A Machine Learning Model to Predict Urban Sprawl Using Official Land-use Data

The rate of global urbanization is constantly increasing. As a result of the massive population growth, there is an increasing demand for further urban development, especially in developing regions such as Aswan city. This paper aims to examine the usage official land-use data in predicting future urban growth until 2046, moreover, to define urban driving forces in case study area. This was done using Similarity weighted model, a machine learning based model to simulate future urban growth. The results show that official land-use data produce a slightly better results’ accuracy than remote sensing sources within small to medium scales. The results although reveal that for study region, urban area is expected to expand to cover an area of almost 4460 Feddan by year 2046. The outcome of this research assesses decision makers to accurately predict future urban sprawl areas using available official land-use data

Proportional Variation of Potential Groundwater Recharge as a Result of Climate Change and Land-Use: A Study Case in Mexico

Artículo en revista indexadaThis work proposes a methodology whereby the selection of hydrologic and land-use cover change (LUCC) models allows an assessment of the proportional variation in potential groundwater recharge (PGR) due to both land-use cover change (LUCC) and some climate change scenarios for 2050. The simulation of PGR was made through a distributed model, based on empirical methods and the forecasting of LUCC stemming from a supervised classification with remote sensing techniques, both inside a Geographic Information System. Once the supervised classification was made, a Markov-based model was developed to predict LUCC to 2050. The method was applied in Acapulco, an important tourism center for Mexico. From 1986 to 2017, the urban area increased 5%, and by 2050 was predicted to cover 16%. In this period, a loss of 7 million m3 of PGR was assumed to be caused by the estimated LUCC. From 2017 to 2050, this loss is expected to increase between 73 and 273 million m3 depending on the considered climate change scenario, which is the equivalent amount necessary for satisfying the water needs of 6 million inhabitants. Therefore, modeling the variation in groundwater recharge can be an important tool for identifying water vulnerability, through both climate and land-use change.CONACyT Centro de Ciencias de Desarrollo Regional (CCDR

Ecological risk assessment based on land cover change: A case of Zanzibar-Tanzania, 2003-2027

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesLand use under improper land management is a major challenge in sub-Saharan Africa, and this has drastically affected ecological security. Addressing environmental impacts related to this major challenge requires faster and more efficient planning strategies that are based on measured information on land-use patterns. This study was employed to access the ecological risk index of Zanzibar using land cover change. We first employed Random Forest classifier to classify three Landsat images of Zanzibar for the year 2003, 2009 and 2018. And then the land change modeler was employed to simulate the land cover for Zanzibar City up to 2027 from land-use maps of 2009 and 2018 under business-as-usual and other two alternative scenarios (conservation and extreme scenario). Next, the ecological risk index of Zanzibar for each land cover was assessed based on the theories of landscape ecology and ecological risk model. The results show that the built-up areas and farmland of Zanzibar island have been increased constantly, while the natural grassland and forest cover were shrinking. The forest, agricultural and grassland have been highly fragmented into several small patches relative to the decrease in their patch areas. On the other hand, the ecological risk index of Zanzibar island has appeared to increase at a constant rate and if the current trend continues this index will increase by up to 8.9% in 2027. In comparing the three future scenarios the results show that the ERI for the conservation scenario will increase by only 4.6% which is at least 1.6% less compared to 6.2% of the business as usual, while the extreme scenario will provide a high increase of ERI of up to 8.9%. This study will help authorities to understand ecological processes and land use dynamics of various land cover classes, along with preventing unmanaged growth and haphazard development of informal housing and infrastructure

Similarity weighted instance-based learning for the Generation of transition potentials in land use change modeling

Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance-based learning algorithm based on the logic of the K-Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi-Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform. © 2010 Blackwell Publishing Ltd

Analysis of land use and land cover change in Kiskatinaw River Watershed: A remote sensing, gis & modeling approach.

