Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo

Abstract

Villages within the Luki Biosphere Reserve and the surrounding cities have undergone rapid demographic growth and urbanization that have impacted the reserve’s natural landscape. However, no study has focused on the spatiotemporal analysis of its land use/land cover. The present research aims at providing a comprehensive analysis of land use/land cover change in the Luki Biosphere Reserve from the year 1987 to 2020, and to predict its future change for the year 2038. Landsat images were classified in order to provide land use/land cover maps for the years 1987, 2002, 2017 and 2020. Based on these maps, change detection, gradient direction, and landscape metric analyses were performed. In addition, land use/land cover change prediction was carried out using the Multilayer Perceptron Markov model. The results revealed significant land use/land cover changes in the Luki Biosphere Reserve during the study period. Indeed, tremendous changes in the primary forest, which lost around 17.8% of its total area, were noted. Other classes, notably savannah, secondary forest, built-up area, fallow land and fields had gained 79.35, 1150.36, 67.63, 3852.12 hectares, respectively. Based on the landscape metric analysis, it was revealed that built-up areas and fallow land and fields experienced an aggregation trend, while other classes showed disaggregation and fragmentation trends. Analysis further revealed that village expansion has significantly affected the process of land use/land cover change in the Luki Biosphere Reserve. However, the prediction results revealed that the primary forest will continue to increase while built-up area, fallow land and fields will follow a trend similar to a previous one. As for secondary forest and savannah, the forecast revealed a decrease of the extent during the period extending from 2020 to 2038. The present findings will benefit the decision makers, particularly in the sustainable natural resources management of the Luki Biosphere Reserve.

Contribution of ‘human induced fires’ to forest and savanna land conversion dynamics in the Luki Biosphere Reserve landscape, western Democratic Republic of Congo

“…The primary forest has lost 13.7% (Landsat) and 13.6% (MODIS) of its area essentially to the benefit of the secondary forest. The same result has been obtained by Michel et al (2021) in the Luki BR where he founded the decreasing of 13.26% for the primary forest from 1987 to 2002 considering only the LBR extent. The same trend was observed by Potapov et al (2012) who found an increase in gross forest cover loss of 13.8% between the period 2000-2010 in DR Congo.…”

“…The secondary forest recorded the greatest gain in surface followed by the anthropogenic zones that gained surface area both for Landsat and MODIS images but at various rates. The results found by Michel et al (2021) showed a decrease in the secondary forest class as this was considered separately from the fields and fallows. The fallow crop and built-up areas gained surface area significantly.…”

“…In this paper, we combine medium and high-resolution remote sensing datasets to study the effect of human-induced fires on land use and land cover (LULC) in the landscape of the LUKI Biosphere Reserve (LBR), one of the three biosphere reserves in the DRC, a relic of the Mayombe forest that has been exposed to several cycles of degradation (WWF-RDC 2009). The reserve is located between two main roads with heavy traffic (Matadi-Boma and Boma-Tshela) and is close to the megacity of Kinshasa, which exposes the reserve to overexploitation (Pendje and Mbaya 1992;Michel et al 2021;Cizungu et al 2021). Several studies (Rageade 2014;Michel et al 2021;Cizungu et al 2021) have highlighted the role of fire regimes in LULC changes and potentially explanatory causes without properly quantifying their contribution or their behaviour in the different types of LULC.…”

“…The reserve is located between two main roads with heavy traffic (Matadi-Boma and Boma-Tshela) and is close to the megacity of Kinshasa, which exposes the reserve to overexploitation (Pendje and Mbaya 1992;Michel et al 2021;Cizungu et al 2021). Several studies (Rageade 2014;Michel et al 2021;Cizungu et al 2021) have highlighted the role of fire regimes in LULC changes and potentially explanatory causes without properly quantifying their contribution or their behaviour in the different types of LULC. We propose here to quantify the contribution of fire to LULC, based on methods used to characterize fire originally developed in Zhihua, Ballantyne, and Annie Cooper (2019) and van Wees et al (2021) but adapted to the case and the scale of the Luki Biosphere Reserve and highlighting 2 forest types (primary and secondary forests) often combined in many studies.…”

Intelligent Error Correction of College English Spoken Grammar Based on the GA‐MLP‐NN Algorithm

“…Multilayer perceptron with multiple hidden layers is a deep learning structure. These three kinds of deep neural networks are applied in different fields and technologies [ 11 ]. The combination of the genetic algorithm and multilayer perceptron neural network is a hybrid method of DL and the metaheuristic algorithm [ 12 ].…”