scholarly journals Assessing Land Use and Land Cover Change in River Beas Floodplain, Punjab

2020 ◽  
Vol 15 (1) ◽  
pp. 52-58
Author(s):  
Gursewak Singh Brar ◽  
Vishwa B.S. Chandel ◽  
Karanjot Kaur Brar ◽  
◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
River Channels ◽  
Human Environment ◽  
Barren Land ◽  
Natural Land ◽  
Multi Temporal ◽  
River Beas

Floodplains are the most fragile ecosystems of the world which attracted the humans since the dawn of civilizations. Due to their resource enrichment, these remained center of attraction to fulfill the socio-economic needs of people. As a result, the natural land cover of these floodplains are under the influence of human induced activities. River Beas Floodplain of Punjab has also witnessed such changes. Human intervention in these landscapes has depleted natural wealth and has altered its land use. Construction of upstream dam and artificial embankments and diversion of water through canals further paved the ways for intensification of land use changes. The outcome of these human actions is that wetlands, barren land, and river channels has reduced. On the other hand, agriculture and settlements recorded a sharp increase in recent decades. The growth of agricultural area and human settlements are putting pressure on the natural resources and depleting the human environment relationship in the floodplain. This study utilized multi-temporal satellite data from Landsat for the classification of land use and land cover.

Use of Savitzky - Golay Filters to Minimize Multi-temporal Data Anomaly in Land use Land cover mapping

2019 ◽  
Vol 42 (4) ◽  
pp. 362-368
Author(s):  
Ram Kumar Singh ◽  
◽  
Vinay Shankar Prasad Sinha ◽  
Pawan Kumar Joshi ◽  
Manoj Kumar ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
Spectral Signature ◽  
Atmospheric Effects ◽  
Temporal Data ◽  
Land Use Land Cover ◽  
Distance Method ◽  
Multi Temporal

Land use land cover characterization and mapping have become a prerequisite in all environmental Planaing. The array of satellites deployed in the space provides multi-temporal images that can be used for the land use land cover classification. But, much often these multi-temporal images have data noise and anomaly owing to the cloud and atmospheric effects. This brings pseudo hikes and lows in data adding classification with possible errors. We present a method for the removal of data anomaly where monthly data of MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (MODIS 13Q1) was used for the classification of images over a large area encompassing the SAARC nations. MODIS multi-temporal data were filtered usinga Savitzky-Golay (S-G) algorithm which provided smoothened data and the seasonality (start, end of the season) were identified. Phenology profile curves were created for the characterization of the agriculture and forestry feature classes. The S-G filtered images and raw MODIS data phenology profile curves were compared for the eleven classes of land cover, viz., ever green needle forest, ever green broad leave, deciduous broad leave, shrub, savannas, grass, agriculture, built-up, water, snow (ice), and barren. Spectral signature separability was also compared using Euclidean spectral distance method. In conclusion, it was observed that multi-spectral S-G filtered data were more useful for the classification of agriculture and forestry classes for a larger coverage.

scholarly journals Land Use and Land Cover Classification from ETM Sensor Data : A Case Study from Tamakoshi River Basin of Nepal

2014 ◽  
Vol XL-8 ◽  
pp. 943-948
Author(s):  
U. S. Shrestha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Classification Methods ◽  
Barren Land ◽  
Grass Land ◽  
Mountain Watershed

The mountain watershed of Nepal is highly rugged, inaccessible and difficult for acquiring field data. The application of ETM sensor Data Sat satellite image of 30 meter pixel resolutions has been used for land use and land cover classification of Tamakoshi River Basin (TRB) of Nepal. The paper tries to examine the strength of image classification methods in derivation of land use and land classification. Supervised digital image classification techniques was used for examination the thematic classification. Field verification, Google earth image, aerial photographs, topographical sheet and GPS locations were used for land use and land cover type classification, selecting training samples and assessing accuracy of classification results. Six major land use and land cover types: forest land, water bodies, bush/grass land, barren land, snow land and agricultural land was extracted using the method. Moreover, there is spatial variation of statistics of classified land uses and land cover types depending upon the classification methods. <br><br> The image data revealed that the major portion of the surface area is covered by unclassified bush and grass land covering 34.62 per cent followed by barren land (28 per cent). The knowledge derived from supervised classification was applied for the study. The result based on the field survey of the area during July 2014 also verifies the same result. So image classification is found more reliable in land use and land cover classification of mountain watershed of Nepal.

scholarly journals Estimation of Peat Surface Carbon Stocks on The Semenanjung Kampar from Land Use Changes Using Landsat Multi-Temporal in 2009-2018