This thesis study was conducted to capture the land use and land cover (LULC) change dynamics in Kiskatinaw River Watershed, BC, Canada. A combination of remote sensing, GIS and modeling approach was utilized for this purpose. Landsat TM and ETM+ satellite images of the years 1984, 1999 and 2010 were analyzed using object oriented image classification technique to produce LULC maps and detect the associated changes. The dynamic nature of different forest types, increase in built-up area and significant depletion of wetlands were found to be notable among the detected LULC changes. Thereafter, a multi-layer perception neural network technique was used to model transition potentials of various LULC types, which was later realized with a Markov Chain land use model to predict future changes. The integration of advanced satellite remote sensing tools and neural network aided Markov Chain modeling was illustrated to be an effective means for LULC change detection and prediction in Kiskatinaw River Watershed. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b189079

Integração de modelagem espacial e sensoriamento remoto como método de avaliação para a lista vermelha de ecossistemas : campo com barba-de-bode, Tio Grande do Sul, Brasil

Cerca de 45,8% das áreas campestres existentes no mundo foram, em algum momento, submetidas a processos de degradação, fragmentação ou conversão da sua vegetação nativa. A falta de uma metodologia padronizada e de critérios universais faz com que a comparação do estado de conservação entre distintos ecossistemas não seja possível. Para superar esse obstáculo, em 2013 a União Internacional para a Conservação da Natureza (IUCN) publicou a Lista Vermelha de Ecossistemas (RLE), que dá diretrizes para o diagnóstico de ameaça segundo 5 critérios replicáveis a todos os tipos de ecossistemas. Desse modo, considerando a extensão dos ecossistemas campestres das Savanas Uruguaias, a escassez de dados ópticos prévios ao ano de 1972 e de sítios de medição de variáveis abióticas, o objetivo geral deste estudo foi avaliar o uso de dados de sensoriamento remoto integrados a técnicas de modelagem espacial como método de avaliação para RLE, mais especificamente no Campo com Barba-de-Bode do Bioma Pampa. Os critérios A e B – Redução na Distribuição Geográfica e Distribuição Geográfica Restrita – foram avaliados a partir do Land Change Modeler para TerrSet®, utilizando-se dados do projeto MapBiomas em conjunto com 9 variáveis condicionantes relacionadas a conversão agropecuária na região e teve como resultado mapas de uso e cobertura da terra (LULC) para o ano de 1970. A avaliação de Degradação Ambiental (Critério C) foi conduzida no módulo Carbon para o software InVEST considerando o declínio no armazenamento de carbono decorrente das alterações no LULC identificadas no critério A. O critério D (Ruptura de interações e processos bióticos) foi omitido do diagnóstico em virtude da escassez de registros bióticos para o período de referência. Por fim, o critério E, probabilidade de colapso do ecossistema, foi parcialmente avaliado no software Dinamica EGO com a geração de cenários futuros de LULC para os anos de 2070 e 2120 considerando as tendências de transição ocorridas nos últimos 35 anos. Nosso diagnóstico enquadra o CBB como Vúlneravel/Em perigo segundo o critério A1, Em Perigo (EN) segundo o critério A2, Vulnerável segundo os critérios B1 e C1, Pouco Preocupante (LC) segundo o critério B2 e Dados Insuficientes (DD) no critério E. Estudos futuros devem ser conduzidos de modo a avaliar os demais sistemas ecológicos das Savanas Uruguaias, para que iniciativas de conservação tenham como respaldo a comparação do risco ao qual as áreas estão submetidas e, desse modo, priorizem esforços nas mais vulneráveis ao colapso.About 45.8% of the world's existing grasslands have at some point been subjected to processes of degradation, fragmentation, or conversion of their native vegetation. The lack of a standardized methodology and universal criteria makes comparison of conservation status between different ecosystems not possible. To overcome this barrier, in 2013 the International Union for Conservation of Nature (IUCN) published the Red List of Ecosystems (RLE), which gives guidelines for threat assessment according to 5 criteria replicable to all types of ecosystems. Thus, considering the extent of the grassland ecosystems of the Uruguayan Savannas, the lack of optical data prior to 1972 and sites of abiotic variables measurement, the overall objective of this study was to evaluate the use of remote sensing data integrated with spatial modeling techniques as an evaluation method for RLE, more specifically in the Campo com Barba-de-Bode (Aristida spp. grasslands) in the Pampa Biome. Criteria A and B - Reduced Geographical Distribution and Restricted Geographical Distribution - were evaluated using the Land Change Modeler for TerrSet®, using data from the MapBiomas project in conjunction with 9 forcing variables related to agricultural conversion in the region and resulted in land use and land cover (LULC) maps for the year 1970. The Environmental Degradation assessment (Criterion C) was conducted in the Carbon module for the InVEST software considering the decline in carbon storage resulting from the changes in LULC identified in Criterion A. Criterion D (Disruption of biotic interactions and processes) was omitted from the diagnostic due to the scarcity of biotic records for the reference period. Finally, criterion E, probability of ecosystem collapse, was partially evaluated in Dinamica EGO software with the generation of future LULC scenarios for the years 2070 and 2120 considering the transition trends that occurred in the last 35 years (1985-2020). Our diagnosis classifies the CBB as Vulnerable/Endangered under criterion A1, Endangered under criterion A2, Vulnerable under criteria B1 and C1, Least Concern under criterion B2, and Data Deficient under criterion E. Future studies should be conducted to assess the remaining ecological systems of the Uruguayan Savannas, so that conservation initiatives can be supported by a risk comparison to which the areas are subjected and thus prioritize efforts in those most vulnerable to collapse

