Land Change Modeler (LCM) for Ecological Sustainability. Land Change Modeler is available as a software extension for use with ESRI’s ArcGIS product. It is developed by Clark Lab, a prediction software which also incorporates tools that allows you to analyze, measure and project the impacts on habitat and biodiversity. LCM provides a robust set of tools for the analysis of change and the creation for the viable plans and scenarios for the future. LCM implication has the following main tasks: 1.Change analysis; 2.Change prediction;3.Impact assessment for habitability and biodiversity;4.Planning interventions The LCM is testing the stability of linked social and ecological systems, through scenario building. The Land Change Modeler allows the user to specify planning interventions that may alter the course of development including constrains and incentives, such as proposed reserved areas, infrastructure modifications, and biological corridors. It is analyzing complex change and behavior based upon dynamic( time dependable) and static(not changing over time) process of the context (land cover). Hard prediction = (static change), offering only a single realization, whereas soft prediction= (dynamic change) looks at change potentials. “The distinction between hard and soft prediction is very important. At any point in time, there are typically more areas that have the potential to change than will actually change. Thus a commitment to a single prediction is a commitment to our “best guess” at just one of many highly plausible scenarios. If you compare the result to what actually occurred, the change of getting it right is thus quite slim. A soft prediction, however, maps out all the areas that are thought to be plausible candidates of change. If the concern is with the risks to habitat and biodiversity, this may be better format of change. “ LCM using one of two methodologies—a multi-layer perceptron neural network (MLP) or logistic regression (LR). Logistic regression is used extensively in the medical and social sciences as well as marketing applications, such as predicting the customer’s propensity to purchase a product. In neuroscience, a neural network describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signaling targets define or recognizable circuit. Neural networks are non-linear statistical data modeling. They can be used to model complex relationships between inputs and outputs or to find patterns in data. The Dynamic change is using Markov Change Analysis, which is a mathematical system that undergoes transitions from one state to another (from a finite or countable number of possible states) in a chainlike manner. Tool for land cover change analysis, allowing you to quickly map changes in the landscape, identifying and uncover land class transitions, trends, and monitor ongoing plans, by using parameters both static and dynamics and deriving prediction methodologies from both math and science.