 2019-20. The ground truth data for different LULC was collected using GPS, and data was used for classification of IRS LISS 4 data using maximum likelihood classifier in ERDAS imagine software. The sample plots of 23.2 m X 23.2 m were laid out randomly in forests and growth parameters (tree height and diameter) were recorded, and biomass was estimated using the standard formula. There are eight LULC classes’ viz., dense forest, moderately dense forest, open/sparse forest, scrub forest, agriculture, settlement, horticulture plantation and waterbody. The overall accuracy of the classification was 97.09%. The total biomass in Joida Taluk from four forest classes (dense forest, moderately dense forest, open/sparse forest and scrub forest) was 44.16 million m3 and carbon sequestered was 15.57 million tonnes. The NDVI values ranging from -0.23 to 0.74, indicating a higher value for dense forest. Based on this study, it is concluded that forests have potential in carbon sequestration, which in turn helps in mitigating the climate change.

scholarly journals An Assessment of Changes the Land Use Land Cover and Impact of the It Park at Talwade Pune On the Nearby Villages

2020 ◽  
pp. 172-179
Author(s):  
Ujjwala Khare ◽  
Prajakta Thakur
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Rapid Growth ◽  
Urban Areas ◽  
Satellite Images ◽  
Ground Truth ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Tertiary Sector ◽  
Land Cover Maps

<p>The expansion of urban areas is common in metropolitan cities in India. Pune also has experienced rapid growth in the fringe areas of the city. This is mainly on account of the development of the Information Technology (IT) Parks. These IT Parks have been established in different parts of Pune city. They include Hinjewadi, Kharadi, Talwade and others like the IT parks in Magarpatta area. The IT part at Talwade is located to close to Pune Nashik Highway has had an impact on the villages located around it. The surrounding area includes the villages of Talwade, Chikhli, Nighoje, Mahalunge, Khalumbre and Sudumbre.</p> <p>The changes in the land use that have occurred in areas surrounding Talwade IT parks during the last three decades have been studied by analyzing the LANDSAT images of different time periods. The satellite images of the 1992, 2001 and 2011 were analyzed to detect the temporal changes in the land use and land cover.</p> <p>This paper attempts to study the changes in land use / land cover which has taken place in these villages in the last two decades. Such a study can be done effectively with the help of remote sensing and GIS techniques. The tertiary sector has experienced a rapid growth especially during the last decade near the IT Park. The occupation structure of these villages is also related to the changes due to the development of the IT Park.</p> <p>The land use of study area has been analysed using the ground truth applied to the satellite images at decadal interval. Using the digital image processing techniques, the satellite images were then classified and land use / land cover maps were derived. The results show that the area under built-up land has increased by around 14 per cent in the last 20 years. On the contrary, the land under agriculture, barren, pasture has decreased significantly.</p>

scholarly journals POI Mining for Land Use Classification: A Case Study

2020 ◽  
Vol 9 (9) ◽  
pp. 493 ◽  
Author(s):  
Renato Andrade ◽  
Ana Alves ◽  
Carlos Bento
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Smart Cities ◽  
Ground Truth ◽  
Land Use Classification ◽  
Data Types ◽  
Ground Truth Data ◽  
Functional Roles ◽  
Artificial Neural Network Ann

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

scholarly journals Assessing the Spatio-Temporal Pattern of Land Use and Land Cover Changes in Osun Drainage Basin, Nigeria

2019 ◽  
Vol 12 (1-2) ◽  
pp. 41-50 ◽  
Author(s):  
Eniola Damilola Ashaolu ◽  
Jacob Funso Olorunfemi ◽  
Ifatokun Paul Ifabiyi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Temporal Pattern ◽  
Forest Cover ◽  
Drainage Basin ◽  
Anthropogenic Activities ◽  
Satellite Image ◽  
Land Use Land Cover ◽  
Lulc Change ◽  
Spatio Temporal

