Land Use and Landscape Structural Changes in the Ecoregions of Ghana

2014 ◽  
Vol 9 (4) ◽  
pp. 452-467 ◽  
Author(s):  
Effah Kwabena Antwi ◽  
◽  
John Boakye-Danquah ◽  
Stephen Boahen Asabere ◽  
Gerald A. B. Yiran ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landscape Structure ◽  
Structural Changes ◽  
Agricultural Land ◽  
Deciduous Forest ◽  
Diversity Index ◽  
Research Literature ◽  
Forest Garden ◽  
Open Land

In recent years, land use (LU) and landscape structure in ecoregions around the world have been faced with enormous pressures, from rapid population growth to urban sprawl. A preliminary account of changes in land cover (LC) and landscape structure in the ecoregions of Ghana is missing from the academic and research literature. The study therefore provides a preliminary assessment of the changing LU and landscape structure in the ecoregions of Ghana, identifying the causes and assessing their impact on land-based resources, and on urban and agricultural development. LU/LC maps produced from 30 m resolution Landsat TM5 in 1990 and ETM+ in 2000 were classified into dominant land cover types (LCTs) and used to survey the changing landscape of Ghana. LC-changemap preparation was done with change detection extension “Veränderung” (v3) in an ArcGIS 10.1 environment. At the class level, Patch Analyst version 5.1 was used to calculate land use (LU) statistics and to provide landscape metrics for LU maps extracted from the satellite imagery. The results showed that commonly observed LCCs in the ecoregions of Ghana include conversion of natural forest land to various forms of cultivated lands, settlements, and open land, particularly in closed and open forest and savannah woodland. The dominant LU types in the ecoregions of Ghana are arable lands, which increased by 6168.98 km2. Forest and plantation LCTs decreased in area and were replaced by agricultural land, forest garden, and open land. Afforestation rarely occurred except in the rainforests. The mean patch size (MPS), ameasure of fragmentation, was generally reduced consistently from 1990 to 2000 in all the ecoregions. Similar results that indicated increased fragmentation were an increased number of patches (NumP) and the Shannon diversity index (SDI). Habitat shape complexity inferred from mean shape index (MSI) decreased in all ecoregions except for rainforest and wet evergreen. The SDI and Shannon evenness index (SEI) showed that habitat diversity was highest in the coastal savannah and the deciduous forest ecoregions. The main drivers of changes in the LUs and landscape structure are demand for land and land-based natural resources to support competing livelihoods and developmental activities in the different ecoregions.

scholarly journals SEBARAN PENGGUNAAN LAHAN PERTANIAN DI KOTA TARAKAN DAN PREDIKSI POTENSI BAHAYA EROSI BERBASIS (SIG)

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Nur Indah Mansyur, S.P., M.P ◽  
Ramdani A.I
Keyword(s):  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Agricultural Land ◽  
Geographical Information ◽  
Dry Land ◽  
Digital Elevation ◽  
Open Land ◽  
Elevation Model

