Evaluation of the development of residential areas and the impact of geomorphologic status of the area on future development of Baneh city using LCM model

The growth rate of urban population varies in different regions, and the role and position of cities in this area is very influential. Baneh is now considered one of the most important cities in the west of the country, which is very much considered, and due to its commercial location, the urban population and the expansion of its urban settlements are in a steady rising trend. Due to the geomorphologic situation of the area, the development of the settlement areas of this city is confronted, and as a result of the development of this city, many settlements are located in areas that are geomorphologically part of the hazardous areas. Considering the above mentioned cases in this research, the Baneh urban development process from 1992 to 2017 has been evaluated and the aim of this research is to evaluate the Baneh urban development process during the period 1992 to 2017, and then on the basis of it, the development rate the city of Baneh will be projected until 2030. The research data included Landsat satellite imagery of 1992, 2001, 2011, and 2017, as well as information layers including DEM 30m. Data analysis was performed using two ARC GIS and IDRISI software. In this research, using satellite imagery of land use, a study area was developed from 1992 to 2017, and based on these maps, the LCM model predicts the development of residential areas by 2030. The results of the present study indicate that during the period 1992 to 2017 the size of the residential areas increased from 9 km2 to 20 km2, as well as the results of the prediction of the development of residential areas also indicate that the size of the restricted settlement areas until 2030 It will reach about 27.7 square kilometers

Assessing the Forest cover changes of Yankari game reserve using remote sensing and GIS techniques

This paper aim at assessing the Forest cover changes of Yankari game reserve in Nigeria using remote sensing and GIS techniques. The vegetation of the area consist of Savannah grass land with-developed patches of woodland. The study determine the land cover changes of the area using Land sat 4 imagery for the year 1990 and Land sat 7 Enhanced Thematic Mapper (ETM+7) for 2001 and 2011. The imageries were processed and classified using maximum likelihood classifier. The results revealed that there is a drastic decline in the vegetation cover over the period of the study. The percentage changes from forest cover to grassland was 22.93% from 1990-2001, 24.48% from 2001-2011 and 35.52% from 1990-2011.Also forest changes to open space was 18.29%, from 1990-2001, 7.78% from 2001-2011 and 13.64% from 1990-2011.While forest change to agriculture was 0.50% from 1990-2001, 2.15% from 2001-2011 and 2.98% from 1990-2011. An overall accuracy assessment of 71% for Landsat 1990, 65%, and 64% for ETM+ for 2001 and 2011 respectively. The game reserve is facing a threat in the disappearance of its forest cover and wildlife extinction in no distance time. The use remote sensing and GIS approach allowed us to quantify the extent of the forest cover changes in terms of percentages of the area affected, the rate of change as well as the nature of the change in terms of impact on natural vegetation

Case study greater Cairo Region Egypt

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively

Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales

International audienceLand cover has been changing rapidly throughout the world, and this issue is important to researchers, urban planners, and ecologists for sustainable land cover planning for the future. Many modeling tools have been developed to explore and evaluate possible land cover scenarios in future and time scales vary greatly from one study to another. The main objective of this study is to test land cover change prediction at different time scales in a Mediterranean catchment in SE France. Land cover maps were created from aerial photographs (1950, 1982, 2003, 2008, and 2011) of the Giscle catchment (235 Km 2) and surfaces were classified into four land cover categories: forest, vineyard, grassland, and built area. Explanatory variables were selected through Cramer's coefficient. Different time scales were tested in the study: short (2003-2008), intermediate (1982-2003), and long (1950-1982). To test the model's accuracy, Land Change Modeler (LCM) of IDRISI was used to predict land cover in 2011 and predicted images were compared to a real 2011 map. Kappa index and confusion matrix were used to evaluate the model's accuracy. Altitude, slope, and distance from roads had the greatest impact on land cover changes among all variables tested. Good to perfect level of spatial and perfect level of quantitative agreement were observed in long to short time scale simulations. Kappa indices (Kquantity = 0.99 and Klocation = 0.90) and confusion matrices were good for intermediate and best for short time scale. The results indicate that shorter time scales produce better predictions. Time scale effects have strong interactions with specific land cover dynamics, in which stable land covers are easier to predict than cases of rapid change and quantity is easier to predict than location for longer time periods

Análisis de los impactos ambientales causados por el cambio de uso del suelo en la cuenca hidrográfica del río Lita

