Spatio-temporal analysis of forest fire events in the Margalla Hills,Islamabad,Pakistan using socio-economic and environmental variable data with machine learning methods

Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study considers both environmental(altitude,precipitation,forest type,terrain and humidity index)and socioeconomic(population density,distance from roads and urban areas)factors to analyze how human behavior affects the risk of forest fires.Maximum entropy(Maxent)modelling and random forest(RF)machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills.The receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were used to compare the models.We studied the fire history from 1990 to 2019 to establish the relationship between the probability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999 s,2009 s,and 2019 s were 0.532,0.569,and 0.518,respectively;using RF,they were 0.782,0.825,and 0.789,respectively.Fires were mainly distributed in urban areas and their probability of occurrence was related to accessibility and human behaviour/activity.AUC principles for validation were greater in the random forest models than in the Maxent models.Our results can be used to establish preventive measures to reduce risks of forest fires by considering socio-economic and environmental conditions.

Soil erosion assessment by RUSLE model using remote sensing and GIS in an arid zone

In this research,we used the Revised Universal Soil Loss Equation(RUSLE)and Geographical Information System(GIS)to predict the annual rate of soil loss in the District Chakwal of Pakistan.The parameters of the RUSLE model were estimated using remote sensing data,and the erosion probability zones were determined using GIs.The estimated length slope(LS),crop management(C),rainfall erosivity(R),soil erodibility(K),and support practice(P)range from 0-68,227,0-66.61%,0-0.58,495.99-648.68 MJ/mm.t.ha^(-1).year^(-1),0.15-0.25 MJ/mm.t.ha^(-1).year^(-1),and 1 respectively.The results indicate that the estimated total annual potential soi loss of approximately 4,67,064.25 t.ha^(-1).year^(-1) is comparable with the measured'sediment ioss of 11,631 t.ha^(-1).year^(-1) during the water year 2020.The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 t.ha^(-1).year^(-1).In this study,we also used,Landsat imagery to rapidly achieve actual land use classification.Meanwhile,38.i3%of the region was threatened by very high soil erosion,where the quantity of soil erosion ranged from 365487.35 t.ha^(-1).year^(-1),Integrating GIS and remote sensing with the RUSLE model helped researchers achieve their final objectives.Land-use planners and decision-makers use the result's spatial distribution of soil erosion in District Chakwal for conservation and management planning.

Urban sprawl in provincial capital cities in China: evidence from multi-temporal urban land products using Landsat data

The onset of global urbanization has led to a population shift from rural to urban areas and the expansion of urban development [1-3]. Urban land areas are growing at faster rates than their populations in most parts of the world [4]. The urban sprawl process refers to dispersed land development characterized by low-density, unplanned, and uneven patter ns of growth duri ng urban expansion. This process can result in inefficient use of land resources contradictory to the principle of sustainable development [5]. Additionally, urban sprawl has various environmental, economic, and social consequences, including the following: loss of agricultural land, higher costs for transportation infrastructure, increased landscape fragmentation, degeneration of soil ecological functions, and reduced ecosystem resilience. It is crucial to understand the trends and patterns of urban sprawl to implement a framework for sustainable urban development in developing regions.