Modelling land cover change in tropical rainforests

Tropical deforestation is one of the most important drivers of biodiversity loss and carbon emissions. This thesis seeks to analyse the dynamics of tropical deforestation and develop a probabilistic model that predicts land cover change (LCC) in the tropics. The main findings from the analysis of the Brazilian Amazon deforestation dynamics are that large clearings comprised progressively smaller amounts of total annual deforestation while the number of smaller clearings remained unchanged over time. These changes were coincident with the implementation of conservation policies by the government. The review of LCC models presented here showed that this modelling community would benefit from improving: the openness to share model inputs, code and outputs; model validations; and standardised frameworks to be used for model comparisons. The modelling framework developed aimed to tackle the limitations found before and two scenarios of deforestation in the Brazilian Amazon were simulated. For both scenarios forest next to roads and areas already deforested were found to be more likely to be deforested. States in the south and east of the region showed high predicted probability of losing nearly all forest outside of protected areas by 2050. The release of carbon to the atmosphere is an important consequence of tropical deforestation. Even if deforestation had ended in 2010 there would still be large quantities of carbon to be released. The amount of carbon released immediately is higher than the one committed for future release in the first few years of analysis, but presently these accounted for at least two-thirds of total carbon emissions. Finally, the drivers of LCC were found to vary among transition types, but less so through time. The accuracy of the model predictions was heavily dependent on the year calibrated, suggesting that a widespread reliance on single calibration time period may be providing biased predictions of future LCC

Dinámica de la Ocupación del Suelo en la Cuenca del Río Combeima, Colombia (1991-2015)

Colombia es el segundo país más biodiverso en el mundo y está localizado en la zona intertropical. Allí sus cuencas hidrográficas presentan una variedad de coberturas y usos que están siendo sometidos a unas dinámicas que amenazan esta riqueza biológica, principalmente por acciones antrópicas. Este es el caso de la cuenca del rio Combeima, considerada un ecosistema estratégico por proveer servicios ambientales, tales como el suministro de agua para gran parte de la ciudad de Ibagué y para el riego de áreas claves en la producción de alimentos. Ante esto se han planteado dos objetivos que son complementarios: el primero, construir cartografía e indicadores espaciales y temporales de los cambios de la ocupación de la tierra en los periodos 1991-2005 y 2005-2015, que permitan dar respuesta a las siguientes inquietudes: ¿cuáles son las proporciones de los cambios espaciales y temporales ocurridos en la cobertura y usos de la tierra (CUT)?, ¿Qué categorías de CUT presentan cambios sistemáticos y en donde se ubican?. Y el segundo, seleccionar procedimientos para establecer los factores incidentes en la dinámica de la ocupación de la tierra. En su desarrollo se utilizan, de manera integrada, tecnologías de la información geográficas, técnicas de análisis estadístico explícitamente espaciales e interpretación de imágenes de satélite. Los resultados obtenidos revelan que la cuenca del río Combeima presenta cambios importantes en el sistema de uso de la tierra para el periodo 1991 y 2015. Se determinó la existencia de procesos de deforestación, crecimiento urbano, y recuperación/restauración, entre otros. Siendo el proceso de deforestación el más dinámico, presentando en el segundo periodo una tendencia a recuperarse el bosque. Mientras tanto el modelo de regresión utilizado identifica, del conjunto de variables analizadas, que la deforestación, entre 1991-2005 es condicionada principalmente por la densidad de población, la distancia al PNNN, la distancia a la zona urbana (1991) y la precipitación. La recuperación se da principalmente sobre áreas alejadas de la zona urbana y sobre terrenos de alta pendiente. Finalmente, el proceso de urbanización está especialmente incentivado por la densidad de población, la precipitación y el tamaño de los predios. De esta manera los resultados de la investigación serán insumo para integrarlo al plan de ordenación y manejo de la cuenca hidrográfica del río Combeima

