Applied Geomatics最新文献

The use of remote sensing to map land cover and changes in land use has proven to be a practical, reliable, and accessible approach. These images provide precise details about the landscape, utilizing image processing techniques, modeling, and classification algorithms. This study aimed to identify different areas, such as coffee plantations, water bodies, urban areas, forests, exposed soil, and pastures in the Funil reservoir region of Minas Gerais, Brazil. Image data from Landsat-8, Sentinel-1, and Sentinel-2 satellites for June 2021 were used. Different supervised classification algorithms such as rf, rpart1SE, and svmLinear2 were applied based on a large volume of remote sensing data. The analyses and maps were performed using the software RStudio, considering a significance level of 5%. The highest accuracy and kappa index values were found for the rf algorithm, followed by svmLinear2 and rpart1SE. The results showed that the rf algorithm achieved the highest accuracy and kappa index values, followed by svmLinear2 and rpart1SE. However, during the validation phase, the svmLinear2 algorithm outperformed based on the statistical results of the confusion matrix. Therefore, it was considered the most suitable for generating the thematic mapping of the landscape. This is because svmLinear2 identified a more significant number of coffee areas and better-distinguished vegetation areas.

Image geotagging is a process where geographic coordinates are attached to an image. Mobile-based geotagging application has many advantages, viz. real-time monitoring, ensuring data authenticity etc. Since an ordinary mobile camera cannot interpret the geotagged images, they are manually analysed later for a specific purpose. Therefore, the human interpreters are to put their time and effort to analyse the images. This becomes difficult when the number of images is more. The heterogeneity of captured images, in terms of intensity, viewing angle etc., limits the application of traditional image processing techniques for automatic image interpretation. Hence, artificial intelligence (AI)–based image processing technique needs to be employed that enables machines to learn from instances and provide assistance in field photo interpretation. In the present work, a smartphone-based application, embedded with enhanced capabilities of AI and geospatial technology, has been developed using open-source technology. The application employs AI to detect certain categories’ semantic objects and automatically generates their details. The mean of detection precision, recall and F1 score are estimated as 0.96, 0.91 and 0.93, respectively. The present work successfully demonstrates the use of open-source technology for AI-enabled geotagging and dissemination of ground information through WebGIS application.

Due to the ongoing population increase over the past years, fast and unchecked urbanization has been occurring in the urban centers of developing nations like India. As a result, land transformation is taking place at a fast pace leading to the creation of urban heat island (UHI). Urban heat island (UHI) constitutes a significant human alteration to the Earth system. Hence, this study presents a rigorous and comprehensive analysis of the impact of land use and cover on land surface temperature (LST) in Aligarh City, Uttar Pradesh, India, using multi-dimensional satellite data. The research collected Landsat data for four different phases (1991, 2001, 2011, and 2021) and analyzed it in conjunction with land use and cover (LULC) data to identify trends and variations. The result shows a consistent increase in LST since 1991, with built-up and bare land areas exhibiting the highest temperatures across all phases. Moreover, the study found that impervious land had the most significant effect on LST, followed by water bodies and vegetation cover. The analysis of the proportion of the area with the lowest and highest LST showed interesting trends, with a greater portion of Aligarh City experiencing a temperature range between 15 and 16 °C in 2021 compared to previous years. However, the study also found that 13.55% of the area had a maximum LST of over 17 °C, which is higher than the previous measurement of 9.04%, and has been steadily increasing since 1991. The accuracy of the study was verified by detecting elevated temperatures in non-porous areas and cooler temperatures near green zones and water bodies. This study’s contribution to the research community lies in the data-driven, systematic analysis of the complex relationship between land use and cover and LST in an urban environment. The study’s findings suggest that alterations in land use/cover patterns have a significant impact on LST, which has important implications for urban planning policies. The research provides valuable insights for urban planners, policymakers, and city officials, as it highlights the need for sustainable and efficient urban planning policies to mitigate the effects of urban heat islands and rising temperatures. The study’s results have broader implications beyond Aligarh City and can inform land-use planning and policymaking in other cities facing similar challenges. This research presents a comprehensive analysis that can serve as a framework to inform land-use planning and policymaking, contributing to the development of sustainable and efficient urban environments.

