Spatial and transient modelling of land use/land cover (LULC) dynamics in a Sahelian landscape under semi-arid climate in northern Burkina Faso

Yonaba, Roland; Koïta, Mahamadou; Mounirou, Lawani Adjadi; Fowé, Tazen; Queloz, Pierre; Biaou, Angelbert; Niang, Dial; Zouré, Cheick Oumar; Karambiri, Harouna; Yacouba, Hamma

doi:10.1016/j.landusepol.2021.105305

Cited by 55 publications

(40 citation statements)

References 65 publications

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“…Among them, the LIDAR-based geographic mapping method obtains higher accuracy of spatial land use classification planning and is closer to the real environment, but the reconstructed effect of this method lacks texture and only reflects 3D spatial information, and the cost is higher; the reconstructed texture of RGBD camera-based method is clearer, but it is not suitable for geographically complex the large-scale mapping. In contrast, the land use spatial classification planning method of GIS based on multivision stereo matching can obtain 3D information from 2D images by simulating human binoculars and using the principle of stereo vision to adapt to complex geographic environments and has the advantages of automatic, online, noncontact detection, high flexibility, low cost, and clear texture, which can be used to build 3D land use spatial classification planning for geographically large-scale environments [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

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With the development of China’s social economy as well as the accelerating urbanization construction and the expanding scale of cities, the integration of land use and urban land classification based on land use spatial planning has become an important task for the sustainable development of China at present. Land use spatial classification planning is the basic basis for all kinds of development and protection construction activities, and government land use spatial planning at all levels plays an important role in implementing major national, provincial, and municipal strategies and promoting the rational and effective use of land use space. By briefly describing the spatial classification of land use and analyzing the idea of systematic integration of land use, this paper provides guidance and reference for exploring the construction of urban land use classification under land use spatial planning, aiming to improve the classification system of land use spatial planning. A neural network-based land use classification algorithm is proposed for the problems of few labeled samples of remote sensing images with high spatial resolution and feature deformation due to sensor height changes in land use spatial classification planning. By multiscale adaptive fusion of multiple convolutional layer features, the impact of feature deformation on classification accuracy is reduced. To further improve the classification accuracy, the depth features extracted from the pretraining network are used to pretrain the multiscale feature fusion part and the fully connected layer, and the whole network is fine-tuned using the augmented dataset. The experimental results show that the adaptive fusion method improves the fusion effect and effectively improves the accuracy of land use spatial classification planning.

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Section: Introductionmentioning

confidence: 99%

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

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“…The model can predict spatial and temporal land changes for the next decades. It can also be a framework to understand underlying drivers of changes [44]. In the next step, we can use the model to assess the urbanization rates of Shenyang in the future, which can be used to predict the trend of ExHP and provide recommendations for urban planning.…”

Section: Discussionmentioning

confidence: 99%

Influence of Urbanization on Spatio-Temporal Characteristics of Extreme Hourly Precipitation in Shenyang

Ao¹,

Zhai

Zhao³

et al. 2022

Land

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Understanding changes in extreme hourly precipitation is critical to urban planners for building more sustainable and resilient cities. In this study, we use satellite nighttime light data, urban land area data, population, and economic data to objectively classify urban and rural stations. Based on the hourly precipitation data from national meteorological stations in 1974–2020 and from regional automatic weather stations in 2005–2020 in Shenyang (China), the spatio-temporal distribution characteristics of the thresholds, maximums, intensities, and frequencies of extreme hourly precipitation (ExHP) in urban and rural areas are analyzed and compared. The results show that the large-value centers of ExHP thresholds, maximums, and intensities are mainly concentrated in urban areas. Both the frequency and intensity of ExHP at urban stations are obviously larger than those at rural stations, and the peaks mainly appear at night for stations of both two types. From 1974 to 2020, the average frequency and intensity of ExHP at urban stations both show increasing trends, with the increasing rate being much higher than those at rural stations. In terms of temporal variation, precipitation events of the abrupt type are the most frequent, accounting for 48.6% of the total, followed by the growing type (42.7%) and continuous type (8.7%). ExHP events of the abrupt type are mostly concentrated in Kangping County and Faku County of Northern Shenyang, but rarely occur in Xinmin City. ExHP events of the growing type are mainly found in Xinmin City and the municipal district of Shenyang. For urban stations, the ExHP frequency decreases in the early stage of urbanization, while increasing evidently during the rapid urbanization stage. However, the situation is just the opposite for rural stations. This indicates that the rapid urbanization in Shenyang has a certain impact on the increase in ExHP.

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“…The explanatory power of influential variables was also evaluated by Cramer's V statistic which is counted using Equation (Yonaba et al, 2021).

V = \sqrt{\frac{x^{2 / n}}{\min (k - 1, r - 1)},}

…”

Section: Methodsmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

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Spatial and transient modelling of land use/land cover (LULC) dynamics in a Sahelian landscape under semi-arid climate in northern Burkina Faso

Cited by 55 publications

References 65 publications

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Influence of Urbanization on Spatio-Temporal Characteristics of Extreme Hourly Precipitation in Shenyang

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

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