scholarly journals Historical and Future Spatial and Temporal Changes in Land Use and Land Cover in the Little Ruaha River Catchment, Tanzania

2020 ◽  
Vol 08 (02) ◽  
pp. 76-96
Author(s):  
Nyemo A. Chilagane ◽  
Japhet J. Kashaigili ◽  
Edmund Mutayoba
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Temporal Changes ◽  
River Catchment ◽  
Spatial And Temporal Changes

scholarly journals Anthropic transformation in the Gurupi river basin, eastern Amazon

2019 ◽  
Vol 10 (3) ◽  
pp. 212-235
Author(s):  
Fabiana da Silva Pereira ◽  
Ima Célia Guimarães Vieira
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Forest Cover ◽  
Temporal Changes ◽  
Amazon Region ◽  
Digital Data ◽  
Forest Protection ◽  
Spatial And Temporal Changes

The objective of this paper was to evaluate the degree of anthropic transformation of a river basin in the Amazon region. We used the digital data of the TerraClass Project to calculate the Anthropic Transformation Index - ATI. In order to verify spatial and temporal changes along a decade in the Gurupi river basin, we used the database of the years 2004 and 2014. The results showed an increase of anthropic changes in the basin over a decade, as a result of forest cover conversion into agricultural and pastures areas. Although the Gurupi river basin remains at a regular level of degradation after a decade, the intensification of land use and land cover change is a threat to the few rainforest remnants of the river basin, which can lead the region to the next level of degradation, if effective forest protection, conservation and restoration actions are not implemented in the region.  

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 Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

2020 ◽  
Vol 12 (24) ◽  
pp. 4048
Author(s):  
Yrneh Ulloa-Torrealba ◽  
Reinhold Stahlmann ◽  
Martin Wegmann ◽  
Thomas Koellner
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Historical Maps ◽  
River Catchment ◽  
Main River ◽  
Object Based

The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.

ASSESSMENT OF LAND COVER CHANGES DUE TO ANTHROPOGENIC CAUSES IN THE MOUNTAINOUS AREA OF ISHKOMAN WATERSHED, GILGIT, PAKISTAN

2021 ◽  
Vol 14(63) (1) ◽  
pp. 25-46
Author(s):  
Komal NABI ◽  
Karamat ALI ◽  
Muhammad Irfan ASHRAF ◽  
Areeba Binte IMRAN ◽  
Naveed AHMAD
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Temporal Changes ◽  
Population Increase ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Community Perception ◽  
Land Type ◽  
Land Use Dynamics

Remote Sensing (RS) provides the best ways to monitor temporal changes and to understand land use dynamics. Remote sensing analysis can be further enhanced when community perception regarding major drivers of change is integrated. The present study was an attempt to assess the land use land cover changes in the Ishkoman watershed in the Ghizer district. The study explored Landsat-5 and Landsat-8 images to assess the LULC dynamics from 1998 to 2018, and also used questionnaires for community perception regarding LULC changes in the past two decades. Supervised classification was used to monitor changes between 1998 and 2018 and the maximum likelihood technique was used to categorize the pixels into six classes: vegetation/forest area, bare rocks, water bodies, glaciers/snow area, rivers, water, and agriculture. Regarding the questionnaires, the correlation matrix and regression models were developed between independent variables (population, land type cleared, and extra land required for new family members) and dependent variables (land use dynamics factors and socio-economic variables). The results showed that all six land cover classes have shown temporal changes between 1998-2018 and the most significant change was observed in forests and pastures (which decreased from 18.7% to 5.9 %). Similarly, glaciers, water, rivers, and agriculture have changed from 13.1, 6.5, 9.3, 1.5 to 15.8, 4.0, 11.32, 3.1, respectively between 1998-2018. The largest change was observed in bare rocks which increased from 50.2 % to 60.06%. Moreover, temporal NDVI analysis showed a decrease in vegetation cover (conversion to bare rocks) between 1998-2018. The questionnaire results revealed that the highest correlation was shown between population increase and decrease in crop production (R2 = -0.348), whereas the lowest correlation was found in population increase and population access to bus stops (R2 = -0.167). Similarly, the highest correlation was found between access to roads and markets (R2 = 0.349) and dependent variable (land type cleared), whereas the lowest correlobserved in access to water resources (R2 = -0.021). The study concluded that land use land cover has been significantly changed from 1998 to 2018 in the Ishkoman Watershed. The study suggested more in-depth research to examine land use land cover changes at finer scales by using high resolution satellite imagery, and conducting details surveys regarding the underlying anthropogenic causes of land use dynamics.

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