Search results

The purpose of this study is to analyze the horizontal expansion of Burayu Town between 1990 and 2020. The study typically acts as a baseline for integrated spatial planning in small- and medium-sized towns, which will help to plan sustainable utilization of land.

Landsat5-TM, Landsat7 ETM+, Landsat5 TM and Landsat8 OLI were used in the study, along with other auxiliary data. The LULC map classifications were generated using the Random Forest Package from the Comprehensive R Archive Network. Post-classification, spatial metrics, and per capita land consumption rate were used to understand the manner and rate of expansion of Burayu Town. Focus group discussions and key informant interviews were also used to validate land use classes through triangulation.

The study found that the built-up area was the most dynamic LULC category (85.1%) as it increased by over 4,000 ha between 1990 and 2020. Furthermore, population increase did not result in density increase as per capita land consumption increased from 0.024 to 0.040 during the same period.

As a result of financial limitations, there were no high-resolution satellite images available, making it challenging to pinpoint the truth as it is on the ground. Including senior citizens in the study region allowed this study to overcome these restrictions and detect every type of land use and cover.

Data on urban growth are useful for planning land uses, estimating growth rates and advising the government on how best to use land. This can be achieved by monitoring and reviewing development plans using satellite imaging data and GIS tools.

The use of Random Forest for image classification and the employment of local knowledge to validate the accuracy of land cover classification is a novel approach to properly customize remote sensing applications.

Land use changes significantly impacts ecosystem services and functions. The estimation of ecosystem services value is conducive to clarifying the ecological changes in response to LULC changes due to urbanization. Chengde was the upper water source of Beijing and Tianjin, the ecosystem is very fragile. After a series of ecology conservation projects like "returning cropland to forestry", the ecosystem service value increased from 5100.17 x 10⁷ Yuan in 1990 to 5104.08 x 10⁷ Yuan in 2008 respectively, with the average increase of 2.3 x 10⁶ Yuan per year. It is indicated that ecosystem service value has the potential to inform policy decisions by emphasizing the benefits of sustainable ecosystem management. So plans on land use management should be made to maintain a balance between urbanization and ecosystem health.

This paper aims to assess the rate and land category contributing to the changes in seven land-uses in the Kintampo North Municipality of Ghana and the effect of the decisions of land users on future landscapes.

LANDSAT images were classified to generate land use/cover maps to detect changes that had occurred between 1986 and 2014. In total, 120 farmers were also interviewed to determine their perceptions on land use changes. Interval, category and transition levels of changes were determined. Savanna woodland, settlement and forest were mostly converted to farmland in both intervals (1986-2001 and 2001-2014).

Results showed that rock outcrop, plantation, cropland and savanna woodland increased at an annual rate of 13.86, 1.57, 0.82 and 0.33 per cent, respectively, whilst forest, settlement and water body decreased at 4.90, 1.84 and 1.17 per cent annual rate of change, respectively. Approximately, 74 per cent of farmers will not change land use in the future, while 84.2 per cent plan to increase farm sizes.

The study shows that more land cover will be targeted for conversion as farmers expand their farmlands. There is the need for strict implementation of appropriate land use/cover policies to sustain food production in the region in this era of changing climate and population increase.

This research assessed the land use changes in the Kintampo North Municipality and its impacts on agriculture and carbon stocks release via land use changes. It identified how the decisions of the local farmers on land management will affect future landscape.

Despite the enormous need to succeed in the urban model, scientists and policymakers should work consistently to create blueprints to regulate urbanization. The absence of coordination between the crucial requirements and the regional strategies of the local authorities leads to a lack of conformance in urban development. The purpose of this paper is to address this issue.

This study intends to manage future urban growth patterns using integrated methods and then employ the results in the genetic algorithm (GA) model to considerably improve growth behavior. Multi-temporal land-use datasets have been derived from remotely sensed images for the years 1990, 2000, 2010 and 2020. Urban growth patterns and processes were then analyzed with land-use-and-land-cover dynamics. Results were examined for simulation and utilization of the GA.

Model parameters were derived and evaluated, and a preliminary assessment of the effective coefficient in the formation of urbanization is analyzed, showing the city's urbanization pattern has followed along with the transportation infrastructure and outward growth, and the scattering rates are high, with an increase of 5.64% in building area associated with a decrease in agricultural lands and rangelands.

The research achieved a considerable improvement over the growth behavior. The conducted research design was the first of its type in that field to be executed to any specific growth pattern parameters in terms of regulating and policymaking. The method has integrated various artificial intelligence models to monitor, measure and optimize the projected growth by applying this design. Other research on the area was limited to projecting the future of Amman as it is an urbanized distressed city.

This paper aims at providing the evidence about how carbon sequestration in terrestrial ecosystems could contribute to the decrease of atmospheric CO₂ rates through the adoption of appropriate cropping systems such as agroforestry.

Stratified randomly selected plots were used to collect data on tree diameter at breast height (DBH). Composite soil samples were collected from three soil depths for soil carbon analysis. Above ground biomass estimation was made using an allometric equation. The spectral signature of each plot was extracted to study the statistical relationship between carbon stock and selected vegetation indices.

There was a significant difference in vegetation and soil carbon stocks among the different land use/land cover types (P < 0.05). The potential carbon stock was highest in the vegetation found in sparsely cultivated land (13.13 ± 1.84 tons ha⁻¹) and in soil in bushland (19.21 ± 3.79 tons ha⁻¹). Carbon sequestration potential of the study area significantly increased (+127174.5 tons CO₂e) as a result of conversion of intensively cultivated agricultural lands to agroforestry systems. The amount of sequestered carbon was found to be dependent on species diversity, tree density and tree size. The vegetation indices had a better correlation with soil and total carbon.

The paper has addressed an important aspect in curbing greenhouse gases in integrated land systems. The paper brings a new empirical insight of carbon sequestration potentials of agroforestry systems with a focus on drylands.

Deepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.

The lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.

The MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m³ in 2001 to 16.73 million m³ in 2011 and further declined to 15.35 million m³ in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.

None of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.