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      How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?

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      Applied Sciences
      MDPI AG

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          Abstract

          In recent decades, population growth and economic development have greatly influenced the pattern of land use/land cover (LULC) in Rwanda. Nevertheless, LULC patterns and their underlying change mechanisms under future climate conditions are not well known. Therefore, it is particularly important to explore the direction of LULC transfer in the study area, identify the factors driving the transfer of different types of LULC and their changes, and simulate future LULC patterns under future climate conditions. Based on LULC analyses of Rwanda in 1990, 2000, 2010, and 2015, the LULC pattern of Rwanda in the next 30 years was simulated using an LULC transition matrix, random forest sampling, the Markov chain model, and the PLUS model. The results showed that LULC change in the study area primarily comprised a decrease in forest area and expansion of cropland area, accompanied by a small increase in grassland area and an annual increase in urban land area. Prior to 2000, the LULC in Rwanda was mainly converted from forest and grassland to cropland, with the ratio being 0.72:0.28. After 2010, the LULC was mainly converted from forest to grassland and cropland, with the ratio being 0.83:0.17. Changes in forests, grasslands, and cropland are driven by multiple factors, whereas changes in wetlands, water, urban land, and unused land are more likely to be driven by a single factor. The existing trend of LULC change will continue for the next 30 years, and the future LULC pattern will exhibit a trend in which cropland area will increase in the west and grassland area will decrease, whereas grassland area will increase in the east and cropland area will decrease.

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          The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

          The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
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            The causes of land-use and land-cover change: moving beyond the myths

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              Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit.

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                Author and article information

                Contributors
                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                June 2021
                June 09 2021
                : 11
                : 12
                : 5376
                Article
                10.3390/app11125376
                711b84a1-9cf0-47d4-af55-eb27143c6a5f
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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