Land-Use and Land Cover Changes on the Slopes of Mount Meru-Tanzania

Kitalika, Aldo J.; Machunda, Revocatus L.; Komakech, Hans C.; Njau, Karoli N.

doi:10.12944/cwe.13.3.07

Cited by 9 publications

(9 citation statements)

References 37 publications

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“…As shown in (5), where the f ′ represents the derivative of the above pooling layer function (5). e k ζ ij is convolution kernel, the δ ζ+1 j is the weight parameters.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…e use of land will undergo major changes over time, which requires a technology that can process land data in real time. Land is a very important resource for humans and even organisms, and it is the basis for human production, development, and survival [5]. e changes in land use directly affect the progress and development of human society and also have an important impact on the development and distribution of natural resources.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

Zhang

2022

Scientific Programming

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With the rapid development of urbanization, the utilization rate of land has become the focus of attention. Remote sensing technology can provide a large amount of data for the prediction of urban land. It is also a thorny problem to find the correlation between the complex data of land change. The neural network technology has obvious advantages in finding the mapping relationship between high-dimensional and nonlinear data. This paper combines the dynamic changes of urban land and neural network methods to analyze the utilization rate and coverage of urban land in the future. In this paper, the data obtained by remote sensing technology is normalized and clustered to classify different types of urban land. Convolutional neural networks and long-short-term memory neural networks are used to extract the spatial and temporal dynamic characteristics of urban land use. The research results show that the clustering method used in this paper can reasonably classify different urban land types, especially the classification of buildings. The method of predicting the future trend of land use is also in line with the dynamic process of land use. The largest prediction error comes from the prediction of the building, and the largest error is only 2.56%, which is a reasonable error range. The smallest error does not exceed 1%, and the correlation coefficient between the real and predicted values of urban land use types reaches 0.9698.

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“…As shown in (5), where the f ′ represents the derivative of the above pooling layer function (5). e k ζ ij is convolution kernel, the δ ζ+1 j is the weight parameters.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

Zhang

2022

Scientific Programming

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“…The current rates are higher than ever recorded [7]. Many studies have also shown that land use/cover changes influence the hydrology of watersheds [8][9][10][11]. Thus, evaluating the impact of land cover (LC) and climate changes on water resource availability is an important Earth 2021, 2, 225-247. https://doi.org/10.3390/earth2020014 https://www.mdpi.com/journal/earth challenge for the current hydrological science [12,13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction of the Impacts of Land Cover Changes on Hydrological Processes in Data Constrained Southern Slopes of Kilimanjaro, Tanzania

Said

Hyandye

Mjemah

et al. 2021

Earth

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This study provides a detailed assessment of land cover (LC) changes on the water balance components on data constrained Kikafu-Weruweru-Karanga (KWK) watershed, using the integrated approaches of hydrologic modeling and partial least squares regression (PLSR). The soil and water assessment tool (SWAT) model was validated and used to simulate hydrologic responses of water balance components response to changes in LC in spatial and temporal scale. PLSR was further used to assess the influence of individual LC classes on hydrologic components. PLSR results revealed that expansion in cultivation land and built-up area are the main attributes in the changes in water yield, surface runoff, evapotranspiration (ET), and groundwater flow. The study findings suggest that improving the vegetation cover on the hillside and abandoned land area could help to reduce the direct surface runoff in the KWK watershed, thus, reducing flooding recurring in the area, and that with the ongoing expansion in agricultural land and built-up areas, there will be profound negative impacts in the water balance of the watershed in the near future (2030). This study provides a forecast of the future hydrological parameters in the study area based on changes in land cover if the current land cover changes go unattended. This study provides useful information for the advancement of our policies and practices essential for sustainable water management planning.

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“…Since the water available is insufficient for economic use, urban dwellers extend to the nearby streams for easy access [9]. Rivers in the Arusha region pass through human settlements and downstream to areas with several human activities, including inefficiently treated effluent wastewater and inappropriate urban planning [10].…”

Section: Introductionmentioning

confidence: 99%

Socio-Economic Feasibility for Implementation of Environmental Legislation along the Riparian Buffer Zones in Urban Rivers of Northern Tanzania

Mwasenga,

Mjemah

2023

Urban Science

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The development of socio-economic activities within the 60 m buffer zone has imposed change on the characteristics of rivers in northern Tanzania, subjecting rivers to collateral and irreversible damage due to their prolonged exposure to anthropic activities. Time series satellite images were classified to analyze land use/cover (LULC) changes and anthropic development along the buffer zone from 2000 to 2020. Structured questionnaires were used to identify the encroachment factors and level of compliance with alleged environmental legislation. Furthermore, focus group discussions were used to acquire information on the concurrent barriers to environmental legislation implementation. The land use/cover change along the buffer zone showed that agriculture and artificial areas had a credible increase of about 43% and 30% from 2000 to 2020, respectively. Furthermore, forest and semi-natural areas decreased by 71% from 2000 to 2020, whereas wetlands decreased by about 2% within the same timeframe. On the other hand, artificial and agricultural areas increased by 24.5% and 19.5%, respectively. Forest and semi-natural areas decreased by about 44%, whereas wetlands and water showed a flimsy increase from 2000 to 2020. This trend shows that high land use/cover changes occurred along the riparian buffer zone. The results suggest that urbanization is the main driving force for riparian buffer zone encroachment, threatening ecological well-being and water resource sustainability in urban rivers. The findings of this study are useful for advancing regional and national policies and practices for sustainable water resource management.

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Land-Use and Land Cover Changes on the Slopes of Mount Meru-Tanzania

Cited by 9 publications

References 37 publications

Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

Evaluation and Prediction of the Impacts of Land Cover Changes on Hydrological Processes in Data Constrained Southern Slopes of Kilimanjaro, Tanzania

Socio-Economic Feasibility for Implementation of Environmental Legislation along the Riparian Buffer Zones in Urban Rivers of Northern Tanzania

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