scholarly journals Assessment of land use change in the Thuma forest reserve region of Malawi, Africa

2021 ◽  
Author(s):  
Mihla Phiri ◽  
Harrington Nyirenda
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Sustainable Management ◽  
Landsat 8 ◽  
Forest Reserve ◽  
Future Studies ◽  
Open Forest ◽  
Closed Forest ◽  
Land Cover Maps

Abstract A study was conducted in Thuma area in central Malawi to quantify contemporary land cover and to explore the degree of land use change in the Thuma forest reserve area of Malawi by analysing and comparing satellite-derived land cover maps from 1997, 2007 and 2017. The study was carried out using Remote Sensing and Geographic Information System (GIS), focusing on analysis of Landsat 5 ETM and Landsat 8 ORI/TIRS satellite images. The classification was conducted for the following distinct classes; closed forest, open forest, shrubland, savanna grassland, agriculture fields, and water. The analysis revealed that closed forest diminished from 19% in 1997 to 10% in 2007 to 6% in 2017. Open forest reduced from 30% to 21% from 1997 to 2007 but increased to 22% in 2017. Agriculture area almost doubled from 37 % in 1997 to 64 % in 2017. Actual area from 1997 to 2017, shows that closed forest has reduced from 7,000 ha to 3,000 ha while open forest from 12,900 ha to 7800 ha. Savanna grassland has doubled from 5,900 ha to 13,000 ha. However, future studies should use modern satellites such as Sentinel and Landsat 9 for improved quantification of changes. The findings show that even the protected forest reserve (previously dominated by closed forest) is not fully protected from deforestation by local communities. Government and other stakeholders should devise measures to meet the needs of the surrounding communities and the ecological/biophysical needs of the reserves. Based on this study, issues of re-demarcation of the forest reserve and accessed area should also be explored. This study serves as a reference for the management of Thuma Forest Reserve as a refuge for natural tree species, rivers that harbour endemic fish species (Opsaridium microlepis and Opsaridium microcephalis) and the sustainable management of endangered elephants in the reserve.

scholarly journals Land Use Land Cover Analysis using Geospatial Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2561-2566
Author(s):  
Babita Singh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Anthropogenic Activities ◽  
Google Earth ◽  
Landscape Configuration ◽  
Landsat 8 ◽  
Visual Interpretation ◽  
Open Forest

Abstract: Remote sensing and Geographic information system (GIS) techniques can be used for the changing pattern of landscape. The study was conducted in Dehradun, Haridwar and Pauri Garhwal Districts of Uttarakhand State, India. In order to understand dynamics of landscape and to examine changes in the land use/cover due to anthropogenic activities, two satellite images (Landsat 5 and Landsat 8) for 1998 and 2020 were used. Google Earth Engine was used to perform supervised classification. Spectral indices (NDVI, MNDWI, SAVI, NDBI) were calculated in order to identify land cover classes. Both 1998 and 2020 satellite images were classified broadly into six classes namely agriculture, built-up, dense forest, open forest, scrub and waterbody. Using high resolution google earth satellite images and visual interpretation, overall accuracy assessment was performed. For land cover/use change analysis, these images were imported to GIS platform. Landscape configuration was observed by calculating various landscape metrices Images. It was observed that scrub land area had increased from 11 % to 14 % but a decrease in agriculture by 4.65 %. The increased value of NP, PD, PLAND, LPI and decrease in AI landscape indices shows that land fragmentation had increased since 1998. The most fragmented classes were scrub (PD - 3.32 to 5.18) and open forest (PD - 3.57 to 5.07). Decrease in AI for open forest, agriculture, built-up indicated that more fragmented patches of these classes were present. The result confirmed increase in the fragmentation of landscape from 1998 onwards. Keywords: GIS, LULC, landscape metrics, Remote Sensing

scholarly journals Land use and land cover dynamics in the Melap Forest Reserve, West cameroon: implications for sustainable management

2021 ◽  
pp. 1-11
Author(s):  
Lucie Félicité Temgoua ◽  
Alvine Larissa Meyabeme Elono ◽  
Younchahou Mfonkwet Njiaghait ◽  
Amadou Ngouh ◽  
Clovis Nzuta Kengne
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Sustainable Management ◽  
Forest Reserve ◽  
Land Cover Dynamics

scholarly journals Land-Use Change in New Moscow: First Outcomes after Five Years of Urbanization

2019 ◽  
Vol 12 (4) ◽  
pp. 24-34 ◽  
Author(s):  
Viacheslav I. Vasenev ◽  
Alexey M. Yaroslavtsev ◽  
Ivan I. Vasenev ◽  
Sofiya A. Demina ◽  
Elvira A. Dovltetyarova
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Spectral Resolution ◽  
Environmental Changes ◽  
Land Use Changes ◽  
Landsat 8 ◽  
Fallow Lands ◽  
Forested Areas ◽  
New Moscow

