Integration of logistic regression,Markov chain and cellular automata models to simulate urban expansion

This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades.     

Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.     

Dynamic modeling of forest conversion: Simulation of past and future scenarios of rural activities expansion in the fringes of the Xingu National Park,Brazilian Amazon

The present work is committed to simulate the expansion of agricultural and cattle raising activities within a watershed located in the fringes of the Xingu National Park, Brazilian Amazon. A spatially explicit dynamic model of land cover and land use change was used to provide both past and future scenarios of forest conversion into such rural activities, aiming to identify the role of driving forces of change in the study area. The employed modeling platform – Dinamica EGO – consists in a cellular automata environment that embodies neighborhood-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables and legal restrictions drove this simulation model, and statistical validation tests were then conducted for the generated past simulations (from 2000 to 2005), by means of multiple resolution fitting methods. Based on optimal calibration of past simulations, future scenarios were conceived, so as to figure out trends and spatial patterns of forest conversion in the study area for the year 2015. In all simulated scenarios, pasturelands remained nearly stable throughout the analyzed period, while a large expansion in croplands took place. The most optimistic scenario indicates that more than 50% of the natural forest will be replaced by either cropland or pastureland by 2015. This modeling experiment revealed the suitability of the adopted model to simulate processes of forest conversion. It also indicates its possible further applicability in generating simulations of deforestation for areas with expanding rural activities in the Amazon and in tropical forests worldwide.     

Exploration of spatial and temporal characteristics of PM2.5 concentration in Guangzhou,China using wavelet analysis and modified land use regression model

ABSTRACT

This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou (China) from 1 June 2012 to 31 May 2013 based on wavelet analysis tools, and discuss its spatial distribution using geographic information system software and a modified land use regression model. In this modified model, an important variable (land use data) is substituted for impervious surface area, which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method. Impervious surface has higher precision than land use data because of its sub-pixel level. Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood. Results include: (1) the highest concentration of PM2.5 occurs in October and the lowest in July, respectively; (2) average concentration of PM2.5 in winter is higher than in other seasons; and (3) there are two high concentration zones in winter and one zone in spring.