An evaluation of urbanisation processes in suburban zones using land-cover data and fuzzy set theory

The aim of this article was to evaluate urbanization processes in space with the use of the CORINE Land Cover (CLC) databases. The study was conducted in the rural municipality of Dywity, located in the direct vicinity of the city of Olsztyn. Basic concepts and methods for evaluating urbanization processes were determined based on a review of the literature. The article addresses issues related to spatial management and GIS as a data source and a tool for analyzing land management activities. The search for new methods for evaluating spatial management and spatial processes plays a particularly important role in rapidly urbanizing areas. The study explored the applicability of GIS as a data source and a tool for evaluating urbanization processes in studies that rely on modern methods such as the fuzzy set theory. The intensity and dynamics of urbanization processes were evaluated based on changes in land cover with the use of CLC databases.The aim of this article was to evaluate urbanisation processes in space using the CORINE Land Cover (CLC) databases. The study was conducted in the rural municipality of Dywity in the direct vicinity of the city of Olsztyn. Basic concepts and methods for evaluating urbanisation processes were determined based on a review of the literature. The article addresses issues related to spatial management and GIS as a data source and a tool for analysing land management activities. The search for new methods for evaluating spatial management and spatial processes plays a particularly important role in rapidly urbanising areas. The study explored the applicability of GIS as a data source and a tool for evaluating urbanisation processes in studies that rely on modern methods such as fuzzy set theory. The intensity and dynamics of urbanisation processes were evaluated based on changes in land cover with the use of CLC databases

Flood hazard mapping of a rapidly urbanizing city in the foothills (Birendranagar, Surkhet) of Nepal

Flooding in the rapidly urbanizing city of Birendranagar, Nepal has been intensifying, culminating in massive loss of life and property during July and August 2014. No previous studies have monitored underlying land-cover dynamics and flood hazards for the area. This study described spatiotemporal urbanization dynamics and associated land-use/land-cover (LULC) changes of the city using Landsat imagery classifications for five periods between 1989 and 2016 (1989-1996, 1996-2001, 2001-2011, 2011-2016). Areas with high flood-hazard risk were also identified on the basis of field surveys, literature, and the Landsat analysis. The major LULC changes observed were the rapid expansion of urban cover and the gradual decline of cultivated lands. The urban area expanded nearly by 700%, from 85 ha in 1989 to 656 ha in 2016, with an average annual growth rate of 23.99%. Cultivated land declined simultaneously by 12%, from 7005 ha to 6205 ha. The loss of forest cover also contributed significantly to increased flood hazard. Steep topography, excessive land utilization, fragile physiographic structure, and intense monsoonal precipitation aggravate hazards locally. As in Nepal generally, the sustainable development of the Birendranagar area has been jeopardized by a disregard for integrated flood-hazard mapping, accounting for historical land-cover changes. This study provides essential input information for improved urban-area planning in this regard

Impact of Land Cover Change on Ecosystem Services in a Tropical Forested Landscape

Ecosystems provide a wide range of goods, services or ecosystem services (ES) to society. Estimating the impact of land use and land cover (LULC) changes on ES values (ESV) is an important tool to support decision making. This study used remote sensing and GIS tools to analyze LULC change and transitions from 2001 to 2016 and assess its impact on ESV in a tropical forested landscape in the southern plains of Nepal. The total ESV of the landscape for the year 2016 is estimated at USD 1264 million year−1. As forests are the dominant land cover class and have high ES value per hectare, they have the highest contribution in total ESV. However, as a result of LULC change (loss of forests, water bodies, and agricultural land), the total ESV of the landscape has declined by USD 11 million year−1. Major reductions come from the loss in values of climate regulation, water supply, provision of raw materials and food production. To halt the ongoing loss of ES and maintain the supply and balance of different ES in the landscape, it is important to properly monitor, manage and utilize ecosystems. We believe this study will inform policymakers, environmental managers, and the general public on the ongoing changes and contribute to developing effective land use policy in the region

Geo-Spatial Assessment of Masterplan Alteration of Ibeju-Lekki Area of Lagos State