2019 ◽  
Vol 3 (1) ◽  
pp. 49-53
Author(s):  
Yudi Antomi ◽  
Ristalia Ristalia
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Oil Palm ◽  
Land Use Changes ◽  
Carbon Stocks ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Multi Temporal ◽  
Peat Surface

Remote sensing has advantages in terms of temporal resolution that can be used to check changes in an object at different times. The Semenanjung Kampar peatland underwent land use change after the change in PP No. 71 of 2014 became PP No. 57 of 2016 which requires companies (paper companies) to restore the ecosystem on the Semenanjung Kampar. These changes were analyzed by utilizing remote sensing technology through multi-temporal imagery.This study aims to analyze changes in peatland use on the Semenanjung Kampar in 2009, 2013 and 2018, then estimate carbon stocks from changes in peatland use. The method used is the classification of Iso Cluster unsupervised and calculation of increase and decrease in carbon stocks (Gain and Loss). Based on this research the results of the accuracy of the classification of changes in land use on the Semenanjung Kampar were 0.72 or 72%.Changes in land use on the Semenanjung Kampar occur dynamically.The dominant land change for the 2009-2013 period was shrubs which became acacia forests 89386.31 ha and bushes from 2013-2018 to oil palm plantations 57878.47 ha. Furthermore, carbon stocks in the period 2009-2013 that have increased (acces) are 8.2% acacia forest and 13% decrease in primary peat forest while the 2013-2018 period has increased, namely 8% oil palm plantation and 21% shrub decline.

scholarly journals DISTRICT-WISE CHANGE ANALYSIS OF LAND USE-LAND COVER IN DELHI TERRITORY USING REMOTE SENSING & GIS

2017 ◽  
Vol 10 (2) ◽  
pp. 201-213
Author(s):  
Surya Prakash Pattanayak ◽  
Sumant Kumar Diwakar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
High Growth ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Multi Temporal

Digital change detection is the process that helps in determining the changes associated with Land use and Land cover properties with reference to geo-referenced multi-temporal remote sensing data. It helps in identifying change between two or more dates that is uncharacterized of normal variation. This work is an attempt to assess the district-wise changes in land use/land cover in Delhi, India. The study made use of LISS -III imageries of 2008 and 2012 year. The images were classified using Maximum Likelihood classification method. The output can be useful in many applications such as Land use changes, habitat fragmentation, rate of deforestation, urban sprawl and other cumulative changes through spatial and temporal analysis. The study shows that Delhi land cover from 2008 to 2012 a major rapid changes in the landscape as there is high growth in the fallow and built up area. Agriculture land and forest area has reduced marginally and water body is showing almost stagnant condition over time.

Impacts of land use and land cover changes on evapotranspiration and runoff at Shalamulun River watershed, China

2012 ◽  
Vol 43 (1-2) ◽  
pp. 23-37 ◽  
Author(s):  
Xiaoli Yang ◽  
Liliang Ren ◽  
V. P. Singh ◽  
Xiaofan Liu ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Potential Evapotranspiration ◽  
Land Use Changes ◽  
Temporal Variations ◽  
Annual Runoff ◽  
Thematic Mapper ◽  
Land Cover Changes ◽  
Barren Land ◽  
River Watershed

The study assesses the effect of land use and land cover changes (LUCC) on evapotranspiration and runoff in the Shalamulun River watershed of 2,453 km2 located in Inner Mongolia Autonomic Region of China. First, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 1987, 2001 and 2007 are used to quantify LUCC in the watershed. A knowledge-based decision tree (K-DT) classification technique is used to detect LUCC. By comparison of post-classification change among 1987, 2001 and 2007, the results showed significant modification and conversion of land use and cover of the watershed over the 20-year period 1987–2007. The results show that the forest area underwent the greatest change, decreasing by 159.2 km2 in the study period. At the same time, the area of farmland, barren land and residential land increased by 89.5, 46.4 and 25.3 km2, respectively. Subsequently, a two-source potential evapotranspiration (PET) model is used to estimate the potential evapotranspiration response to LUCC. Finally, the influence of LUCC on annual runoff is evaluated using a statistical method. LUCC potentially caused a decrease in annual PET and runoff. Meanwhile, the land use changes resulted in spatio-temporal variations of monthly PET in the growing season (April–September).

scholarly journals Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1026
Author(s):  
Muhammad Majeed ◽  
Aqil Tariq ◽  
Muhammad Mushahid Anwar ◽  
Arshad Mahmood Khan ◽  
Fahim Arshad ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Land Use Land Cover ◽  
Barren Land ◽  
Multi Temporal ◽  
Barren Soil ◽  
Environmental Variations

