scholarly journals PROBABILITAS PERUBAHAN TUTUPAN LAHAN BERDASARKAN KEBERADAAN LOKASI WISATA DI WILAYAH PESISIR SARBAGITA

2019 ◽  
Vol 1 (1) ◽  
pp. 33-39
Author(s):  
Wayan Damar Windu Kurniawan
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Coastal Area ◽  
Agricultural Land ◽  
Rapid Development ◽  
Coastal Region ◽  
Limiting Factors ◽  
Landsat Images ◽  
The Road ◽  
Network Pattern

he availability of land for fulfillment of space in the Sarbagita coastal area is increasingly limited. This iscaused by the rapid development of tourism in the Sarbagita coastal region, which can eliminate a large portion ofproductive agricultural land. This study specifically examines the probability value of land cover change, especially fromnon-built up area to built up area, in the Sarbagita coastal area until 2030. Calculation of the probability of land coverchange is done through fuzzy set logic which is assessed based on 1 main parameter, namely tourist location and 2supporting parameters, namely accessibility and service facilities, and also limiting factors. The value of the fuzzymembership is taken from Landsat images from 1995 to 2015. The results show the probability of changes in land coverhas values from 0 (very low) to 0.97 (very high). This means that there is no one land that must change (value 1) fromnon-built land to being built. The probability of a high land cover change tends to follow the road network pattern.  

Amazon deforestation: Rates and patterns of land cover change and fragmentation in Pando, northern Bolivia, 1986 to 2005

2011 ◽  
Vol 35 (3) ◽  
pp. 353-374 ◽  
Author(s):  
Matthew Marsik ◽  
Forrest R. Stevens ◽  
Jane Southworth
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Decision Tree Classifier ◽  
Landsat Images ◽  
Aggregation Index ◽  
Humid Forest ◽  
The Road ◽  
Access Road ◽  
Linear Relationships ◽  
Tree Classifier

Much research has focused on deforestation in the Amazon, particularly with proximity to roads and population centers as proximate causes. This research presents the analysis of rates and patterns of land cover change in Pando, northern Bolivia, an area with most of its tropical humid forest still intact. Using a decision tree classifier, five forest/non-forest (FNF) classifications were created for 1986, 1991, 1996, 2000, and 2005 from 40 Landsat images that were preprocessed and mosaicked. FNF trajectory images were created for each date pair to indicate areas of stable forest and non-forest, and areas and rates of de/reforestation. Mean patch size, perimeter-area ratio, fractal dimension, and aggregation index metrics were calculated for the FNF trajectory images based on increasing buffer distances from road and along the main access road. In 2005, forest covered 95% of the area in Pando. Large areas of aggregated deforestation occur nearest the department capital of Cobija, along the border with Brazil, and about 50 km west and east of Cobija along the principal access road. Deforestation becomes patchier with increased distance from the population center and laterally from the road. Multiple non-linear relationships exist between the fragmentation metrics and distance from road. The results have implications for understanding and managing the spatial contiguity of these forests, which provide valuable ecological services as well as the livelihood base for many inhabitants.

scholarly journals Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

2021 ◽  
Vol 13 (16) ◽  
pp. 3337
Author(s):  
Shaker Ul Din ◽  
Hugo Wai Leung Mak
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Land Cover ◽  
Land Cover Change ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Barren Land

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.

scholarly journals Agricultural Land Cover Change in Gazipur, Bangladesh, in Relation to Local Economy Studied Using Landsat Images

2015 ◽  
Vol 04 (03) ◽  
pp. 214-223 ◽  
Author(s):  
Tarulata Shapla ◽  
Jonggeol Park ◽  
Chiharu Hongo ◽  
Hiroaki Kuze
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Agricultural Land ◽  
Local Economy ◽  
Landsat Images

scholarly journals Land use/land cover change assessment of Mohana watershed (Far-Western Nepal) using GIS and remote sensing

2021 ◽  
Vol 6 (3) ◽  
pp. 320-328
Author(s):  
Suraj Prasad Bist ◽  
Rabindra Adhikari ◽  
Raju Raj Regmi ◽  
Rajan Subedi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Forest Cover ◽  
Agricultural Land ◽  
Water Bodies ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Barren Land ◽  
Gis And Remote Sensing

