Mapping and Change Assessment of Captive Limestone Mining Areas Using Landsat-5/8 Images

Limestone is a non-metallic mineral extensively used in cement manufacturing and construction sector. Extensive mineral mining processes impact the environment. The study aims to map and evaluate the limestone mining area change at the Yerraguntla industrial zone in the YSR district of Andhra Pradesh, India. The normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI) are computed from the Landsat-5/8 images using Quantum GIS (QGIS) software. Experimental results show that the limestone mining area increases from 307 ha to 469.92 ha during 2005-2019. NDVI method is more effective than MSAVI in change assessment of limestone mining areas with overall accuracy of 87.75 % and 79.49 % and kappa coefficient of 0.89 and 0.62 respectively in 2019. The finding is compared with industry field survey reports (487.10 ha). This study contributes to the limestone mining industry management in developing a land-environmental management plan for the long-term sustainability of limestone mining

Implementation DBSCAN algorithm to clustering satellite surface temperature data in Indonesia

Forest and land fires are national and international problems. The frequency of fires in one of Indonesia's provinces, Riau, is a significant problem. Knowing where to repair the burn is essential to prevent more massive fires. Fires occur because of a fire triangle, namely fuel, oxygen, and heat. The third factor can be seen through remote sensing. Using the Landsat-8 satellite, named the Enhanced Vegetation Index (EVI) variable, Normalized Burn Area (NBR), Normal Difference Humidity Index (NDMI), Normal Difference Difference Vegetation Index (NDVI), Soil Adapted Vegetation Index (SAVI), and Soil Surface Temperature (LST). DBSCAN, as a grouping algorithm that can group the data into several groups based on data density. This is used because of the density of existing fire data, according to the character of this algorithm. The selected cluster is the best cluster that uses Silhouette Coefficients, eps, and minutes value extracted from each variable, so there is no noise in the resulting cluster. The result is more than 0, and the highest is the best cluster result. There are 5 clusters formed by clustering, each of which has its members. This cluster is formed enough to represent the real conditions, cluster which has a high LST value or has an NBR value. A high  LST value indicates an increase in the area's temperature; a high NBR value indicates a fire has occurred in the area. The combination of LST and NBR values indicates the area has experienced forest and land fires. This study shows that DBSCAN clustered fire and surface temperature data following data from the Central Statistics Agency of Riau Province. Proven DBSCAN can cluster satellite imagery data in Riau province into several clusters that have a high incidence of land fires

SPATIO-TEMPORAL DEVELOPMENT OF COASTAL TOURIST CITY OVER THE LAST 50 YEARS FROM LANDSAT SATELLITE IMAGE PERSPECTIVE IN TAKUA PA DISTRICT, PHANG-NGA PROVINCE, THAILAND

The objective of this research was to study the land-use patterns through the Landsat satellite image perspective in order to see the spatio-temporal development of coastal tourist city in Takua Pa District, Phang-Nga Province, Thailand. The study found that there is a noticeable land-use change in the cassiterite (tin) mining area that has declined over the past 50 years, from 1973 appearing 55.82 km2 (9.68%) until the current year 2022 without remaining, as it has been transformed into an agricultural area where rubber, palm, and coconut are planted. In addition, the mining area has become an urban area and buildings such as hotels and resorts, and a water source for shrimp farms. It can be seen that Landsat satellite imagery is very useful for land-use planning, especially in the coastal tourist city area. The results of this research can be classified as a spatial database for tourism planning in Takua Pa community by zoning into 3 areas for major tourism, Zone-1 Eco Tourism, Zone-2 Cultural Tourism, and Zone-3 High-end Tourism. This is important research data to support decision-making in regulating, monitoring, and controlling areas for further tourism business expansion in order to avoid negative impacts on the environment

Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático.

O objetivo deste trabalho foi avaliar o desempenho dos classificadores digitais SVM e K?NN para a classificação orientada a objeto em imagens Landsat?8, aplicados ao mapeamento de uso e cobertura do solo da Alta Bacia do Rio Piracicaba?Jaguari, MG. A etapa de pré?processamento contou com a conversão radiométrica e a minimização dos efeitos atmosféricos. Em seguida, foi feita a fusão das bandas multiespectrais (30 m) com a banda pancromática (15 m). Com base em composições RGB e inspeções de campo, definiramse 15 classes de uso e cobertura do solo. Para a segmentação de bordas, aplicaram-se os limiares 10 e 60 para as configurações de segmentação e união no aplicativo ENVI. A classificação foi feita usando SVM e K?NN. Ambos os classificadores apresentaram elevados valores de índice Kappa (k): 0,92 para SVM e 0,86 para K?NN, significativamente diferentes entre si a 95% de probabilidade. Uma significativa melhoria foi observada para SVM, na classificação correta de diferentes tipologias florestais. A classificação orientada a objetos é amplamente aplicada em imagens de alta resolução espacial; no entanto, os resultados obtidos no presente trabalho mostram a robustez do método também para imagens de média resolução espacial