Abstract Over the years, Osun drainage basin has witnessed tremendous increase in population, and urbanization that have changed the landscape of the area. This study evaluated the spatio-temporal pattern of land use/land cover change (LULC) in the study area, and made hydrological inferences. Landsat imageries were acquired from USGS-EROS satellite image database for the period 1984, 2000 and 2015, while the Digital Elevation Model (DEM) was obtained from Shuttle Radar Topography Mission (SRTM) of the National Aeronautics and Space Agency (NASA). Supervised image classification using the Maximum Likelihood Algorithm in Erdas Imagine was adopted to classified the land use/land cover of the study area into seven classes. Elevation, aspect and slope of the study area were processed from DEM using ArcGIS. Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS was used to simulate the basin future LULC change, using change driving factors of population, elevation, aspect and slope of the study area. There was about 234% increase in built up areas and 89.22% in crop/shrubs between 1984 and 2015. The most significant decrease in LULC occurred in forest (58.75%) and wetland (84.69%) during this period. The predicted future LULC change suggests that only about 12% of the basin will remain under forest cover by the year 2046. The results underscored the increasing anthropogenic activities in the basin that influenced recharge rate, surface runoff, incidences of soil erosion, etc., in Osun drainage basin. The planting of the lost native trees was recommended for the sustainability of the basin’s ecosystem.

scholarly journals Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988–2018)

2018 ◽  
Vol 10 (12) ◽  
pp. 4433 ◽  
Author(s):  
Iman Rousta ◽  
Md Sarif ◽  
Rajan Gupta ◽  
Haraldur Olafsson ◽  
Manjula Ranagalage ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Heat Island ◽  
Spatiotemporal Pattern ◽  
Heat Island ◽  
Land Use Land Cover ◽  
Metropolitan City ◽  
Open Land ◽  
Urban Heat ◽  
Normal Difference

This article summarized the spatiotemporal pattern of land use/land cover (LU/LC) and urban heat island (UHI) dynamics in the Metropolitan city of Tehran between 1988 and 2018. The study showed dynamics of each LU/LC class and their role in influencing the UHI. The impervious surface area expanded by 286.04 (48.27% of total land) and vegetated land was depleted by 42.06 km2 (7.10% of total land) during the period of 1988–2018. The mean land surface temperature (LST) has enlarged by approximately 2–3 °C at the city center and 5–7 °C at the periphery between 1988 and 2018 based on the urban–rural gradient analysis. The lower mean LST was experienced by vegetation land (VL) and water body (WB) by approximately 4–5 °C and 5–7 °C, respectively, and the higher mean LST by open land (OL) by 7–11 °C than other LU/LC classes at all time-points during the time period, 1988–2018. The magnitude of mean LST was calculated based on the main LU/LC categories, where impervious land (IL) recorded the higher temperature difference compared to vegetation land (VL) and water bodies (WB). However, open land (OL) recorded the highest mean LST differences with all the other LU/LC categories. In addition to that, there was an overall negative correlation between LST and the normal difference vegetation index (NDVI). By contrast, there was an overall positive correlation between LST and the normal difference built-up index (NDBI). This article, executed through three decadal change analyses from 1988 to 2018 at 10-year intervals, has made a significant contribution to delineating the long records of change dynamics and could have a great influence on policy making to foster environmental sustainability.

scholarly journals Assessment of Land use /Land cover changes using Landsat data series: A case study of Chandauli district

2020 ◽  
Vol 66 (3) ◽  
pp. 298-305
Author(s):  
Kaushalendra Prakash Goswami ◽  
◽  
Sushil Kumar Yadav ◽  
Himanshu Shekher ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Water Bodies ◽  
Land Cover Changes ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Dense Forest ◽  
Open Forest ◽  
Classi Cation

The rapid growth of population, urbanization, economic activities and natural phenomena have affected and simultaneously changed the land use land cover pattern. The main aim of this study is to gain a quantitative understanding of land use land cover changes in Chandauli district from 2000 to 2019. The maximum likelihood supervised classication in ERDAS imagine and ARC GIS software is applied in this study for the preparation of land use land cover maps and analysis of the pattern of land cover through satellite data for the years 2000, 2010 and 2019. The classication of land use land cover is divided into nine major classes i.e. water bodies, sand, cropland, built-up land, fallow land, wasteland, dense forest, open forest and scrub forest. Change detection analysis was also included in this analysis. The general pattern of LULC in this area includes an expansion of Fallow land (18.31 per cent), built-up land (13.43 per cent), open forest and water bodies as well as a reduction in the wasteland (12.59 percent) and dense forest areas in the reference period (2000-2019). The result also indicates that the dominating forest cover exists in southern Chandauli district. The mapping of land use land cover classes is also helpful in the study of change detection and natural resource management.

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