The present study was conducted in the Mohana watershed of Far-western Nepal to assess land use land cover change. The study has used ArcGIS and three Landsat images - Landsat TM (1999), Landsat ETM+ (2009), and Landsat OLI (2019) – to analyze land use the land cover change of the watershed. The change matrix technique was used for change detection analysis. The study area was classified into five classes; forest, agriculture, built-up, water bodies, and barren lands. The study has found that among the five identified classes forest and build-up increased positively from 45.40 % to 51.51 % - forest cover and 11.26 % to 19. 85 % - build-up respectively. Similarly, agricultural land and water bodies initially increased but after 2009 both land cover areas decreased to 23.79 % and 0.73 % from 31.38 % and 0.97 % in 2009 respectively. Barren land decreased from 15.37% to 4.12% over the last 20 years. This study might support land-use planners and policymakers to adopt the best suitable land use management option for the Mohana watershed.

scholarly journals Urban Growth and Its Impact on Urban Land Cover Change in Akure South Local Government, Ondo State, Nigeria

2021 ◽  
pp. 32-40
Author(s):  
Dada Ibilewa ◽  
Mustapha Aliyu ◽  
Usman O. Alalu ◽  
Taiwo Hassan Abdulrasheed
Keyword(s):  
Local Government ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Growth ◽  
Agricultural Land ◽  
Urban Land ◽  
Landsat Images ◽  
Rock Outcrops ◽  
Urban Land Cover ◽  
Urban Land Cover Change

Urban Growth and its Impact on Urban land cover change in Akure South Local Government area was investigated to bridge the knowledge gap created by data deficiency on the nature, scope, and magnitude of urban threat on the land use/land cover type, most especially the agricultural land in the area. This was done through the analysis of Landsat images of three epochs from 2000 through 2010 to 2020. The processing of the satellite images was done in ArcGIS 10.8, while the analysis and 2030 projection were done in Microsoft office excel using the result from the analysis. QGIS was used to remove the scan lines error on the 2010 image. The result showed increasing urban growth (built-up area), reducing vegetation and farmlands, and increasing rock outcrops. The changes vary among the different classification characteristics. Both farmlands and vegetation increased in the first epoch and reduced in the second epoch due to man's urbanization and other socio-economic activities. The increasing change in the second epoch was higher in built-up areas while rock outcrops increased throughout the study period. The research was able to assess the magnitude of farmland and vegetation that have been converted for urban uses over time. It also proved the efficiency of Remote Sensing and GIS technology in urban growth studies.

Persistent Homology for Land Cover Change Detection

2021 ◽  
Author(s):  
Djamel Bouchaffra ◽  
Faycal Ykhlef
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Homology Theory ◽  
Urban Morphology ◽  
Detection Methods ◽  
Landsat Images ◽  
Data Repositories ◽  
Land Cover Change Detection

The need for environmental protection, monitoring, and security is increasing, and land cover change detection (LCCD) can aid in the valuation of burned areas, the study of shifting cultivation, the monitoring of pollution, the assessment of deforestation, and the analysis of desertification, urban growth, and climate change. Because of the imminent need and the availability of data repositories, numerous mathematical models have been devised for change detection. Given a sample of remotely sensed images from the same region acquired at different dates, the models investigate if a region has undergone change. Even if there is no substantial advantage to using pixel-based classification over object-based classification, a pixel-based change detection approach is often adopted. A pixel can encompass a large region, and it is imperative to determine whether this pixel (input) has changed or not. A changed image is compared to the available ground truth image for pixel-based performance evaluation. Some existing change detection systems do not take into account reversible changes due to seasonal weather effects. In other words, when snow falls in a region, the land cover is not considered as a change because it is seasonal (reversible). Some approaches exploit time series of Landsat images, which are based on the Normalized Difference Vegetation Index technique. Others evaluate built-up expansion to assess urban morphology changes using an unsupervised approach that relies on labels clustering. Change detection methods have also been applied to the field of disaster management using object-oriented image classification. Some methodologies are based on spectral mixture analysis. Other techniques invoke a similarity measure based on the evolution of the local statistics of the image between two dates for vegetation LCCD. Probabilistic approaches based on maximum entropy have been applied to vegetation and forest areas, such as Hustai National Park in Mongolia. Researchers in this field have proposed an LCCD scheme based on a feed-forward neural network using backpropagation for training. This paper invokes the new concept of homology theory, a subfield of algebraic topology. Homology theory is incorporated within a Structural Hidden Markov Model.

