Article
Environmental Sciences
Chih-Hong Huang, Hsin-Hua Tsai, Chia-Hsing Chen
Summary: This research focuses on the vertical structure of the urban heat island effect. By utilizing big data from sounding balloon and surface measurements, the study confirms the existence of the atmospheric boundary layer and the canopy temperature inversion phenomenon. The height of inversion fluctuates with changes in wind speed and cloudiness, which can guide urban planning strategies to mitigate the urban heat island effect.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Rizwan Niaz, Mohammed M. A. Almazah, A. Y. Al-Rezami, Zulfiqar Ali, Ijaz Hussain, Talha Omer
Summary: The study proposes a new framework to accumulate spatial and temporal information for meteorological drought from various stations and indicators. The framework utilizes the MMCFS and MJIW techniques to assess the drought using three commonly used SDI. By using monthly data from six meteorological stations in the northern region for 47 years, the study provides explicit spatiotemporal information on meteorological drought. The results can serve as an early warning for effective water resource management to mitigate negative drought impacts in Pakistan.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Sehinde Akinbiola, Ayobami T. Salami, Olusegun O. Awotoye, Samuel. O. Popoola, Johnson A. Olusola
Summary: Using OBIA technique, this study examined the characteristics, carbon stocks, and tree species of tropical forests in southwestern Nigeria. SfM technique was used to produce orthomosaics, and near-infrared band was employed for forest assessment. Carbon stock estimates ranged from 65 kg to 8,488 kg per tree, with an overall estimate of 450 tons per hectare. OBIA enhanced carbon inventory and provided spatial characterization of species distribution.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Xuzi Jiang, Xiangyu Min, Tiantian Ye, Xinju Li, Xiao Hu
Summary: Monitoring the subsidence at different periods in high underground water level coal mine areas is important for land reclamation. Using differential interferometric synthetic aperture radar (D-InSAR) with 22 Sentinel-1 images, subsidence in a working face in Guotun Coal Mine was accurately monitored. The results showed the formation of a single-gradient subsidence funnel as a key indicator of the active period, reduced errors in the central subsidence area compared to the edge area, and high monitoring accuracy throughout different subsidence periods.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Darius Phiri, Jacob Mwitwa, Phillimon Ng'andwe, Kennedy Kanja, Justin Munyaka, Felix Chileshe, Patan Hamazakaza, Sydney Kapembwa, Jane Musole Kwenye
Summary: Forest reserves in Zambia have experienced agricultural expansion, resulting in a decline in forest area and an increase in cropland. Approximately 50% of the forest reserves have been encroached, with 10% severely impacted. The drivers of agricultural expansion include population growth, loss of soil fertility, high market demand for crops, land tenure system, and lack of law enforcement. This study emphasizes the importance of considering trade-offs between agricultural expansion and forest conservation in relation to population growth and highlights the need for sustainable agricultural practices and the implementation of legislative frameworks.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Xin Tong, Limin Duan, Tingxi Liu, Zhenlei Yang, Yixuan Wang, Vijay P. P. Singh
Summary: Field spectroradiometer and aboveground biomass data were collected in semiarid grasslands in Inner Mongolia, China, and four forms of commonly used vegetation indices were calculated. Linear regression analysis was used to select the best vegetation indices for estimating aboveground biomass. The combination of the best vegetation indices improved the accuracy of the estimation significantly. This approach is important for accurate and effective grassland aboveground biomass estimation.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Linfeng Wu, Huajun Wang
Summary: In this study, a global and pyramid convolutional network with a hybrid attention mechanism (GPHANet) is proposed for hyperspectral image classification. The model leverages a two-branch architecture to extract both local and global features and applies a compact hybrid attention mechanism (HAM) to capture long-range dependencies. Experimental results on three datasets demonstrate that GPHANet outperforms state-of-the-art methods.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Samy I. Elmahdy, Mohamed M. Mohamed
Summary: This study aims to create precise land use/land cover (LULC) maps using an ensemble machine learning approach that integrates random forest (RF) and support vector machine (SVM). The overall accuracy was enhanced from 70% to more than 90% by training the classifier using multi-temporal Landsat and QuickBird remote sensing data. The proposed approach shows potential for application in other regions and for producing accurate LULC maps.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Murali Krishna Gumma, Pranay Panjala, Kumara Charyulu Deevi, Pavan Kumar Bellam, Venkateswarlu Dheeravath, Ismail Mohammed
