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 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 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 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 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.

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 MONITORING AND PREDICTING LAND USE-LAND COVER (LULC) CHANGES WITHIN AND AROUND KRAU WILDLIFE RESERVE (KWR) PROTECTED AREA IN MALAYSIA USING MULTI-TEMPORAL LANDSAT DATA

2018 ◽  
Vol 5 (1) ◽  
pp. 17 ◽  
Author(s):  
Jibrin Gambo ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Nur Shafira Nisa Shaharum ◽  
Fauzul Azim Zainal Abidin ◽  
Mohd Taufik Abdul Rahman
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Protected Area ◽  
Local Community ◽  
Current Status ◽  
Kappa Statistics ◽  
Land Use Land Cover ◽  
Lulc Changes ◽  
Dense Forest ◽  
Wildlife Reserve

Natural and anthropogenic activities surrounding a Protected Area (PA) may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, there is need of environmental change monitoring within and around PA because of its significant values to ecosystem at conservation scales. Effects and influences of local community within and around PA turn into the major problems for natural resource and conservations management as well as environmental impact assessment. Ascertaining the complex interface in relations to changes and its driving factors over period of time within and around PA is significant in order to predict future LULC changes, build alternative scenarios and serve as tools for decision making.  The main objective of this work was to evaluate temporal change detection and prediction of LULC as well as the trends of changes from 1989 to 2016 within and around Krau Wildlife Reserve (KWR).  The cloud issues were mitigated by producing cloud free image and object-based image analysis (OBIA) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. The comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. The simulation model of Land Change Modeler (LCM) was utilized with digital elevation model (DEM) and past LULC maps to project future LULC pattern using Markov chain. The predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of KWR. The study is important for the conservation of habitat species and monitoring the current status of the KWR

scholarly journals Enset-Based land use land cover change detection and its impact on soil erosion in Meki river watershed, Western Lake Ziway Sub-Basin, Central Rift Valley of Ethiopia

2020 ◽  
Author(s):  
Alemu Beyene Woldesenbet ◽  
Sebsebe Demisew Wudmatas ◽  
Mekuria Argaw Denboba ◽  
Azage Gebreyohannes Gebremariam
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Land Cover Change ◽  
Soil Loss ◽  
Land Use Land Cover ◽  
Ground Truth Data ◽  
River Watershed ◽  
Land Use Systems ◽  
Land Use System

Abstract Background Water erosion, upland degradation and deforestation are key environmental problems in Meki river watershed. . The study assessed the land use land cover change (LULCC) over the last 30 years, examined the contribution of the indigenous Enset-Based land use system (EBLUS) which was not studied so far in reducing soil erosion and preventing Lake Ziway from sedimentation. Based on the outcomes, the research recommended appropriate management interventions based on priority mapped to sustainably manage the watershed. GPS based Ground truth data sampling and collection, Geo-statistical interpolation and RUSLE model were applied for soil erosion modeling. The LULCC detection and analysis was conducted to generate the spatial inputs using ERDAS Imagine 2014. Result Meki river watershed has 2110.4 km² of area which is dominantly covered by cultivated land use system (41.5%), Enset-Based land use system (EBLUS)(10.65%), Bush and Chat land use system (25.6%), Forest and plantations land use system (14.14%), built up (7.4%) and water bodies (0.75%). Severity class of High to severe range (18-125tha -1 yr -1 ) recorded in the sub-watersheds irrespective of the land use systems and facing sever degradation problem that increase in soil loss in all land use systems from 1987 to 2017. The average soil loss of 30.5tha -1 yr-1 and 31.905tha-1yr-1 verified from Enset growing zones and non-Enset growing zones of the watershed respectively. Conclusion Enset-Based land use system (EBLUS) saves significant amount of soil despite the steepness of the slopes of the Enset growing zones of the watershed. Hence, expansion of EBLUS can contribute in sustaining Lake Ziway by reducing soil loss rate and sedimentation problem for ecological sustainability of the watershed. Therefore, separate land use policy and awareness creation are mandatory for such EBLUS expansion, integrated watershed management and conservation of the natural environment in the watershed.

scholarly journals Assessing the effects of climate and land use land cover changes on recent carbon storage in terrestrial ecosystem using model-satellite approach over Wallonia, Belgium

2021 ◽  
Author(s):  
Arpita Verma ◽  
Louis Francois ◽  
Ingrid Jacquemin ◽  
Merja Tölle ◽  
Huan Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Carbon Sequestration ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Storage ◽  
Terrestrial Ecosystem ◽  
Land Use Land Cover ◽  
Vegetation Model ◽  
Dynamic Vegetation Model

&lt;p&gt;The use of a dynamic vegetation model, CARAIB, to estimate carbon sequestration from land-use and land-cover change (LULCC) offers a new approach for spatial and temporal details of carbon sink and for terrestrial ecosystem productivity affected by LULCC. Using the remote sensing satellite imagery (Landsat) we explore the role of land use land cover change (LULCC) in modifying the terrestrial carbon sequestration. We have constructed our LULCC data over Wallonia, Belgium, and compared it with the ground-based statistical data. However, the results from the satellite base LULCC are overestimating the forest data due to the single isolated trees. We know forests play an important role in mitigating climate change by capturing and sequestering atmospheric carbon. Overall, the conversion of land and increase in urban land can impact the environment. Moreover, quantitative estimation of the temporal and spatial pattern of carbon storage with the change in land use land cover is critical to estimate. The objective of this study is to estimate the inter-annual variability in carbon sequestration with the change in land use land cover. Here, with the CARAIB dynamic vegetation model, we perform simulations using remote sensing satellite-based LULCC data to analyse the sensitivity of the carbon sequestration. We propose a new method of using satellite and machine learning-based observation to reconstruct historical LULCC. It will quantify the spatial and temporal variability of land-use change during the 1985-2020 periods over Wallonia, Belgium at high resolution. This study will give the space to analyse past information and hence calibrate the dynamic vegetation model to minimize uncertainty in the future projection (until 2070). Further, we will also analyse the change in other climate variables, such as CO&lt;sub&gt;2&lt;/sub&gt;, temperature, etc. Overall, this study allows us to understand the effect of changing land-use patterns and to constrain the model with an improved input dataset which minimizes the uncertainty in model estimation.&lt;/p&gt;

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