Land Use Land Cover Change Detection by Using Remote Sensing Data in Akaki River Basin

Land use land cover change (LULCC) is the result of the long time process of natural and anthropogenic activities that has been practiced on the land. GIS and remote sensing are the best tools that support to generate the relevant land use/cover change in the basin. This study was conducted in the Akaki River basin to detect land use land cover changes within the 30 years period (1985-2015) by using landsat imagery data acquired from the GCF. Supervised maximum likelihood algorithm classification were deployed to classify land use/cover into four prominent land use groups and data's were processed by using ERDAS imagine 2014 and ArcGIS10.1 software. In the basin dominant LULC was agricultural land use which accounts around 56.28% and the second largest is built-up area by 31.51% and the rest, forest(11.9%) and water body(0.31%) coverage were takes third and fourth position(as 2015 data). The rapid expansion of Addis Ababa city consumes more fertile land near to the city. According to the projected LULCC for 2030 the proportion of agricultural and built-up area near to each other, i.e., agricultural land reduced to 42.33% and urban or built-up area increased to 41.63%. One good thing observed in the basin was an increment of the forest land in between 2011 and 2015 by 23.85% whereas in between 1985 and 2015 the annual rate of change was by 4.2. This may be due to the implementation of green-economy building strategy of the government and other stakeholders to rehabilitate the degraded lands in order to achieve MDG and SDG goals. Urbanization, industrialization, commercial center enlargement and population explosion in the main city Addis Ababa grabs more fertile and productive lands which supports more semi-urban communities. Hence, the government should consider the dramatic and drastic horizontal expansion of the urbanization which resulted due to lack of appropriate master plan for the city and towns in the basin to protect the loss agricultural productive lands

Land use in rural New Zealand: spatial land use, land-use change, and model validation

Abstract Land is an important social and economic resource. Knowing the spatial distribution of land use and the expected location of future land-use change is important to inform decision makers. This paper documents and validates the baseline land-use maps and the algorithm for spatial land-use change incorporated in the Land Use in Rural New Zealand model (LURNZ). At the time of writing, LURNZ is the only national-level land-use model of New Zealand. While developed for New Zealand, the model provides an intuitive algorithm that would be straightforward to apply to different locations and at different spatial resolutions. LURNZ is based on a heuristic model of dynamic land-use optimisation with conversion costs. It allocates land-use changes to each pixel using a combination of pixel probabilities in a deterministic algorithm and calibration to national-level changes. We simulate out of sample and compare to observed data. As a result of the model construction, we underestimate the “churn” in land use. We demonstrate that the algorithm assigns changes in land use to pixels that are similar in quality to the pixels where land-use changes are observed to occur. We also show that there is a strong positive relationship between observed territorial-authority-level dairy changes and simulated changes in dairy area

Land Use Change in Indonesia

With an estimated loss of up to 20 million ha of forest over the past decade, deforestation in Indonesia has come to the forefront of global environmental concerns. Indonesia is one of the most important areas of tropical forests worldwide. In addition to providing a multitude of benefits locally, including both products and services, these forests are also of global importance because of their biodiversity and the carbon they sequester. Despite the benefits they provide, Indonesia’s forests have been under considerable threat in past decades, and the extent of forest cover has declined considerably. This paper takes advantage of new data on the extent and distribution of forest cover change in Indonesia to examine its causes and effects. The paper begins by summarizing the long-term trends in land use change in Indonesia, and the new data on loss of forest cover during the period 1985-1997. It then discusses why this land use change is likely to be undesirable in many cases. Land use change can at times be beneficial, but there are good reasons to believe that current patterns of land use change in Indonesia are in fact socially sub-optimal. The paper then reviews the incentives faced by the major actors in land use change—loggers, estate crop producers, and smallholders—and the reasons their decisions concerning land use change, while privately optimal, are likely to be socially sub-optimal. It also briefly examines the effect that the East Asian financial crisis has had on these incentives. Particular attention is paid to mangrove forests, because of their important ecological role.Deforestation, Land Use, Biodiversity, Environmental Services, Indonesia

