scholarly journals THE CLASSICAL ASSUMPTION TEST TO DRIVING FACTORS OF LAND COVER CHANGE IN THE DEVELOPMENT REGION OF NORTHERN PART OF WEST JAVA

2016 ◽  
Vol XLI-B6 ◽  
pp. 205-210
Author(s):  
Nur Ainiyah ◽  
Albertus Deliar ◽  
Riantini Virtriana
Keyword(s):  
Regression Analysis ◽  
Land Cover ◽  
Land Cover Change ◽  
Binary Logistic Regression ◽  
Driving Factors ◽  
Land Cover Changes ◽  
Environment Change ◽  
Efficiency And Effectiveness ◽  
The One ◽  
Regression Tests

Land cover changes continuously change by the time. Many kind of phenomena is a simple of important factors that affect the environment change, both locally and also globally. To determine the existence of the phenomenon of land cover change in a region, it is necessary to identify the driving factors that can cause land cover change. The relation between driving factors and response variables can be evaluated by using regression analysis techniques. In this case, land cover change is a dichotomous phenomenon (binary). The BLR’s model (Binary Logistic Regression) is the one of kind regression analysis which can be used to describe the nature of dichotomy. Before performing regression analysis, correlation analysis is carried it the first. Both correlation test and regression tests are part of a statistical test or known classical assumption test. From result of classical assumption test, then can be seen that the data used to perform analysis from driving factors of the land cover changes is proper with used by BLR’s method. Therefore, the objective of this research is to evaluate the effectiveness of methods in assessing the relation between driving factors of land cover change that assumed can affect to land cover change phenomena. This research will use the classical assumed test of multiple regression linear analysis, showing that BLR method is efficiency and effectiveness solution for researching or studying in phenomenon of land cover changes. So it will to provide certainty that the regression equation obtained has accuracy in estimation, unbiased and consistent.

scholarly journals THE CLASSICAL ASSUMPTION TEST TO DRIVING FACTORS OF LAND COVER CHANGE IN THE DEVELOPMENT REGION OF NORTHERN PART OF WEST JAVA

2016 ◽  
Vol XLI-B6 ◽  
pp. 205-210
Author(s):  
Nur Ainiyah ◽  
Albertus Deliar ◽  
Riantini Virtriana
Keyword(s):  
Regression Analysis ◽  
Land Cover ◽  
Land Cover Change ◽  
Binary Logistic Regression ◽  
Driving Factors ◽  
Land Cover Changes ◽  
Environment Change ◽  
Efficiency And Effectiveness ◽  
The One ◽  
Regression Tests

Land cover changes continuously change by the time. Many kind of phenomena is a simple of important factors that affect the environment change, both locally and also globally. To determine the existence of the phenomenon of land cover change in a region, it is necessary to identify the driving factors that can cause land cover change. The relation between driving factors and response variables can be evaluated by using regression analysis techniques. In this case, land cover change is a dichotomous phenomenon (binary). The BLR’s model (Binary Logistic Regression) is the one of kind regression analysis which can be used to describe the nature of dichotomy. Before performing regression analysis, correlation analysis is carried it the first. Both correlation test and regression tests are part of a statistical test or known classical assumption test. From result of classical assumption test, then can be seen that the data used to perform analysis from driving factors of the land cover changes is proper with used by BLR’s method. Therefore, the objective of this research is to evaluate the effectiveness of methods in assessing the relation between driving factors of land cover change that assumed can affect to land cover change phenomena. This research will use the classical assumed test of multiple regression linear analysis, showing that BLR method is efficiency and effectiveness solution for researching or studying in phenomenon of land cover changes. So it will to provide certainty that the regression equation obtained has accuracy in estimation, unbiased and consistent.