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

scholarly journals Use of Remote Sensing and Geographic Information System for the Classification of Agricultural Land Uses and Land Cover in the Al-Sad Al-Adhim sub District – Iraq

2018 ◽  
Vol 225 (2) ◽  
pp. 245-273
Author(s):  
Assist. Prof. Dr. Saleem Y. Jamal
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Geographic Information System ◽  
Land Cover ◽  
Agricultural Land ◽  
Geographic Information ◽  
Land Uses ◽  
Barren Land

     Land use refers to the human activity associated with a particular area of land. The land cover refers to the pattern of appearances located on the surface of the earth. Survey, inventory, monitoring and classification of land use and land cover are a fundamental step in the land use planning process, in evaluating and comparing alternatives and in choosing the best and sustainable use of land for development, accomplishment economic and social well-being. Remote sensing and Geographic Information System provided advantages that conventional methods could not provide for surveys and monitoring of natural and human resources, and classification of agricultural land uses and land cover in the area of the Al-Sad Al-Adhim sub District – Iraq. Depending on the Anderson system and others to classify land uses and land cover, through the integration of digital interpretation with the use of Digital Image Processing (ERDAS IMAGINE) software, and visual interpretation using ArcGIS software. Classification of agricultural land use and land cover up to the third level, with over all accuracy of the map 90%. the percentage distribution of the areas shows that the agricultural lands ranked first and occupy 52%, then grassland occupies 19%, barren land is occupied 17%, urban areas and built up occupy 9% water is ranked last occupy 3% of the total area of the study area.

scholarly journals LAND USE/LAND COVER CHANGES USING MULTI-TEMPORAL SATELLITE

2020 ◽  
Vol VI-3/W1-2020 ◽  
pp. 83-90
Author(s):  
H. T. T. Nguyen ◽  
Q. T. N. Chau ◽  
A. T. Pham ◽  
H. T. Phan ◽  
P. T. X. Tran ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Natural Forest ◽  
Land Use Land Cover ◽  
Multi Temporal ◽  
Land Use Policies ◽  
Landsat Satellite ◽  
Social Economic Development

Abstract. Producing the map of land use land cover change (LULCC) at the local extent is fundamental for a variety of applications such as vegetation, forest covers, soil degradation, and global warming. Understanding the directions and spread trend of LULCC plays significant role in obtaining useful data for the local authorities in making land-use policies under the context of climate change. Dak Nong is located in the Central Highlands of Vietnam having the largest tropical forest. Over the past decades, the natural forest in the region has significantly declined due to the pressure of population growth and social-economic development. The current study analyzed the LULCC in the province over the four periods: 2005–2018, 2005–2010, 2010–2015, and 2015–2018. Information from Landsat satellite imagery captured in 2005, 2010, 2015, and 2018 was utilized to create the LULC maps and detect the land-use changes. The Random Forest (RF) was employed to categorize the images into nine different LULC classes. The study showed that classification accuracy was achieved from 72.49% to 84.55% with a kappa coefficient of 0.69 to 0.81. The findings revealed a significant decrease in the natural forest over time from 53.1% to 42.7%, 36.8%, and 34.6% in 2005, 2010, 2015, and 2018, respectively. Meanwhile, the other types of LULC showed an increase in the area over the periods, especially croplands. It was noticeable that the continuous decrease in the forest area over the years has put pressure on the natural environmental resources and generated the risk of climate change.

scholarly journals DISTRICT-WISE CHANGE ANALYSIS OF LAND USE-LAND COVER IN DELHI TERRITORY USING REMOTE SENSING & GIS

2017 ◽  
Vol 10 (2) ◽  
pp. 201-213
Author(s):  
Surya Prakash Pattanayak ◽  
Sumant Kumar Diwakar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
High Growth ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Multi Temporal

Digital change detection is the process that helps in determining the changes associated with Land use and Land cover properties with reference to geo-referenced multi-temporal remote sensing data. It helps in identifying change between two or more dates that is uncharacterized of normal variation. This work is an attempt to assess the district-wise changes in land use/land cover in Delhi, India. The study made use of LISS -III imageries of 2008 and 2012 year. The images were classified using Maximum Likelihood classification method. The output can be useful in many applications such as Land use changes, habitat fragmentation, rate of deforestation, urban sprawl and other cumulative changes through spatial and temporal analysis. The study shows that Delhi land cover from 2008 to 2012 a major rapid changes in the landscape as there is high growth in the fallow and built up area. Agriculture land and forest area has reduced marginally and water body is showing almost stagnant condition over time.

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