Analizar los impactos ambientales causados por el cambio de uso del suelo en la cuenca hidrográfica del río Lita durante los años 1996 y 2017.El cambio en el uso del suelo (CUS) es la manifestación más directa de los efectos de la actividad antrópica en los ecosistemas naturales. En el presente trabajo de investigación se analizó los impactos ambientales causados por el CUS en la cuenca¿hidrográfica del río Lita. Para ello se realizó un análisis multitemporal a partir de imágenes satelitales de los años 1996 y 2017, mismas que pasaron por un proceso de corrección tanto radiométrica como geométrica para posteriormente elaborar una clasificación supervisada y obtener los mapas de usos de suelo para los dos años de estudio. Como resultado se obtuvo 7 diferentes tipos de cobertura vegetal, de la cual se evidenció una pérdida del 11,64% de bosques al mismo tiempo que hubo un aumento en las áreas de cultivos y pastos con un 2,99% y 4,28% respectivamente. Además, con la aplicación del método cualitativo de evaluación de impactos ambientales propuesto por Conesa Fernández se han identificado y evaluado los diferentes procesos que se llevan a cabo en las actividades agrícola, ganadera y minera, y cómo estos afectan a los diferentes componentes ambientales. Como resultados se obtuvo que, para la matriz de la actividad agrícola, 3 de los impactos son severos, 23 moderados, 26 irrelevantes y 12 positivos, mientras que para la actividad ganadera se obtuvieron 3 impactos severos, 7 moderados, 13 irrelevantes y 6 positivos y en la actividad minera 1 impacto crítico, 14 severos, 24 moderados, 11 irrelevantes y 5 positivos. Finalmente, se proyectó un escenario futuro de CUS al 2038 en el cual se mostró una pérdida de 7,24% de bosque y un constante incremento en pastos y cultivos a partir del 2017, tendencia de cambio que se mantiene desde el año 1996, problemática que tiene relación con el crecimiento demográfico y la demanda de recursos naturales que esto conlleva.Ingenierí

Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales

International audienceLand cover has been changing rapidly throughout the world, and this issue is important to researchers, urban planners, and ecologists for sustainable land cover planning for the future. Many modeling tools have been developed to explore and evaluate possible land cover scenarios in future and time scales vary greatly from one study to another. The main objective of this study is to test land cover change prediction at different time scales in a Mediterranean catchment in SE France. Land cover maps were created from aerial photographs (1950, 1982, 2003, 2008, and 2011) of the Giscle catchment (235 Km 2) and surfaces were classified into four land cover categories: forest, vineyard, grassland, and built area. Explanatory variables were selected through Cramer's coefficient. Different time scales were tested in the study: short (2003-2008), intermediate (1982-2003), and long (1950-1982). To test the model's accuracy, Land Change Modeler (LCM) of IDRISI was used to predict land cover in 2011 and predicted images were compared to a real 2011 map. Kappa index and confusion matrix were used to evaluate the model's accuracy. Altitude, slope, and distance from roads had the greatest impact on land cover changes among all variables tested. Good to perfect level of spatial and perfect level of quantitative agreement were observed in long to short time scale simulations. Kappa indices (Kquantity = 0.99 and Klocation = 0.90) and confusion matrices were good for intermediate and best for short time scale. The results indicate that shorter time scales produce better predictions. Time scale effects have strong interactions with specific land cover dynamics, in which stable land covers are easier to predict than cases of rapid change and quantity is easier to predict than location for longer time periods

