Quarrying and its effect on vegetation cover for a sustainable development using high-resolution satellite image and GIS

Akanwa, Angela Oyilieze; Okeke, Francis Ifeanyi; Nnodu, V. C.; Iortyom, Enoch Terlumun

doi:10.1007/s12665-017-6844-x

Cited by 37 publications

(19 citation statements)

References 13 publications

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“…In particular, all satellite images of Sentinel series could be freely downloaded from Sentinel Open Hub (https://scihub.copernicus.eu/). The officially customized software Sentinel Application Platform (SNAP) is utilized to import all the sensor information and export tailored data for follow-up analysis in comparison to other geo-software such as ENVI [25,26]. The selected field is a typical area composed of five main classes: buildings (such as universities, factories and companies), trees, roads, water, and crop fields.…”

Section: Study Areamentioning

confidence: 99%

Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

Zhang

et al. 2021

Applied Sciences

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Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water, and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision, and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable.

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Section: Study Areamentioning

confidence: 99%

Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

Zhang

et al. 2021

Applied Sciences

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“…Vegetation destruction due to unsustainable quarrying practices in Nigeria has diminished the role played by vegetation cover in reducing the global impacts of climate change [12] Rapid globalization, rise in technology, the fast rate of population growth and the perceived diminution of nature's worth for human psychological, emotional and physical health has caused a disconnect between humanity and the natural environment [13] The widening gap of this disconnect has caused the world's prospects of achieving human wellbeing to weaken within the world's dominant economic development paradigms. As a result there is more emphasis on the economic gains from the environment with very little care of the sustainability of the natural resources.…”

Section: Quarrying Activities Have Altered Vegetation Cover All Over mentioning

confidence: 99%

“…This causes stunted growth and even drying up of some plants. All these effects have a negative implication on the socioeconomic wellbeing of the communities in the quarrying areas [12]. Conversely, adequate vegetation cover will mitigate these effects and hence lead to enhanced socio-economic wellbeing of households and the respective communities in the soapstone quarrying areas.…”

Section: Comparison Of the Household Wellbeing Within The Study Areamentioning

confidence: 99%

Effects of Vegetation Destruction on the Socio-Economic Well-being of Households within the Soapstone Quarrying Areas of Gucha South Sub-County, Kenya

Okong’o

Mutinda

Imaita

2021

IJECC

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Soapstone quarrying and carving has taken place at the Tabaka region of Gucha South Sub-County for many decades. The activities associated with soapstone quarrying include clearing of vegetation, removal of top soil, earth moving, excavation of the stones and chopping off the unwanted parts of the soapstone. These activities have caused serious environmental impacts in the quarrying areas. This study focused on the effects of vegetation destruction as a result of soapstone quarrying on the socio-economic well-being of households within the quarrying areas of Gucha South Sub-County, Kenya. The objective of the study was to determine the effects of vegetation destruction (loss of cover and biodiversity erosion) by quarrying on the socio-economic well-being of households within the quarry areas. A cross-sectional survey research design was adopted. Purposive sampling procedure was utilized where 102 households were purposively selected based on their nearness to the soapstone quarries. Data collection was done using a questionnaire, recording observations and taking photographs. Descriptive (Frequency distribution, percentages, means, median and mode) and Inferential (Regression, Chi-square and ANOVA) statistics were used to analyze the data. The findings of this study indicate that vegetation destruction as a result of soapstone quarrying activities has negative effects on the socio-economic wellbeing of the households found within the soapstone quarrying areas. Thus, there is need to regulate the soapstone quarrying activities. There is also an urgent need for an Environmental Impact Assessment and audit to be carried out to determine the state of the environment in the soapstone quarrying areas.

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“…Giardina (2000), states that the erodibility of soil is defined by the vulnerability or susceptibility to erosion. Generally, soils with faster infiltration rates, with higher levels of organic matter and improved soil structure have a greater resistance to erosion, Akanwa et al 2017). Sharhrivar and Christopher (2012) espoused the effects of soil physical characteristics on gully erosion development in Kohgiloyeh and Boyer Ahmad Province, Iran.…”

Section: Soil Dependencementioning

confidence: 99%

Determining Causes of Gully Erosion and Associated Rates of Change in South-east Nigeria, Using a Remote Sensing and GIS Methodology

Iro

2020

Preprint

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In this work a study of gully erosion in southeast Nigeria is presented. The study of gully development on a regional scale is currently undermined by the inherent costs associated with consistent field monitoring and the lack of historic measurements to perform time series analysis. The analysis of study area topography at 30m resolution reveals 85% of the surveyed gullies develop on concave slopes with high values of 6 plan curvatures and >50 inclines. Results also reveal high association with ferralsols soils. Statistical analysis to determine significance of variables on the proportional yearly gully change in metre squared per square metre were conducted via principle component analysis. The analysis of this work was restricted to the time periods 2006/7, 2009/10, and 2014/15. The approach did not report any existence of one singular driver of erosion across the studied years and multiple sites confirming the complexity of gullies. The PCA showed that the level of variance explained in the yearly gully change variable was most similar in PC1 (representing the component with the highest eigenvalue) to Vegetation loss, Vegetation loss and slope in the respective years.The study offers a method of monitoring gully development from early stage to maturity and exemplifies the complexity and variability of erosion drivers in the SE Nigeria region. It presents a verified approach to local and regional monitoring of gullies, enacted through use of low budget/computing cost remote sensing and classification technologies, and serves to embolden civilian and governmental efforts to manage the societal and environmental menace of gully erosion.

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Quarrying and its effect on vegetation cover for a sustainable development using high-resolution satellite image and GIS

Cited by 37 publications

References 13 publications

Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

Effects of Vegetation Destruction on the Socio-Economic Well-being of Households within the Soapstone Quarrying Areas of Gucha South Sub-County, Kenya

Determining Causes of Gully Erosion and Associated Rates of Change in South-east Nigeria, Using a Remote Sensing and GIS Methodology

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