Analysis and Prediction of Land Use Changes Related to Invasive Species and Major Driving Forces in the State of Connecticut

Abstract

Land use and land cover (LULC) patterns play an important role in the establishment and spread of invasive plants. Understanding LULC changes is useful for early detection and management of land-use change to reduce the spread of invasive species. The primary objective of this study is to analyze and predict LULC changes in Connecticut. LULC maps for 1996, 2001 and 2006 were selected to analyze past land cover changes, and then potential LULC distribution in 2018 was predicted using the Multi-Layer Perceptron Markov Chain (MLP_MC) model. This study shows that the total area of forest has been decreasing, mainly caused by urban development and other human activity in Connecticut. The model predicts that the study area will lose 5535 ha of deciduous forest and gain 3502 ha of built-up area from 2006 to 2018. Moreover, forests near built-up areas and agriculture lands appear to be more vulnerable to conversion. Changes in LULC may result in subtle spatial shifts in invasion risk by an abundant invasive shrub, Japanese barberry (Berberis thunbergii). The gain of developed areas at the landscape scale was most closely linked to increased future invasion risk. Our findings suggest that the forest conversion needs to be controlled and well managed to help mitigate future invasion risk.

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

“…Comparing our study to others that used the same MLP Markov Chain modeling method that we used [32,[34][35][36][37][38]72], we can find a few important aspects for discussion. One is that many previous studies did not provide reasoning for why certain driver variables were considered or discuss why certain factors were heavily influencing different types of LULC conversions.…”

“…The explanatory power of all of these variables in relation to different LULC transitions was computed and examined by using Cramer's V [38]. Also known as Cramer's Coefficient (V), this method is used for quantifying the explanatory power of each variable, which is an optional quick test used to determine whether the variables are worthy of consideration in the model [29].…”

“…Triantakonstantis and Stathakis [32] used MLP for modeling future LULC transition probabilities based on information on past LULC changes and geomorphic drivers such as elevation, slope, and distance variables from specific land features, etc. Similar methods can be seen in a number of studies, although the number and types of driver variables utilized vary [27,[34][35][36][37][38].…”

Understanding Land Use and Land Cover Dynamics from 1976 to 2014 in Yellow River Delta

“…Visual interpretation of land use types based on elements such as color, tone, texture, form, size, presence of shadows, and the location of infrastructures [45,46] has been the main approach for identifying land use changes because it can provide more accurate land use maps compared with automatic classification [47,48]. In this study, the land use data series were acquired by interpreting the basic land use map in 2010 and detecting the changing parts between adjacent time periods of Landsat images manually.…”

Prediction of Land Use Change in Long Island Sound Watersheds Using Nighttime Light Data

“…The land use activities of the millions of people information system and remote sensing software. The prediction ability of these two models has been demonstrated in many studies [30][31][32][33][34]. In order to refine our prediction we first compared these two models, and then used the better one (MLP-MC model) for prediction.…”