Upgrading the SLEUTH urban-growth and land-use-change model, realizing its full capability in modeling change simultaneously in land-use and land-cover types, and using it as a self-organizing dynamic land-use planning tool have been the three main objectives of this study. In doing so, SLEUTH was applied to design a better plan for future and assess two scenarios concerning land-use and land-cover changes in Gorgan Township of the Golestan Province of Iran. Four land-use and land-cover maps were derived from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus imagery through a hybrid method including unsupervised, supervised, and on-screen classification for four years. To provide a more desirable forecast of future land-use and land-cover changes, SLEUTH's exclusion layer was combined with an urbanization-suitability layer from a multicriteria evaluation (MCE) using fifteen map layers that most influence land suitability for urban development. The layers used in the MCE process were related to landform, vegetation, soil and geology, and surrogate socioeconomic factors; hence, they portrayed the desirability of the urban growth. SLEUTH was used for forecasting with both this new and a standard exclusion layer. Using the new layer, the fragmentation of the future land-use pattern was controlled and urban development along roads was restrained, thereby safeguarding the remaining urban green space and remnant rural vegetation patches. The results were also compared with a separate site selection process for future urban development showing the desirability of MCE-guided SLEUTH modeling over original SLEUTH and the standalone urban MCE in terms of landform, surrogate socioeconomic factors, and landscape metrics such as patch size, shape, and proximity and fractal dimension. As SLEUTH derives change rules simultaneously for different land-use and land-cover types in a self-modifying self-organizing manner, we showed the approach can be regarded as a tool for dynamic land-use planning.
Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources. In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETMþ) reflectance values. We used multivariate regression technique to predict stand volume and tree density. The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R 2 ¼ 43%; root mean square error (RMSE) ¼ 97.4 m 3 ha -1 ) than other ETMþ bands and vegetation indices. In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R 2 ¼ 73.4%; RMSE ¼ 170.13 ha -1 ) than other combinations of ETM þ bands and vegetation indices. Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETMþ reflectance values and the utility of transformed bands in modelling stand volume and tree density. Based on the results of this study, we conclude that ETMþ data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area. Forest managers could use ETMþ data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.