Invasive alien plants (IAPs) continue to exert significant impacts on agriculture in many countries, resulting in food insecurity. IAPs reduce agricultural production through competition and parasitism with planted crops. More recently, the IAPs continue to extend their plasticity to tea plantations, especially in tropical and subtropical areas. This study thus aimed at exploring the potential of SPOT 7 and Sentinel 2 satellite data in mapping the occurrence and co-occurrence of three common IAPs Solanum mauritianum, Lantana camara, and Chromolaena odorata in the Tshivhase Tea Estate in Limpopo Province, South Africa. The stepwise logistic regression models were generated for Solanum mauritianum and Lantana camara occurrence as well as the observed and conditional co-occurrence probability of S. mauritianum (P1), L. camara (P2) and C. odorata (P3). From the remote sensing indices, the Brightness Index (BI) was significant in most SPOT 7 stepwise logistic regression models at p<0.05 whereas the blue, red, and near infrared (NIR) bands and standard deviation (STDv) variables were significant at p<0.05 in most of the Sentinel 2 models. The SPOT 7 model performed Sentinel-2 models, thus resulting in the area under the curve (AUC) of 0.96 for the conditional co-occurrence of S. mauritianum (P1) and L. camara (P2). The Sentinel 2 model yielded an AUC of 0.83. The SPOT 7 model performed superior in mapping the conditional co-occurrence of S. mauritianum and L. camara than the Sentinel 2 model. These results suggest that high spatial resolution satellite images like SPOT 7 can delineate the potential distribution of IAPs in the tea plantation and thus assisting in management strategies geared towards IAP’s elimination and control.

This study aims to detect changes that occurred in Ravi River channel over the period of last three decades (1990 to 2020). This paper spatially and temporally assesses the changes and geo-visualize variation of Ravi River using Landsat imageries. The maximum likelihood image classification technique has been used to process and analyze the spatial data in geographic information system (GIS) environment. It was found from the results that vegetation cover has gradually decreased from 976 km² in 1990 to 905 km² in 2019, whereas the built-up land had increased from 82 to 188 km² in the same temporal extent. Generally, the channel is shifted from east to west and the growth of built-up land to towards river which has pushed the channel. Similarly, the extreme discharge also causes change in channel shifting. Minor floods have been occurred after 2010 but Ravi is not affected much as their discharge was not that much higher to put any abrupt or significant effect on Ravi River’s channel pattern.

Detecting the potential region of the groundwater resource is a difficult issue all over the world. Nowadays, advanced geospatial technologies are excellent tools for efficient planning, managing, and assessing groundwater resources, particularly in data-scarce developing nations. Remote sensing (RS) and GIS-based multi-criteria decision analysis (MCDA) methods were applied to delineate the groundwater potential (GWP) in the Fetam-Yisir catchment, Blue Nile Basin, Ethiopia. Nine thematic layers: slope, geomorphology, normalized difference vegetation index (NDVI), topographic elevation, geology, land use/land cover (LULC), soil, rainfall, and drainage density from satellite and conventional data were used. The analytical hierarchy process (AHP) of an MCDA was employed to compute the corresponding normalized weight for the class in a layer and weights for the thematic layers on the base of their relative significance to the GWP. Integration of all thematic maps has been done using the “Weighted overlay” tool to obtain a GWP map. The GWP map is then validated using observed boreholes, and springs yield data. The verification of the final GWP zone map against yield data confirms 82% agreement indicating the authenticity of the method. The final GWP output confirmed that 43% area of the Fetam-Yisir catchment falls in a “good” GWP zone; 42%, 7.45%, 7.4%, and 0.02% of the area fall in “moderate,” “very good,” “poor,” and “very poor” GWP zones, respectively. The sensitivity analysis divulges that the GWP map is highly sensitive to slope with a mean variation index of 1.45%. Thus, this study can be used for effective groundwater exploration, development, and sustainable abstraction, as well as it guides the researchers in locating the GWP zone.