Understanding Drivers of Land Use and Land Cover Change in Africa: A Review

Assèdé, Eméline Sêssi Pélagie; Orou, Hidirou; Biaou, Samadori Sorotori Honoré; Geldenhuys, C. J.; Ahononga, Fiacre Codjo; Chirwa, Paxie W.

doi:10.1007/s40823-023-00087-w

Cited by 27 publications

(12 citation statements)

References 85 publications

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“…Theil-Sen Median and Mann-Kendall trend analysis techniques have been used in the quantitative study of vegetation change trends in Shaanxi Province over the previous 20 years using temporal KNDVI data. This study used elevation, slope, and aspect as environmental parameters and land-use type and population density as anthropogenic elements in accordance with previous research [41][42][43][44][45]. The meteorological parameters that were selected included the yearly average temperature, the annual average precipitation, and the annual average potential evapotranspiration.…”

Section: Methodsmentioning

confidence: 99%

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

Shi,

Lin,

Jing

et al. 2023

Sustainability

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(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km2, accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km2, accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE ∩ PET as well as TMP ∩ PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region’s natural ecosystem.

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Section: Methodsmentioning

confidence: 99%

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

Shi,

Lin,

Jing

et al. 2023

Sustainability

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“…Non-climatic factors such as population growth, changes in the dietary pattern, and food demand will accelerate the human-induced transition to agricultural land [12,13]. The population growth rate for West Africa is forecast to increase approximately threefold by 2050 with high expected values for Niger, Nigeria, and Burkina Faso [14].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Yield Responses to Climate Variabilities in West Africa: A Food Supply and Demand Analysis

Schultze,

Kankam,

Sanfo

et al. 2024

Land

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Agricultural productivity is expected to decrease under changing climate conditions that correspond to the stability of West African food systems. Although numerous studies have evaluated impacts of climate variability on crop yields, many uncertainties are still associated with climate extremes as well as the rapid population growth and corresponding dietary lifestyle. Here, we present a food supply and demand analysis based on the relationship between climate change, crop production, and population growth in three sites from southwestern Burkina Faso to southwestern Ghana. Climate and agricultural time series were analyzed by using boxplots mixed with a Mann–Kendall trend test and Sen’s slope. Food balance sheets were calculated by estimating the demand using a population growth model linked to food supply with local consumption patterns. We found almost insignificant rainfall and temperature trends for both sites in the Sudano-Guinean savannah. Conversely, the climate regime of southwestern Ghana revealed a strong significant increasing temperature over time. Crop yield trends demonstrated that maize and sorghum were significantly enhanced in both study areas of the Sudano-Guinean savannah. Southwestern Ghana depicted a different crop pattern where cassava and plantain showed a strong upward yield trend. The grouped food balance sheets across the regions illustrated a surplus for the Sudano-Guinean savannah while southwestern Ghana exhibited a deficit. Despite the growing yield of various crops, food demand is outpacing regional production.

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“…The classification of Land Use Land Cover (LULC) plays a critical role in managing resources in developing areas, particularly in addressing challenges posed by rapid population growth (Avtar et al, 2019;Assede et al, 2023). The phenomenon of rapid urbanization emphasizes the crucial role played by accurate LULC classification in mitigating adverse impacts on global energy resources, promoting sustainable planning, efficient resource management, and safeguarding environmental integrity (Magidi et al, 2021;Praticò et al, 2021;Digra et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Nigar,

Li,

Jat Baloch

et al. 2024

Front. Environ. Sci.

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Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation.

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Understanding Drivers of Land Use and Land Cover Change in Africa: A Review

Cited by 27 publications

References 85 publications

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

Agricultural Yield Responses to Climate Variabilities in West Africa: A Food Supply and Demand Analysis

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

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