scholarly journals IDENTIFYING LAND USE AND LAND COVER (LULC) CHANGE FROM 2000 TO 2025 DRIVEN BY TOURISM GROWTH: A STUDY CASE IN BALI

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1621-1627
Author(s):  
A. B. Rimba ◽  
T. Atmaja ◽  
G. Mohan ◽  
S. K. Chapagain ◽  
A. Arumansawang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Growth Direction ◽  
Landsat 8 ◽  
Open Area ◽  
Landsat Images ◽  
Lulc Change ◽  
Tourism Activity ◽  
Accuracy Of Prediction

Abstract. Bali has been open to tourism since the beginning of the 20th century and is known as the first tourist destination in Indonesia. The Denpasar, Badung, Gianyar, and Tabanan (Sarbagita) areas experience the most rapid growth of tourism activity in Bali. This rapid tourism growth has caused land use and land cover (LULC) to change drastically. This study mapped the land-use change in Bali from 2000 to 2025. The land change modeller (LCM) tool in ArcGIS was employed to conduct this analysis. The images were classified into agricultural land, open area, mangrove, vegetation/forest, and built-up area. Some Landsat images in 2000 and 2015 were exploited in predicting the land use and land cover (LULC) change in 2019 and 2025. To measure the accuracy of prediction, Landsat 8 OLI images for 2019 were classified and tested to verify the LULC model for 2019. The Multi-Layer Perceptron (MLP) neural network was trained with two influencing factors: elevation and road network. The result showed that the built-up growth direction expanded from the Denpasar area to the neighbouring areas, and land was converted from agriculture, open area and vegetation/forest to built-up for all observation years. The built-up was predicted growing up to 43 % from 2015 to 2025. This model could support decision-makers in issuing a policy for monitoring LULC since the Kappa coefficients were more than 80% for all models.

scholarly journals Estimating Human Impacts on Soil Erosion Considering Different Hillslope Inclinations and Land Uses in the Coastal Region of Syria

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2786 ◽  
Author(s):  
Safwan Mohammed ◽  
Hazem G. Abdo ◽  
Szilard Szabo ◽  
Quoc Bao Pham ◽  
Imre J. Holb ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Soil Loss ◽  
Agricultural Land ◽  
Human Impacts ◽  
Coastal Region ◽  
Water Erosion ◽  
Land Uses ◽  
Erosion Processes

Soils in the coastal region of Syria (CRoS) are one of the most fragile components of natural ecosystems. However, they are adversely affected by water erosion processes after extreme land cover modifications such as wildfires or intensive agricultural activities. The main goal of this research was to clarify the dynamic interaction between erosion processes and different ecosystem components (inclination, land cover/land use, and rainy storms) along with the vulnerable territory of the CRoS. Experiments were carried out in five different locations using a total of 15 erosion plots. Soil loss and runoff were quantified in each experimental plot, considering different inclinations and land uses (agricultural land (AG), burnt forest (BF), forest/control plot (F)). Observed runoff and soil loss varied greatly according to both inclination and land cover after 750 mm of rainfall (26 events). In the cultivated areas, the average soil water erosion ranged between 0.14 ± 0.07 and 0.74 ± 0.33 kg/m2; in the BF plots, mean soil erosion ranged between 0.03 ± 0.01 and 0.24 ± 0.10 kg/m2. The lowest amount of erosion was recorded in the F plots where the erosion ranged between 0.1 ± 0.001 and 0.07 ± 0.03 kg/m2. Interestingly, the General Linear Model revealed that all factors (i.e., inclination, rainfall and land use) had a significant (p < 0.001) effect on the soil loss. We concluded that human activities greatly influenced soil erosion rates, being higher in the AG lands, followed by BF and F. Therefore, the current study could be very useful to policymakers and planners for proposing immediate conservation or restoration plans in a less studied area which has been shown to be vulnerable to soil erosion processes.

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