ABSTRACTLand use analysis is useful to identify the mechanisms of changes that occur in a land through a spatial approach in the Geographical Information System. The spatial approach is carried out by using the Arcgis program to analyze geographic data into map units. This study aimed to (1) determine the design of a Geographical Information System (GIS) in an inventory of the use and erosion potential hazards of agricultural land in Tarakan, and (2) inform the use and erosion potential hazards of agricultural land in Tarakan. this study took spatial data from the Public Works and Spatial Planning Office (DPUTR) of Tarakan, Digital Elevation Model (DEM) data to analyze the slope and height of Tarakan and field surveys using GPS. The results showed that the area of agricultural land use in Tarakan in general was 75.33%, dominated by forest land use 38.91%, non-residental open land 25.72%, agriculture 9.35% and plantation land 1.35%. In the land use system, it was never separated from the type of land cover. There were 10 types of land cover in Tarakan including urban forest, dry land forest, swamp/peat forest, mixed garden, dry land/fields, open land, shrubs, orchid botanical gardens, meadows and rice fields. Tarakan had the potential for erosion hazards which could be seen from the slope factor. From 2012-2020, there was an erosion of the area of the slope in each class, namely flat, sloping, steep, rather steep and very steep as well as a reduction in height from 124 MASL in 2012 to 107 MASL in the year 2020. With the existence of land cover and the potential danger of erosion, the land management approach in Tarakan must prioritize aspects of land intensification and conservation, so that the ecosystem can be maintained in a sustainable manner.Keywords : Land Use, Geographical Information System (GIS), Erosion Hazard Potential ABSTRAKAnalisis penggunaan lahan berguna untuk mengidentifikasi mekanisme perubahan-perubahan yang terjadi pada suatu lahan melalui pendekatan spasial dalam Sistem Informasi Geografis. Pendekatan spasial dilakukan dengan menggunakan program Arcgis untuk menganalisis data  geografis kedalam satuan peta. Penelitian ini bertujuan untuk (1) mengetahui rancangan Sistem Informasi Geografis (SIG) dalam inventarisasi penggunaan dan potensi bahaya erosi lahan pertanian di Kota Tarakan, dan (2) menginformasikan penggunaan dan potensi bahaya erosi lahan pertanian di Kota Tarakan.. Penelitian ini mengambil data spasial dari Dinas Pekerjaan Umum dan Tata Ruang (DPUTR) Kota Tarakan, data Digital Elevation Model (DEM) untuk menganalisis kemiringan lereng serta ketinggian Kota Tarakan dan survey lapangan menggunakan GPS. Hasil penelitian menunjukkan bahwa luas penggunaan lahan pertanian di Kota Tarakan secara umum 75,33%, didominasi penggunaan lahan hutan 38,91%, lahan terbuka non pemukiman 25,72%, pertanian secara khusus 9,35% dan lahan perkebunan 1,35%. Dalam sistem penggunaan lahan tidak pernah terlepas dari jenis tutupan lahan, tutupan lahan di Kota Tarakan terdapat 10 jenis tutupan meliputi hutan kota, hutan lahan kering, hutan rawa/gambut, kebun campuran, tegalan/ladang, lahan terbuka, semak belukar, kebun raya anggrek, padang rumput dan sawah. Kota Tarakan memiliki potensi bahaya erosi dapat dilihat dari faktor kemiringan lereng dari tahun 2012-2020 terjadi pengikisan luas kemiringan lereng di setiap kelasnya yaitu datar, landai, curam, agak curam dan sangat curam serta terjadinya pengurangan ketinggian dari 124 mdpl tahun 2012 menjadi 107 mdpl ditahun 2020. Dengan adanya tutupan lahan dan potensi bahaya erosi tersebut maka pendekatan pengelolaan lahan di Kota Tarakan harus lebih mengedepankan aspek intensifikasi dan konservasi lahan, agar ekosistem dapat terpelihara secara berkelanjutan.Kata kunci : Penggunaan Lahan, Sistem Informasi Geografis (SIG), Potensi Bahaya Erosi

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals How Can We Represent Seasonal Land Use Dynamics in SWAT and SWAT+ Models for African Cultivated Catchments?

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1541
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Anna Msigwa ◽  
Hans C. Komakech ◽  
Boud Verbeiren ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Swat Model ◽  
Agricultural Practices ◽  
Area Index ◽  
Land Use Dynamics ◽  
Cropping Seasons ◽  
Decision Tables ◽  
Land Cover Maps

In SWAT and SWAT+ models, the variations in hydrological processes are represented by Hydrological Response Units (HRUs). In the default models, agricultural land cover is represented by a single growing cycle. However, agricultural land use, especially in African cultivated catchments, typically consists of several cropping seasons, following dry and wet seasonal patterns, and are hence incorrectly represented in SWAT and SWAT+ default models. In this paper, we propose a procedure to incorporate agricultural seasonal land-use dynamics by (1) mapping land-use trajectories instead of static land-cover maps and (2) linking these trajectories to agricultural management settings. This approach was tested in SWAT and SWAT+ models of Usa catchment in Tanzania that is intensively cultivated by implementing dominant dynamic trajectories. Our results were evaluated with remote-sensing observations for Leaf Area Index (LAI), which showed that a single growing cycle did not well represent vegetation dynamics. A better agreement was obtained after implementing seasonal land-use dynamics for cultivated HRUs. It was concluded that the representation of seasonal land-use dynamics through trajectory implementation can lead to improved temporal patterns of LAI in default models. The SWAT+ model had higher flexibility in representing agricultural practices, using decision tables, and by being able to represent mixed cropping cultivations.

Vegetation Effects on Soil Properties in the Monteagle Sandy Loam Series: Implications for Land‐use Change

2017 ◽  
Author(s):  
Allison Neil
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Land Use Change ◽  
Soil Properties ◽  
Soil Carbon ◽  
Sugar Maple ◽  
Agricultural Land ◽  
Deciduous Forest ◽  
Coniferous Forest ◽  
The Impact