Historical and Future Land-Cover Change in a Municipality of Ghana

2011 ◽  
Vol 15 (9) ◽  
pp. 1-26 ◽  
Author(s):  
Emmanuel M. Attua ◽  
Joshua B. Fisher
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Land Tenure ◽  
Urban Areas ◽  
Poverty Reduction ◽  
Urban Expansion ◽  
Political Stability ◽  
Land Cover Changes ◽  
Natural Ecosystems ◽  
Land Administration

Abstract Urban land-cover change is increasing dramatically in most developing nations. In Africa and in the New Juaben municipality of Ghana in particular, political stability and active socioeconomic progress has pushed the urban frontier into the countryside at the expense of the natural ecosystems at ever-increasing rates. Using Landsat satellite imagery from 1985 to 2003, the study found that the urban core expanded by 10% and the peri-urban areas expanded by 25% over the period. Projecting forward to 2015, it is expected that urban infrastructure will constitute 70% of the total land area in the municipality. Giving way to urban expansion were losses in open woodlands (19%), tree fallow (9%), croplands (4%), and grass fallow (3%), with further declines expected for 2015. Major drivers of land-cover changes are attributed to demographic changes and past microeconomic policies, particularly the Structural Adjustment Programme (SAP); the Economic Recovery Programme (ERP); and, more recently, the Ghana Poverty Reduction Strategy (GPRS). Pluralistic land administration, complications in the land tenure systems, institutional inefficiencies, and lack of capacity in land administration were also key drivers of land-cover changes in the New Juaben municipality. Policy recommendations are presented to address the associated challenges.

Land Cover Change: Does it affect temperature variability?

2021 ◽  
Author(s):  
Aristoklis Lagos ◽  
Stavroula Sigourou ◽  
Panayiotis Dimitriadis ◽  
Theano Iliopoulou ◽  
Demetris Koutsoyiannis
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Urban Areas ◽  
Temperature Increase ◽  
Temperature Variability ◽  
Land Cover Changes ◽  
The Earth ◽  
Different Types ◽  
General Dynamics ◽  
Global Datasets

<p>Changes in the land cover occur all the time at the surface of the Earth both naturally and anthropogenically. In the last decades, certain types of land cover change, including urbanization, have been correlated to local temperature increase, but the general dynamics of this relationship are still not well understood. This work examines whether land cover is a parameter affecting temperature increase by employing global datasets of land cover change, i.e. the Historical Land-Cover Change Global Dataset, and daily temperature from the NOAA database. We thoroughly investigate the temperature variability and its possible correlation to the different types of land-cover changes. A comparison is specifically made between the rate of temperature increase measured in urban areas, and the same rate measured in nearby non-urban areas.</p>

Long-term land cover change in Zambia: An assessment of driving factors

2019 ◽  
Vol 697 ◽  
pp. 134206 ◽  
Author(s):  
Darius Phiri ◽  
Justin Morgenroth ◽  
Cong Xu
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Driving Factors

Land Cover Change Simulations in Yangon Under Several Scenarios of Flood and Earthquake Vulnerabilities with Master Plan

2018 ◽  
Vol 13 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Tanakorn Sritarapipat ◽  
◽  
Wataru Takeuchi
Keyword(s):  
High Risk ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Areas ◽  
Urban Expansion ◽  
Land Cover Changes ◽  
Master Plan ◽  
Economic Area ◽  
Urban Planning And Management ◽  
Multiple Scenarios

Yangon is the largest city and major economic area in Myanmar. However, it is considered to have a high risk of floods and earthquakes. In order to mitigate future flood and earthquake damage in Yangon, land cover change simulations considering flood and earthquake vulnerabilities are needed to support urban planning and management. This paper proposes land cover change simulations in Yangon from 2020 to 2040 under various scenarios of flood and earthquake vulnerabilities with a master plan. In our methodology, we used a dynamic statistical model to predict urban expansion in Yangon from 2020 to 2040. We employed a master plan as the future dataset to enhance the prediction of urban expansion. We applied flood and earthquake vulnerabilities based on multi-criteria analysis as the areas vulnerable to disaster. We simulated land cover changes from 2020 to 2040 considering the vulnerable areas with a master plan for multiple scenarios. The experiments indicated that by using a master plan, some of the predicted urban areas are still located in areas highly vulnerable to floods and earthquakes. By integrating the prediction of urban expansion with flood and earthquake vulnerabilities, the predicted urban areas can effectively avoid areas highly vulnerable to floods and earthquakes.