Urbanization coincides with remarkable environmental changes, including conversion of natural landscapes into urban. Moscow megapolis is among the largest urbanized areas in Europe. An ambitious New Moscow project expanded the megapolis on extra 1500 km2 of former fallow lands, croplands and forests. The research aimed to monitor land use changes in New Moscow between 1989 and 2016 years. Landsat 5 and Landsat 8 images (30 m spectral resolution) and Sentinel – 2 images (10 m spectral resolution) were analyzed. All the images were collected for the similar summer period (from June to August). The images were preprocessed and classified by Semi-Automatic Classification Plugin in open source QGIS software to derive land cover maps. The following land cover classes were identified: water, built-up areas, bare soils, croplands and forested areas, and the total area covered by each class was estimated. The following land-use change pathways were reported: 1) reduction of the forested areas by 2.5% (almost 2000 ha) between 1989 and 1998; 2) partial reforestation (more than 1000 ha) and abandonment of croplands (more than 3000 ha) between 1998 and 2010 and 3) intensive urbanization (more than 11000 ha) between 2010 and 2016. New build-up areas and infrastructures were constructed on former forested areas and croplands. Although, some uncertainties in the absolute estimates are expected due to the classification errors, the general urbanization trend can be clearly distinguished as a principal outcome after the five years of New Moscow project.

scholarly journals Application of Markov Chain Model and ArcGIS in Land Use Projection of Ala River Catchment, Akure, Nigeria

2021 ◽  
Vol 18 (1) ◽  
pp. 30-38
Author(s):  
P.A. Adegbola ◽  
J.R. Adewumi ◽  
O.A. Obiora-Okeke
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Use Change ◽  
Land Cover ◽  
Markov Chain Model ◽  
Urban Expansion ◽  
Efficiency Index ◽  
Landsat 8 ◽  
Chain Model ◽  
Negative Consequences

Increase land use change is one of the consequences of rapid population growth of cities in developing countries with its negative consequences on the environment. This study generates previous and present land use of Ala watershed and project the future land use using Markov chain model and ArcGIS software (version 10.2.1). Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 used to generate land use (LU) and land cover (LC) images for the years 2000, 2010 and 2019. Six LU/LC classes were considered as follows: developed area (DA), open soil (OS), grass surface (GS), light forest (LF), wetland (WL) and hard rock (HR). Markov chain analysis was used in predicting LU/LC types in the watershed for the years 2029 and 2039. The veracity of the model was tested with Nash Sutcliffe Efficiency index (NSE) and Percent Bias methods. The model results show that the study area is growing rapidly particularly in the recent time. This urban expansion results in significant decrease of WL coverage areas and the significant increase of DA. This implies reduction in the available land for dry season farming and incessant flood occurrence. Keywords: Land cover, land use change, Markov chain, ArcGIS, watershed, urbanization

scholarly journals A new European land systems representation accounting for landscape characteristics

2021 ◽  
Author(s):  
Yue Dou ◽  
Francesca Cosentino ◽  
Ziga Malek ◽  
Luigi Maiorano ◽  
Wilfried Thuiller ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Large Scale ◽  
Bird Species ◽  
Data Availability ◽  
Landscape Characteristics ◽  
Continental Scale ◽  
Land Systems ◽  
Land Cover Maps

Abstract Context While land use change is the main driver of biodiversity loss, most biodiversity assessments either ignore it or use a simple land cover representation. Land cover representations lack the representation of land use and landscape characteristics relevant to biodiversity modeling. Objectives We developed a comprehensive and high-resolution representation of European land systems on a 1-km2 grid integrating important land use and landscape characteristics. Methods Combining the recent data on land cover and land use intensities, we applied an expert-based hierarchical classification approach and identified land systems that are common in Europe and meaningful for studying biodiversity. We tested the benefits of using this map as compared to land cover information to predict the distribution of bird species having different vulnerability to landscape and land use change. Results Next to landscapes dominated by one land cover, mosaic landscapes cover 14.5% of European terrestrial surface. When using the land system map, species distribution models demonstrate substantially higher predictive ability (up to 19% higher) as compared to models based on land cover maps. Our map consistently contributes more to the spatial distribution of the tested species than the use of land cover data (3.9 to 39.1% higher). Conclusions A land systems classification including essential aspects of landscape and land management into a consistent classification can improve upon traditional land cover maps in large-scale biodiversity assessment. The classification balances data availability at continental scale with vital information needs for various ecological studies.

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