Soil properties are strongly influenced by the composition of the surrounding vegetation. We investigated soil properties of three ecosystems; a coniferous forest, a deciduous forest and an agricultural grassland, to determine the impact of land use change on soil properties. Disturbances such as deforestation followed by cultivation can severely alter soil properties, including losses of soil carbon. We collected nine 40 cm cores from three ecosystem types on the Roebuck Farm, north of Perth Village, Ontario, Canada. Dominant species in each ecosystem included hemlock and white pine in the coniferous forest; sugar maple, birch and beech in the deciduous forest; grasses, legumes and herbs in the grassland. Soil pH varied little between the three ecosystems and over depth. Soils under grassland vegetation had the highest bulk density, especially near the surface. The forest sites showed higher cation exchange capacity and soil moisture than the grassland; these differences largely resulted from higher organic matter levels in the surface forest soils. Vertical distribution of organic matter varied greatly amongst the three ecosystems. In the forest, more of the organic matter was located near the surface, while in the grassland organic matter concentrations varied little with depth. The results suggest that changes in land cover and land use alters litter inputs and nutrient cycling rates, modifying soil physical and chemical properties. Our results further suggest that conversion of forest into agricultural land in this area can lead to a decline in soil carbon storage.

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

scholarly journals Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid Hydrologic Modeling and Basin Planning

2018 ◽  
Vol 10 (12) ◽  
pp. 1910 ◽  
Author(s):  
Joseph Spruce ◽  
John Bolten ◽  
Raghavan Srinivasan ◽  
Venkat Lakshmi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Reference Data ◽  
Assessment Tool ◽  
Deciduous Forest ◽  
Land Use Land Cover ◽  
Modis Ndvi ◽  
Mekong Basin ◽  
Lower Mekong Basin

This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common forest and agricultural LULC types. Dry season circa 2010 Landsat top of atmosphere reflectance mosaics were classified to map locally common LULC types. Unsupervised ISODATA clustering was used to derive most LULC classifications. MODIS and Landsat classifications were combined with GIS methods to derive final 250-m LULC maps for Sub-basins (SBs) 1–8 of the LMB. The SB 7 LULC map with 14 classes was assessed for accuracy. This assessment compared random locations for sampled types on the SB 7 LULC map to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data, and Mekong River Commission data (e.g., crop calendars). The SB 7 LULC map showed an overall agreement to reference data of ~81%. By grouping three deciduous forest classes into one, the overall agreement improved to ~87%. The project enabled updated regional LULC maps that included more detailed agriculture LULC types. LULC maps were supplied to project partners to improve use of Soil and Water Assessment Tool for modeling hydrology and water use, plus enhance LMB water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change as part of basin planning and assessment.

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.

Measuring and monitoring land cover, land use, and vegetation characteristics

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Deciduous Forest ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Land Cover Mapping ◽  
Remotely Sensed Data ◽  
Soil P ◽  
Vegetation Characteristics ◽  
Classification Project

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.

scholarly journals Dynamics of forage and land cover changes in Teltele district of Borana rangelands, southern Ethiopia: using geospatial and field survey data

BMC Ecology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yeneayehu Fenetahun ◽  
Wang Yong-dong ◽  
Yuan You ◽  
Xu Xinwen
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Biomass Production ◽  
Agricultural Land ◽  
Southern Ethiopia ◽  
Forest Land ◽  
Lulc Change ◽  
Grass Land ◽  
Land Use Types ◽  
Bare Land

Abstract Background The gradual conversion of rangelands into other land use types is one of the main challenges affecting the sustainable management of rangelands in Teltele. This study aimed to examine the changes, drivers, trends in land use and land cover (LULC), to determine the link between the Normalized Difference Vegetation Index (NDVI) and forage biomass and the associated impacts of forage biomass production dynamics on the Teltele rangelands in Southern Ethiopia. A Combination of remote sensing data, field interviews, discussion and observations data were used to examine the dynamics of LULC between 1992 and 2019 and forage biomass production. Results The result indicate that there is a marked increase in farm land (35.3%), bare land (13.8%) and shrub land (4.8%), while the reduction found in grass land (54.5%), wet land (69.3%) and forest land (10.5%). The larger change in land observed in both grassland and wetland part was observed during the period from 1995–2000 and 2015–2019, this is due to climate change impact (El-Niño) happened in Teltele rangeland during the year 1999 and 2016 respectively. The quantity of forage in different land use/cover types, grass land had the highest average amount of forage biomass of 2092.3 kg/ha, followed by wetland with 1231 kg/ha, forest land with 1191.3 kg/ha, shrub land with 180 kg/ha, agricultural land with 139.5 kg/ha and bare land with 58.1 kg/ha. Conclusions The significant linkage observed between NDVI and LULC change types (when a high NDVI value, the LULC changes also shows positive value or an increasing trend). In addition, NDVI value directly related to the greenness status of vegetation occurred on each LULC change types and its value directly linkage forage biomass production pattern with grassland land use types. 64.8% (grass land), 43.3% (agricultural land), 75.1% (forest land), 50.6% (shrub land), 80.5% (bare land) and 75.5% (wet land) more or higher dry biomass production in the wet season compared to the dry season.

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