Análise comparativa de classificadores digitais em imagens do Landsat‑8 aplicados ao mapeamento temático

The objective of this work was to evaluate the performance of SVM and K‑NN digital classifiers for the object‑based classification on Landsat‑8 images, applied to mapping of land use and land cover of Alta Bacia do Rio Piracicaba‑Jaguari, in the state of Minas Gerais, Brazil. The pre‑processing step consisted of using radiometric conversion and atmospheric correction. Then the multispectral bands (30 m) were merged with the panchromatic band (15 m). Based on RGP compositions and field inspection, 15 land‑use and land‑cover classes were defined. For edge segmentation, the bounds were set to 10 and 60 for segmentation configuring and merging in the ENVI software. Classification was done using SVM and K‑NN. Both classifiers showed high values for the Kappa index (k): 0.92 for SVM and 0.86 for K‑NN, significantly different from each other at 95% probability. A major improvement was observed for SVM by the correct classification of different forest types. The object‑based classification is largely applied on high‑resolution spatial images; however, the results of the present work show the robustness of the method also for medium‑resolution spatial images.O objetivo deste trabalho foi avaliar o desempenho dos classificadores digitais SVM e K‑NN para a classificação orientada a objeto em imagens Landsat‑8, aplicados ao mapeamento de uso e cobertura do solo da Alta Bacia do Rio Piracicaba‑Jaguari, MG. A etapa de pré‑processamento contou com a conversão radiométrica e a minimização dos efeitos atmosféricos. Em seguida, foi feita a fusão das bandas multiespectrais (30 m) com a banda pancromática (15 m). Com base em composições RGB e inspeções de campo, definiram-se 15 classes de uso e cobertura do solo. Para a segmentação de bordas, aplicaram-se os limiares 10 e 60 para as configurações de segmentação e união no aplicativo ENVI. A classificação foi feita usando SVM e K‑NN. Ambos os classificadores apresentaram elevados valores de índice Kappa (k): 0,92 para SVM e 0,86 para K‑NN, significativamente diferentes entre si a 95% de probabilidade. Uma significativa melhoria foi observada para SVM, na classificação correta de diferentes tipologias florestais. A classificação orientada a objetos é amplamente aplicada em imagens de alta resolução espacial; no entanto, os resultados obtidos no presente trabalho mostram a robustez do método também para imagens de média resolução espacial

Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

The assessment of storm damages is critically important if resource managers are to understand the impacts of weather pattern changes and sea level rise on their lands and develop management strategies to mitigate its effects. This study was performed to detect land cover change on Assateague Island as a result of Hurricane Sandy. Several single-date classifications were performed on the pre and post hurricane imagery utilized using both a pixel-based and object-based approach with the Random Forest classifier. Univariate image differencing and a post classification comparison were used to conduct the change detection. This study found that the addition of the coastal blue band to the Landsat 8 sensor did not improve classification accuracy and there was also no statistically significant improvement in classification accuracy using Landsat 8 compared to Landsat 5. Furthermore, there was no significant difference found between object-based and pixel-based classification. Change totals were estimated on Assateague Island following Hurricane Sandy and were found to be minimal, occurring predominately in the most active sections of the island in terms of land cover change, however, the post classification detected significantly more change, mainly due to classification errors in the single-date maps used

Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina

This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in two stages. The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision Tree (DT). The results obtained indicate that object-based analyses clearly outperform pixel-based classifications, with an increase in accuracy of up to 35%. The second stage focused on advanced object-based derived variables with topographic ancillary data classifications. The combinations of variables were tested in order to obtain the most accurate map of landforms based on the most successful classifiers identified in the previous stage (ML, SVM and DT). The results indicate that DT is the most accurate classifier, exhibiting the highest overall accuracies with values greater than 72% in both the winter and summer images. Future work could combine both, the most appropriate methodologies and combinations of variables obtained in this study, with physico-chemical variables sampled to improve the classification of landforms and even of types of soil.EEA Santiago del EsteroFil: Castillejo González, Isabel Luisa. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; EspañaFil: Angueira, Maria Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; ArgentinaFil: García Ferrer, Alfonso. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; EspañaFil: Sánchez de la Orden, Manuel. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; Españ