scholarly journals An Assessment of Land Use and Land Cover Changes and Its Impact on the Surface Water Quality of the Crocodile River Catchment, South Africa

2021 ◽  
Author(s):  
Nde Samuel Che ◽  
Sammy Bett ◽  
Enyioma Chimaijem Okpara ◽  
Peter Oluwadamilare Olagbaju ◽  
Omolola Esther Fayemi ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Surface Water ◽  
Land Cover ◽  
Land Cover Change ◽  
Physicochemical Parameters ◽  
Anthropogenic Activities ◽  
Water Bodies ◽  
Land Cover Changes ◽  
River Catchment

The degradation of surface water by anthropogenic activities is a global phenomenon. Surface water in the upper Crocodile River has been deteriorating over the past few decades by increased anthropogenic land use and land cover changes as areas of non-point sources of contamination. This study aimed to assess the spatial variation of physicochemical parameters and potentially toxic elements (PTEs) contamination in the Crocodile River influenced by land use and land cover change. 12 surface water samplings were collected every quarter from April 2017 to July 2018 and were analyzed by inductive coupled plasma spectrometry-mass spectrometry (ICP-MS). Landsat and Spot images for the period of 1999–2009 - 2018 were used for land use and land cover change detection for the upper Crocodile River catchment. Supervised approach with maximum likelihood classifier was used for the classification and generation of LULC maps for the selected periods. The results of the surface water concentrations of PTEs in the river are presented in order of abundance from Mn in October 2017 (0.34 mg/L), followed by Cu in July 2017 (0,21 mg/L), Fe in April 2017 (0,07 mg/L), Al in July 2017 (0.07 mg/L), while Zn in April 2017, October 2017 and April 2018 (0.05 mg/L). The concentrations of PTEs from water analysis reveal that Al, (0.04 mg/L), Mn (0.19 mg/L) and Fe (0.14 mg/L) exceeded the stipulated permissible threshold limit of DWAF (< 0.005 mg/L, 0.18 mg/L and 0.1 mg/L) respectively for aquatic environments. The values for Mn (0.19 mg/L) exceeded the permissible threshold limit of the US-EPA of 0.05 compromising the water quality trait expected to be good. Seasonal analysis of the PTEs concentrations in the river was significant (p > 0.05) between the wet season and the dry season. The spatial distribution of physicochemical parameters and PTEs were strongly correlated (p > 0.05) being influenced by different land use type along the river. Analysis of change detection suggests that; grassland, cropland and water bodies exhibited an increase of 26 612, 17 578 and 1 411 ha respectively, with land cover change of 23.42%, 15.05% and 1.18% respectively spanning from 1999 to 2018. Bare land and built-up declined from 1999 to 2018, with a net change of - 42 938 and − 2 663 ha respectively witnessing a land cover change of −36.81% and − 2.29% respectively from 1999 to 2018. In terms of the area under each land use and land cover change category observed within the chosen period, most significant annual change was observed in cropland (2.2%) between 1999 to 2009. Water bodies also increased by 0.1% between 1999 to 2009 and 2009 to 2018 respectively. Built-up and grassland witness an annual change rate in land use and land cover change category only between 2009 to 2018 of 0.1% and 2.7% respectively. This underscores a massive transformation driven by anthropogenic activities given rise to environmental issues in the Crocodile River catchment.

scholarly journals AN ASSESSMENT OF LAND COVER CHANGE DYNAMICS OF GAJA CYCLONE IN COASTAL TAMIL NADU, INDIA USING SENTINEL 1 SAR DATASET

