Using neural networks and GIS to forecast land use changes: a Land Transformation Model

By Bryan C. Pijanowski, Daniel G. Brown, B. Shellito, Gaurav Manik. 2002

775 citations recorded by the source corpus.

Subfields: Ecology, Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3f481fe3f753b26f1b0b46c4585b9d39617d01aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Robert Gilmore Pontius Jr.
2. A. Veldkamp
3. Peter H. Verburg
4. Bryan C. Pijanowski
5. Keith C. Clarke
6. Ton C. M. de Nijs

In Economics and Econometrics

1. A. Veldkamp
2. Peter H. Verburg
3. Y. Zhaoc
4. Md. Kamruzzaman
5. Haitao Maa
6. Yan-sui Liu
7. Kasper Kok
8. H. Wanga

In Global and Planetary Change

1. Yan-sui Liu
2. Robert Gilmore Pontius Jr.
3. Peter H. Verburg
4. A. Veldkamp
5. Ralf Wieland
6. Claas Nendel
7. Tobia Lakes
8. Yufu Chen

Citations in the corpus

• Exploring the relationship between urbanization and the eco-environment-A case study of Beijing-Tianjin-Hebei region (2014, background): “…Wang t al., 2013a,b), environmental changes forecasting with urban prawl (Liu et al., 2011a,b), urban suitability assessment (Fischer nd Sun, 2001; Pijanowski et al., 2002; Wang et al., 2013a,b), energy se and CO2 emissions (Martinez-Zarzoso and Maruotti, 2011), nd water resources (Graniel et…”
• Spatio-temporal change of urban–rural equalized development patterns in China and its driving factors (2013, background): “A first-order Markov model is the model of a system inwhich probability distribution over next state is assumed to only depend on current state, but not on previous ones (non-aftereffect) (Veldkamp and Lambin, 2001; Fischer and Sun, 2001; Pijanowski et al., 2002; Guan et al., 2011).”
• Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh (2013, methodology): “In this research, the combination of Markov Chain [59] and MLP modeling techniques [60] were applied to project the future land cover patterns due to their accuracy in outcomes and wide acceptability [61,62].”
• Comparing the input, output, and validation maps for several models of land change (2008, background): “Land Transformation Model (2005) uses artificial neural networks to simulate land change (Pijanowski et al. 2000, 2002, 2005).”
• Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China (2021, background): “CLUE-S, GeoSOS-FLUS, LTM, Fu et al., 2018; Liang et al., 2018a; Pijanowski et al., 2002, 2005; Ver-burg and Veldkamp, 2004; Zhang et al., 2013); others are not (e. .”