Context and Background The master plan of Ibeju-Lekki, developed in 2009, aims to conserve the environment, control urban sprawl, reduce transportation expenses, avoid land use disputes, and minimize pollution exposure. However, inadequate planning has led to the growth of urban sprawl in the area, causing flood and erosion hazards in some communities. Goal and Objectives: This study aimed to assess the spatial distribution of land use types in the master plan, determine the land cover change between the year of master plan production and the present year, and assess the development of Ibeju-Lekki in relation to the master plan. Methodology: The analogue format of the master plan was obtained, georeferenced, and converted into a vector format (ESRI Shapefile) for spatial analysis. Multi-spectral images (Landsat images) were obtained for the years 2009 and 2023 to detect changes in land cover and development in the study area. Results: The results showed that residential land use had the largest spatial extent on the master plan, but it will soon be congested due to increased industries. Built-up areas, bare land, and wetlands increased between 2009 and 2023, while vegetation and water bodies declined. The highest development occurred on residential land use between 2009 and 2023, with unplanned development in fragile and conserved areas like lagoons, green areas, flood basins, open space, and eco-tourism. The study recommends vulnerability assessment of altered areas in the master plan and re-evaluation to accommodate the changes occurring in the Ibeju-Lekki area of Lagos State

Assessing Vegetation Cover Change Using Remote Sensing: Case Study at Binh Duong Province, Vietnam

This study aims to present the application of remote sensing in monitoring vegetation change in Binh Duong Province, Vietnam. The study used Landsat 5 images in the year 2010 and Landsat 8 images in the years 2015 and 2020 to investigate the area of vegetation. The maximum likelihood classification method (MLC) was used to classify land cover and an accuracy matrix was computed to validate the classification results. The references data were collected to support classification and accuracy assessment processes including land use maps in 2010, 2015, and 2020. In addition, collected field points and UAV (unmanned aerial vehicle) in 2020 were used. The overall accuracies are 81.27%, 84.41%, and 83.86%, and Kappa indices were 0.76, 0.80, and 0.80, corresponding to 2010, 2015, and 2020. The results showed that as compared to 2010 and 2015, the area of vegetation in 2020 decreased 10% and 8%, respectively. The average vegetation cover per capita was 740 m2 person-1 in 2020, compared to 1000 m2 person-1 in 2015 and 1200 m2 person-1 in 2010. This reduction was obvious in urban areas in the province, due to the need for construction and development. The study provides meaningful information on vegetation change and green area per capita in Binh Duong Province from 2010 to 2020

Urban expansion occurred at the expense of agricultural lands in the Tarai region of Nepal from 1989 to 2016

Recent rapid urbanization in developing countries presents challenges for sustainable environmental planning and peri-urban cropland management. An improved understanding of the timing and pattern of urbanization is needed to determine how to better plan urbanization for the near future. Here, we describe the spatio-temporal patterns of urbanization and related land-use/land-cover (LULC) changes in the Tarai region of Nepal, as well as discuss the factors underlying its rapid urban expansion. Analyses are based on regional time-series Landsat 5, 7 and 8 image classifications for six years between 1989 and 2016, representing the first long-term observations of their kind for Nepal. During this 27-year period, gains in urban cover and losses of cultivated lands occurred widely. Urban cover occupied 221.1 km2 in 1989 and increased 320% by 2016 to a total 930.22 km2. Cultivated land was the primary source of new urban cover. Of the new urban cover added since 1989, 93% was formerly cultivated. Urban expansion occurred at moderately exponential rates over consecutive observation periods, with nearly half of all urban expansion occurring during 2006–2011 (305 km2). The annual rate of urban growth during 1989–1996 averaged 3.3% but reached as high as 8.09% and 12.61% during 1996–2001 and 2011–2016, respectively. At the district level, the rate of urban growth and, by extension, agricultural loss, were weakly related to total population growth. Variability in this relationship suggests that concerted urban-growth management may reduce losses of agricultural lands relative to historic trends despite further population growth and urbanization. Urbanization and LULC change in the Tarai region are attributable to significant inter-regional migration in a context of poor urban planning and lax policies controlling the conversion and fragmentation of peri-urban cultivated lands. Urban expansion and farmland loss are expected to continue in the future

Evaluación comparativa de los algoritmos de aprendizaje automático Support Vector Machine y Random Forest