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 323-329
Author(s):  
K. Nivedita Priyadarshini ◽  
V. Sivashankari ◽  
S. Shekhar
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Tropical Cyclones ◽  
Tamil Nadu ◽  
Indian Subcontinent ◽  
Sensor Data ◽  
Land Cover Changes ◽  
Cyclonic Storm ◽  
High Death ◽  
Research Findings

<p><strong>Abstract.</strong> Land cover change is a dynamic phenomenon addressing environmental issues including natural calamities. Recent advancements in geospatial technology and availability of remote sensor data have fostered monitoring and mapping of land cover changes more precisely. Remote sensing is widely used where emerging research findings are focused mainly on coastal hazard studies. Tropical cyclones being an extreme weather event are more powerful and hazardous to southern parts of the Indian subcontinent. Aftermath of the cyclone is extreme causing land cover changes like defoliation, water logging, destruction of cultivable lands, plantations shrub vegetation, dissolving salt pans etc. The tropical cyclones are fierce to devastate the coastal districts of Tamil Nadu and make it a prey to these cyclones. In this paper, an attempt has been made to assess the pre and post cyclonic land cover change by utilizing potential microwave Synthetic Aperture Radar (SAR) dataset. The study portrays the occurrence of a severe cyclonic storm named <q>Gaja</q> that was formed over Bay of Bengal which hit Tamil Nadu on 15<sup>th</sup> of November 2018 causing high death toll and demolition. The study focuses on the pre and post damage assessment provoked by Gaja cyclone. For analysis, a methodical procedure was followed by utilizing the Sentinel 1 SAR dataset. Random Forest (RF) classifier approach was incorporated for mapping land cover types as it reduces the variance among the classes thus yielding accurate predictions. Results demonstrate that classified imagery using dual polarization SAR dataset outperforms well for RF classifier thus escalating the overall accuracy.</p>

scholarly journals Monitoring Land Cover Changes in the Tropics using Satellite Remote Sensing Data

2019 ◽  
Vol 8 (12) ◽  
pp. 3631-3636
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Satellite Data ◽  
Land Cover Changes ◽  
Classification Methods ◽  
Land Covers ◽  
The Tropics ◽  
Land Cover Change Detection

Changes in land cover are inevitable phenomena that occur in all parts of the world. Land cover changes can occur due to natural phenomena that include runoff, soil erosion and sedimentation besides man-made phenomena that include deforestation, urbanization and conversion of land covers to suit human needs. Several works on change detection have been carried out elsewhere, however there were lack of effort in analyzing the issues that affect the performance of existing change detection techniques. The study presented in this paper aims to detect changes of land covers by using remote sensing satellite data. The study involves detection of land cover changes using remote sensing techniques. This makes use satellite data taken at different times over a particular area of interest. The data has resolution of 30 m and records surface reflectance at approximately 0.4 to 0.7 micrometers wavelengths. The study area is located in Selangor, Malaysia and occupied with tropical land covers including coastal swamp water, sediment plumes, urban, industry, water, bare land, cleared land, oil palm, rubber and coconut. Initially, region of interests (ROI) were drawn on each of the land covers in order to extract the training pixels. Landsat satellite bands 1, 2, 3, 4, 5 and 7 were then used as the input for the three supervised classification methods namely Support Vector Machine (SVM), Maximum Likelihood (ML) and Neural Network (NN). Different sizes of training pixels were used as the input for the classification methods so that the performance can be better understood. The accuracy of the classifications was then assessed by analyzing the classifications with a set of reference pixels using a confusion matrix. The classification methods were then used to identify the conversion of land cover from year 2000 to 2005 within the study area. The outcomes of the land cover change detection were reported in terms quantitative and qualitative analyses. The study shows that SVM gives a more accurate and realistic land cover change detection compared to ML and NN mainly due to not being much influenced by the size of the training pixels. The findings of the study serve as important input for decision makers in managing natural resources and environment in the tropics systematically and efficiently.

Sign in / Sign up

Export Citation Format

Share Document