Análisis del cambio de uso de suelo y proyección futura para el cantón Otavalo

Analizar el cambio de uso de suelo del cantón Otavalo para la identificación de áreas afectadas con proyección a futuro.El cambio de uso del suelo (CUS) se encuentra directamente relacionado a actividades antrópicas generando problemas que afectan a los ecosistemas, ocasionando pérdida y fragmentación de los hábitats. En el presente estudio se analizó el cambio de uso del suelo en el cantón Otavalo. Para ello se realizó un análisis espacial mediante ortofotografías de los años 1993, 2002 y 2012, que fueron empleadas en un proceso de digitalización en pantalla, y, posteriormente, la clasificación supervisada mediante el método de máxima verosimilitud, obteniendo cartografía de uso del suelo, lo cual fue validado mediante el índice Kappa. Como resultado de la clasificación se obtuvieron 12 categorías de cobertura y usos del suelo, donde se evidenció una pérdida del 0,67% de bosque nativo y 0,20% de páramo en los 19 años analizados, mientras que las áreas de cultivo aumentaron en 2,39%. El valor del índice Kappa obtenido fue de 0,93 que indica una clasificación casi perfecta. En el análisis de conflictos históricos de cambio de uso de suelo se evidenció que el uso adecuado disminuyó en 0,49%, mientras que la sobreutilización aumentó en 0,64%; y la subutilización fue de 1,19%. En la proyección a futuro del cambio de uso del suelo al año 2031 se obtuvo una pérdida de 28,18% de bosque nativo y una disminución de páramo en 12,08%, mientras que las áreas intervenidas se encuentran en constante aumento, dicha tendencia de cambio se mantiene desde 1993. Finalmente, se establecieron acciones para mitigar los problemas detectados de cambio de uso del suelo orientados a: 1) Evitar la pérdida de bosque nativo, 2) Evitar la pérdida del ecosistema páramo, y 3) Controlar la expansión de la frontera agrícola, permitiendo la conservación de los ecosistemas más vulnerables con un criterio sostenible.Ingenierí

Land Use Change from Non-urban to Urban Areas

This reprint is related to land-use change and non-urban and urban relationships at all spatiotemporal scales and also focuses on land-use planning and regulatory strategies for a sustainable future. Spatiotemporal dynamics, socioeconomic implication, water supply problems and deforestation land degradation (e.g., increase of imperviousness surfaces) produced by urban expansion and their resource requirements are of particular interest. The Guest Editors expect that this reprint will contribute to sustainable development in non-urban and urban areas

Monitoring and modelling disturbances to the Niger Delta mangrove forests

The Niger River Delta provides numerous ecosystem services (ES) to local populations and holds a wealth of biodiversity. Nevertheless, they are under threat of degradation and loss mainly due to the population increase and oil and gas extraction activities. Monitoring mangrove vegetation change and understanding the dynamics related with these changes is crucial for the short and longer-term sustainability of the Niger Delta Region (NDR) and its mangrove forests. Over the last two decades, open access remote sensing data, together with technological and algorithmic advancements, have provided the ability to monitor land cover over large areas through space and time. However, the analysis of land cover dynamics over the NDR using freely available optical remote sensing data, such as Landsat, remains challenging due to the gaps in the archive associated with the West African region and the issue of cloud contamination over the wet tropics. This thesis applies state-art-of-the-art remote sensing techniques and integrated modelling approaches to provide reliable information relating to monitoring and modelling of land cover change in the NDR, focusing on its mangrove forests. Spectral-temporal metrics from all available Landsat images were used to accurately map land cover in three time points, using a Random Forests machine learning classification model. The performance of the classification was tested when L-band radar data are added to the Landsat-based metrics. Results showed that Landsat based metrics are sufficient in mapping land cover over the study region with high overall classification accuracies over the three time points (1988, 2000, and 2013) and degraded mangroves were accurately mapped for the first time. Two additional assessments: a change intensity analysis for the entire NDR and, fragmentation analysis focusing on mangrove land cover classes were carried out for the first time ever. The drivers of mangrove degradation were assessed using a Multi-layer Perceptron, Artificial Neutral Networks (MLP-ANN) algorithm. The results reveal that built-up infrastructure variables were the most important drivers of mangrove degradation between 1988 and 2000, whilst oil and gas infrastructure variables were the most important drivers between 2000 and 2013. Results also show that population density was the least important driver of mangrove degradation over the two study periods. Future land cover changes and mangrove degradation were predicted under two business-as-usual scenarios in the short (2026) and longer-term (2038) using a Multi-Layer Perceptron neutral network and Markov chain (MLP-ANN+MC) model. The model’s accuracy was assessed using the highly-accurate land cover classification of 2013. Results show that that mangrove forest and woodlands (lowland and freshwater forests) are demonstrating a net loss, whilst the built-up areas and agriculture are indicating a net increase in both the short and longer-term scenarios. However, degraded mangroves are demonstrating a net increase in the short-term scenario. Interestingly, in the longer-term scenario, more than double the net increase of mangroves degraded in the short-term scenario, are predicted to recover to their healthier state. The thesis results could provide useful information for planning conservation measures for sustainable mangrove forest management of the entire NDR