Genetic Land - Modeling land use change using evolutionary algorithms

Future land use configurations provide valuable knowledge for policy makers and economic agents, especially under expected environmental changes such as decreasing rainfall or increasing temperatures, or scenarios of policy guidance such as carbon sequestration enforcement. In this paper, modelling land use change is designed as an optimization problem in which landscapes (land uses) are generated through the use of genetic algorithms (GA), according to an objective function (e.g. minimization of soil erosion, or maximization of carbon sequestration), and a set of local restrictions (e.g. soil depth, water availability, or landscape structure). GAs are search and optimization procedures based on the mechanics of natural selection and genetics. The GA starts with a population of random individuals, each corresponding to a particular candidate solution to the problem. The best solutions are propagated; they are mated with each other and originate “offspring solutions” which randomly combine the characteristics of each “parent”. The repeated application of these operations leads to a dynamic system that emulates the evolutionary mechanisms that occur in nature. The fittest individuals survive and propagate their traits to future generations, while unfit individuals have a tendency to die and become extinct (Goldberg, 1989). Applications of GA to land use planning have been experimented (Brookes, 2001, Ducheyne et al, 2001). However, long-term planning with a time-span component has not yet been addressed. GeneticLand, the GA for land use generation, works on a region represented by a bi-dimensional array of cells. For each cell, there is a number of possible land uses (U1, U2, ..., Un). The task of the GA is to search for an optimal assignment of these land uses to the cells, evolving the landscape patterns that are most suitable for satisfying the objective function, for a certain time period (e.g. 50 years in the future). GeneticLand develops under a multi-objective function: (i) Minimization of soil erosion – each solution is validated by applying the USLE, with the best solution being the one that minimizes the landscape soil erosion value; (ii) Maximization of carbon sequestration – each solution is validated by applying atmospheric CO2 carbon uptake estimates, with the best solution being the one that maximizes the landscape carbon uptake; and (iii) Maximization of the landscape economic value – each solution is validated by applying an economic value (derived from expert judgment), with the best solution being the one that maximizes the landscape economic value. As an optimization problem, not all possible land use assignments are feasible. GeneticLand considers two sets of restrictions that must be met: (i) physical constraints (soil type suitability, slope, rainfall-evapotranspiration ratio, and a soil wetness index) and (ii) landscape ecology restrictions at several levels (minimum patch area, land use adjacency index and landscape contagion index). The former assures physical feasibility and the latter the spatial coherence of the landscape. The physical and landscape restrictions were derived from the analysis of past events based on a time series of Landsat images (1985-2003), in order to identify the drivers of land use change and structure. Since the problem has multiple objectives, the GA integrates multi-objective extensions allowing it to evolve a set of non-dominated solutions. An evolutive type algorithm – Evolutive strategy (1+1) – is used, due to the need to accommodate the very large solution space. Current applications have about 1000 decision variables, while the problem analysed by GeneticLand has almost 111000, generated by a landscape with 333*333 discrete pixels. GeneticLand is developed and validated for a Mediterranean type landscape located in southern Portugal. Future climate triggers, such as the increase of intense rainfall episodes, is accommodated to simulate climate change . This paper presents: (1) the formulation of land use modelling as an optimization problem; (2) the formulation of the GA for the explicit spatial domain, (3) the land use constraints derived for a Mediterranean landscape, (4) the results illustrating conflicting objectives, and (5) limitations encountered.

Analyzing Land Use Change In Urban Environments

This four-page fact sheet provides a brief summary of the analysis of land use in urban environments. Topics include the rapid growth in urban populations, some of the methods used to analyze land use change (mapping, databases, time series documents), and some of the concerns and possible consequences created by the rapid shift of human populations to urban centers. Educational levels: High school, Undergraduate lower division, Undergraduate upper division, Graduate or professional

ARTMAP Neural Network Classification of Land Use Change

The ability to detect and monitor changes in land use is essential for assessment of the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could be used for detecting and monitoring long-term changes. Existing methods are insufficient for detecting subtle long-term changes from high-dimensional data. This project employs neural network architectures as alternatives to conventional systems for classifying changes in the status of agricultural lands from a sequence of satellite images. Landsat TM imagery of the Nile River delta provides a testbed for these land use change classification methods. A sequence often images was taken, at various times of year, from 1984 to 1993. Field data were collected during the summer of 1993 at88 sites in the Nile Delta and surrounding desert areas. Ground truth data for 231 additional sites were determined by expert site assessment at the Boston University Center for Remote Sensing. The field observations are grouped into classes including urban, reduced productivity agriculture, agriculture in delta, desert/coast reclamation, wetland reclamation, and agriculture in desert/coast. Reclamation classes represent land use changes. A particular challenge posed by this database is the unequal representation of various land use categories: urban and agriculture in delta pixels comprise the vast majority of the ground truth data available in the database. A new, two-step training data selection method was introduced to enable unbiased training of neural network systems on sites with unequal numbers of pixels. Data were successfully classified by using multi-date feature vectors containing data from all of the available satellite images as inputs to the neural network system.National Science Foundation Graduate Fellowship; National Science Foundation (SBR 95-13889); Office of Naval Research (N00014-95-I-409, N00014-95-0657); Air Force Office of Scientific Research (F49620-0l-1-0397)