Urbanization and sustainable land use planning challenges in the Mazandaran metropolitan area, the case of Sari city

After the industrial revolution in the late eighteenth century, massive changes in the social and economic sectors occurred, particularly in cities. These changes have accelerated urban growth by providing more financial resources and opportunities for urban areas than rural areas. As a result, cities have become larger, more populated than rural areas, and urban issues have become more complex and serious. The city of Sari, since the last decades, has been facing massive migration, uncontrolled land-use change, non-sustainable consumption of farmlands, and sprawling growth. Simultaneously growing the city, the city's issues became more challenging. Methodologically, this thesis benefits from methodological pluralism and seeks to analyze the city of Sari with attention to the defined objectives and questions in chapter one. Methodological pluralism involves employing multiple methods to obtain a value. Therefore, this thesis applied several mathematical models and softwares considering the types of data. Parts of the data were collected from the published data and reports by Iran’s national and regional organizations. Moreover, some of the data that were not available were collected through field works, surveys, and interviews with experts and managers. The results show that the current centralized political structure has made urban management ineffective and reduced public participation in the planning and executing processes. The results of the models for analyzing the urban system in the Mazandaran metropolitan area have shown agglomeration of the population in the urban areas, especially in the big cities, particularly in Sari. The urban system is centralized, and there is an imbalance between the size and rank of cities, especially in big cities. Also, the results of land-use change modeling and working with satellite images have shown that the city of Sari has been faced with massive sprawling growth, particularly during the last decades. And finally, the results of land-use changes analysis using GIS and aerial images have shown progressed unplanned land-use changes, particularly near the official border of Sari city

Road development in the Brazilian Amazon and its ecological implications

Roads are a distinctive feature in any landscape, with many countries giving 1-2% of their land surface over to roads and roadsides (Forman 1998). However, the ecological effects of roads spread beyond the physical footprint of the network and may impact 15-20% of the land or more (Forman & Alexander 1998). The Brazilian Amazon contains approximately one third of the world’s remaining rainforest, covering an area of 4.1 million km2. The region is highly biodiverse with 10-20 percent of the planet’s known species, it is also one of the three most bioculturally diverse areas in the world (Loh & Harmon 2005), and it provides many valuable ecosystem services. However, the Brazilian Amazon is rapidly undergoing extensive development with widespread land-use conversion. Road development is often perceived as the initial stage of development, opening access to remote areas for colonisation, agriculture development, resource extraction, and linked with these; deforestation (Chomitz & Gray 1996, Laurance et al. 2001, Perz et al. 2007, Laurance et al. 2009, Caldas et al. 2010). As such roads are a key spatial determinant of land use conversion in the Amazon region, dictating the spatial pattern of deforestation and biodiversity loss (Fearnside 2005, Kirby et al. 2006, Perz et al. 2008). Given that roads are a key spatial determinant of land use conversion and that they have extensive impacts on rates and patterns of habitat loss, it is important that we know how much, how fast and where road networks are developing in this globally important ecosystem. In this thesis, I aim to construct models of road network development to help better understand and predict the impacts of economic development in the Brazilian Amazon.Open Acces