En el presente estudio se examinó el rendimiento de los algoritmos Support Vector Machine (SVM) y Random Forest (RF) utilizando un modelo de segmentación de imágenes basado en objetos (OBIA) en la zona metropolitana de Barranquilla, Colombia. El propósito fue investigar de qué manera los cambios en el tamaño de los conjuntos de entrenamiento y el desequilibrio en las clases de cobertura terrestre influyen en la precisión de los modelos clasificadores. Los valores del coeficiente Kappa y la precisión general revelaron que svm superó consistentemente a RF. Además, la imposibilidad de calibrar ciertos parámetros de SVM en ArcGIS Pro planteó desafíos. La elección del número de árboles en RF mostró ser fundamental, con un número limitado de árboles (50) que afectó la adaptabilidad del modelo, especialmente en conjuntos de datos desequilibrados. Este estudio resalta la complejidad de elegir y configurar modelos de aprendizaje automático, que acentúan la importancia de considerar cuidadosamente las proporciones de clases y la homogeneidad en las distribuciones de datos para lograr predicciones precisas en la clasificación de uso del suelo y cobertura terrestre. Según los hallazgos, alcanzar precisiones de usuario superiores al 90 % en las clases de pastos limpios, bosques, red vial y agua continental, mediante el modelo svm en ArcGIS Pro, requiere asignar muestras de entrenamiento que cubran respectivamente el 2 %, 1 %, 3 % y 8 % del área clasificada

Development at the cost of unsustainable degradation of wetlands: Unraveling the dynamics (historic and future) of wetlands in the megacity Dhaka

Despite the recognized role of wetlands in providing ecological benefits for human wellbeing, ∼70% of global wetland ecosystems have been destroyed since the 1990s. Further intensive studies revealed that 3.4 million km2 of natural wetland has declined since 1700. In particular, wetland habitats in the world's megacities have been replaced unsustainably by faster economic, urban, and population growth, and have received less attention in research and policy. However, wetlands degradation in the megacities of developing countries is not quantified and the trends of Land Surface Temperature (LST) are not well understood. Therefore, we are making our first attempt to unravel the historical and future spatiotemporal dynamics of wetlands and the trends of LST in the megacity of Dhaka. The results show that Dhaka lost ∼69% of wetlands and LST has increased between 3.44°C and 9.35°C from 1990 to 2020. An environmental Kuznets curve analysis implies that the point has not yet been reached for wetlands when economic development feeds back to the sustainability of the environment. This assumption coincides with our model-based prediction, as respectively ∼74% and ∼90% of wetlands area of Dhaka city will be decreased by 2050 in Business as Usual (BAU) and development scenarios, whereas, ∼66% of wetlands area will be decreased under conservation scenario over the time period of next 30 (2020 to 2050) years. Our findings suggest that it will be incredibly challenging to restore wetlands to their 1990s condition. Efforts to preserve them should be made, as they potentially provide a nature-based alternative for coping with wetland sustainability and climate change

Cálculo da curva número para bacia hidrográfica urbana utilizando diferentes abordagens de classificação para imagem orbital RapidEye: estudo de caso para o arroio Pepino (Pelotas, RS)

