Modeling and Simulating Land Use/Cover Change Using Artificial Neural Network From Remotely Sensing Data

Abstract

BUĞDAY, E.; ERKAN BUĞDAY, S. Modeling and simulating land use/cover change using artificial neural network from remotely sensing data. CERNE, v. 25, n. 2, p.246-254, 2019. HIGHLIGHTSApplicability of decision support systems in landscape planning.To reveal the spatio-temporal land use and land cover changes.Estimation of land use and cover change by human population movements. ABSTRACTIncreasing population, mobility and requirements of human beings have significant effects on the dynamics of land use and land cover. Today, these impacts need to be understood and analyzed for the applicability of decision support systems, which are an important tool in the management of natural resources, urban and rural areas. The aim of this study is to detect the temporal and spatial changes of land cover and human population, in northwest Turkey. For this purpose, using satellite images of 1997-2007 and 2017 years' land cover was estimated for 2027 by ANN (Artificial Neural Network) approach. Kappa values are 93%, 87% and 95% for 1997, 2007 and 2017 respectively. As a result, learning success was 80.6%, and correctness validation value was 90.1% for 2027 simulation. In parallel, the spatial analysis of the population was conducted for 2000-2007-2017. Using the exponential rate of change; the population was predicted to increase by concentrating on the urban area and the rural areas surrounding the urban (with a rate of 2.019%) for 2027. According to the results; rural population, urban population, forest, and built-up areas is estimated to increase by 4.14%, 5.58%, 2.72%, and 0.77% respectively from 2017 to 2027, while the agricultural and water area is estimated to decrease by 3.47% and 0.02% respectively. Consequently, the observation of population movements and the use of the ANN approach in simulations could be suggested for the success of planning in forest and land management.v.25 n.

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

“…Under rapid regional socioeconomic development and urban mechanisms, the GBA experienced a transformation that has had a tremendous impact on the spatial pattern of LULC changes [71,72]. In this study, we modeled the spatiotemporal transition potential and future scenario of LULC with the help of the Modules for Land-Use Change Simulation (MOLUSCE) plugin within QGIS software [73][74][75]. The MOLUSCE plugin incorporates some well-known algorithms, such as artificial neural networks (ANNs) and Monte Carlo cellular automata (CA) modeling approaches [76].…”

“…The MOLUSCE plugin incorporates some well-known algorithms, such as artificial neural networks (ANNs) and Monte Carlo cellular automata (CA) modeling approaches [76]. We used remote sensing data from 1980 to 2020 with a 10-year interval, spatial variables, DEM, slope [45,77,78], population [45,74], GDP [45], distance from roads [45,78], distance from streams [74], distance from a city [77] and the CA-ANN approach for spatiotemporal transition potential modeling and future LULC simulation for 2030, 2040, and 2050. After the simulation and prediction of LULC, we used an intensity analysis approach at three levels, i.e., interval level, category level and transition level, and further engaged indices to quantify the annual rate of change in LULC classes.…”

Land Use and Land Cover Change Detection in the Bhavani Basin of India through Remote Sensing Approach

“…The technique was used to estimate image classification accuracy by comparing the LULC map to a LULC reference map (Aneesha Satya et al, 2020). As a result, a thorough accuracy assessment must include a report on overall precision, consumer accuracy, and producer accuracy, all of which were examined using the Kappa coefficient (Buğday & Erkan Buğday, 2019).…”

Apropriação do cerrado e pressão nos recursos naturais da terra indígena Avá-Canoeiro e entorno (Goiás)

“…Nesse sentido, contemplou além da cobertura e uso da terra (1989, 2009 e 2019); a disposição do relevo (Declividade e Hipsometria); a rede rodoviária (distância entre as Rodovias Estaduais e Estradas não pavimentadas); e o arranjo dos Recursos Hídricos (densidade dos Cursos d'água/drenagem). A definição destas variáveis espaciais levaram em consideração, sobretudo, as influências que as mesmas exercem no contexto das mudanças em escala de paisagem, como destaca Lee (2005), Kamusoko et al (2009), Nogueira et al (2017), e Buğday (2019.…”