O valor da curva-número (CN) é um parâmetro empírico usado na determinação do escoamento superficial direto a partir dos excessos de precipitações, sendo dependente das mudanças de uso e cobertura da superfície. Imagens de alta resolução espacial são importantes para identificar tais mudanças em bacias hidrográficas urbanas. O objetivo deste trabalho foi comparar os efeitos de diferentes mapas de uso e cobertura, produzidos a partir de classificações não-supervisionada (K-médias) e supervisionadas (MaxVer, SAM e SVM) em uma imagem orbital de alta resolução espacial, no cálculo do valor CN da bacia hidrográfica urbana do Arroio Pepino (Pelotas, RS). A hipótese é de que diferentes algoritmos de classificação produzem diferentes mapas de superfície que por sua vez afetam o valor CN final. As classificações foram realizadas em uma imagem RapidEye e 10 classes foram identificadas: água, asfalto, estrada de terra, vegetação (3 tipos) e coberturas (4 tipos). O valor CN de cada classe foi obtido pela comparação com valores tabulados, e o valor CN total foi calculado pela média ponderada considerando a área proporcional de cada classe. O SVM foi o algoritmo de melhor desempenho (acurácia global de 70,36% e índice kappa de 0,66). Os valores finais de CN apresentaram distintas intensidades: CNtotal = 88,96 para SAM, CNtotal = 89,66 para K-médias, CNtotal = 89,94 para SVM e CNtotal = 90,71 para MaxVer. A proximidade entre estes valores foi influenciada pela baixa capacidade de drenagem da bacia estudada mesmo em áreas vegetadas. Diferenças nas proporções das classes afetam o valor do CN final da bacia, e sua qualidade é altamente dependente da acurácia da imagem classificada. O valor da curva-número (CN) é um parâmetro empírico usado na determinação do escoamento superficial direto a partir dos excessos de precipitações, sendo dependente das mudanças de uso e cobertura da superfície. Imagens de alta resolução espacial são importantes para identificar tais mudanças em bacias hidrográficas urbanas. O objetivo deste trabalho foi comparar os efeitos de diferentes mapas de uso e cobertura, produzidos a partir de classificações não-supervisionada (K-médias) e supervisionadas (MaxVer, SAM e SVM) em uma imagem orbital de alta resolução espacial, no cálculo do valor CN da bacia hidrográfica urbana do Arroio Pepino (Pelotas, RS). A hipótese é de que diferentes algoritmos de classificação produzem diferentes mapas de superfície que por sua vez afetam o valor CN final. As classificações foram realizadas em uma imagem RapidEye e 10 classes foram identificadas: água, asfalto, estrada de terra, vegetação (3 tipos) e coberturas (4 tipos). O valor CN de cada classe foi obtido pela comparação com valores tabulados, e o valor CN total foi calculado pela média ponderada considerando a área proporcional de cada classe. O SVM foi o algoritmo de melhor desempenho (acurácia global de 70,36% e índice kappa de 0,66). Os valores finais de CN apresentaram distintas intensidades: CNtotal = 88,96 para SAM, CNtotal = 89,66 para K-médias, CNtotal = 89,94 para SVM e CNtotal = 90,71 para MaxVer. A proximidade entre estes valores foi influenciada pela baixa capacidade de drenagem da bacia estudada mesmo em áreas vegetadas. Diferenças nas proporções das classes afetam o valor do CN final da bacia, e sua qualidade é altamente dependente da acurácia da imagem classificada

Crecimiento urbano y su influencia en los cambios de cobertura y uso del suelo utilizando autómatas celulares en los distritos de Bagua Grande y Chachapoyas, Perú

En las últimas décadas, el crecimiento urbano se ha incrementado aceleradamente en todas las ciudades del mundo. En esta investigación analizamos el crecimiento urbano y su influencia en los cambios de cobertura y uso del suelo (CCUS) aplicando Autómatas Celulares (AC) en los distritos de Bagua Grande y Chachapoyas (Perú). Utilizamos la plataforma de computación en la nube de Google Earth Engine (GEE) para analizar series temporales anuales de imágenes Landsat 5 (L5) y Landsat 8 (L8) desde 1990 a 2021. Se aplicó una clasificación supervisada Random Forest (RF) para generar mapas de CCUS para 1990, 2000, 2011 y 2021. Posteriormente, se utilizó el complemento MOLUSCE de QGIS integrando cuatro variables predictoras del crecimiento urbano al 2031. Los mapas de cobertura y uso del suelo reportaron una precisión general (OA) superiores al 92%. La superficie de bosque se redujo de 20,807.97 ha en 1990 a 14,629.44 ha en 2021 para el distrito de Bagua Grande. A su vez el distrito de Chachapoyas presentó patrones similares con una superficie de 7,796.08 ha en 1990 a 3,598.19 ha en 2021. Por su parte, las áreas urbanas se incrementaron de 287.49 a 1,128.77 ha para Bagua Grande y de 185.65 a 924.50 ha para Chachapoyas entre 1990 y 2021. Mediante la aplicación de AC se predijo el crecimiento urbano para 2031 con precisiones superiores al 70%, se estimó que el área urbana del distrito de Bagua Grande se incrementará a 1,459.25 ha y 1,138.05 ha el distrito de Chachapoyas. El modelamiento de escenarios futuros del crecimiento urbano a partir de los mapas de CCUS y MOLUSCE demostró un incremento de la superficie urbana y la reducción de superficies de cobertura vegetal al 2031