Summary: In 2014, the State of Telangana in southern India initiated the Mission Kakatiya project, investing over USD 2 billion to repair and restore more than 46,000 irrigation water tanks. By using remote sensing imagery and land use/land cover mapping algorithms, this study aimed to map the temporal changes in cropland areas and water bodies caused by the project. The results showed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. Ground survey data and remote sensing analysis demonstrated an overall accuracy of 87%, indicating the effectiveness of periodic monitoring in capturing the project's impact on land use.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Merve Yilmaz
Summary: Population density is essential for addressing land use and accessibility issues in cities under the Sustainable Development Goals. The study compares and evaluates regression tools to estimate population density differences. The analysis tools used include Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The results show the importance of descriptive variables such as density difference and connectivity in the Random Forest-Based Classification model, and explanatory variables like centrality, vehicle ownership, and accessibility in the Multiple Linear Regression model. The results of the non-spatial Multiple Linear Regression model and the spatial Geographically Weighted Regression model are found to be similar.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Shokouh Riahi, Abbas Bahroudi, Maysam Abedi, Soheila Aslani
Summary: This study successfully created a potential map of porphyry Cu mineralization in the Urumieh-Dokhtar area of Kerman province, Iran, by employing different image processing methods and integrating Landsat 8 OLI and ASTER data.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Kamal Taheri, Thomas M. Missimer, Maryam Bayatvarkeshi, Siamak Mahmoudi Sivand, Saadi Fathi, Amin Toranjian, Behrouz Dehghan Manshadi
Summary: In this study, the DRASTIC model was modified by adding two factors related to sinkholes, distance to a sinkhole and the sinkhole catchment factors, to evaluate the pollution potential of the Abarkouh aquifer in Yazd province, Central Iran. The results showed that the Sin-DRASTIC model can provide a more accurate prediction of the vulnerability of aquifers with sinkholes to pollution.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Swakangkha Ghosh, George Philip, Anup K. Prasad, Tajdarul H. Syed, Sarada P. Mohanty
Summary: With the increasing availability of high-resolution satellite data and the rapid development of Geographic Information System (GIS) technology, the mapping of active faults and quantification of tectonic activity in inaccessible regions has greatly improved. This study examined the tectonic activity in the Trans-Yamuna region of the NW Himalaya using geomorphic indices derived from a Digital Elevation Model (DEM). The results suggest that the majority of the region is tectonically active, with upliftment continuing to occur in the north of the Main Boundary Thrust (MBT).
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Kubra Bahsi, Beyza Ustaoglu, Samet Aksoy, Elif Sertel
Summary: This research aims to determine the amount of crop-based residue burning (CRB) in the Southeastern Anatolia Region of Turkiye, using Sentinel-2 images and Intergovernmental Panel on Climate Change standards. The analysis showed that CRB practices in 2019 released 14,444.307 Gg of Greenhouse Gases and 117.809 Gg of Particulate Matters. The results can improve national statistics and support agricultural decision-making processes.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Bowen Niu, Quanlong Feng, Jianyu Yang, Boan Chen, Bingbo Gao, Jiantao Liu, Yi Li, Jianhua Gong
Summary: Urbanization leads to increased solid waste worldwide, endangering the environment and people's wellbeing. Detecting solid waste sites accurately is challenging due to complex landscapes, and few studies have investigated solid waste mapping across multiple cities and large areas. This study proposes a deep learning model that integrates a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer to map solid waste from high-resolution remote sensing imagery. Experiments in China, India, and Mexico show that the model achieves high performance with an average accuracy of 90.62%. The novelty lies in fusing CNN and Transformer for solid waste mapping without the need for pixel-wise labeled data. Future work may explore more advanced methods such as semantic segmentation for fine-grained solid waste classification.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Mozhgan Bagherinia, Siamak Bodaghpour, Neamat Karimi, Fatemeh Ghasempour, Muhammad Bilal, Alaa Mhawish
Summary: Based on ground-based and satellite-based data, spatio-temporal analyses of air quality in Tehran during the lockdown periods in 2020 and 2021 were conducted. The study evaluated the differences in emissions of six air pollutants at different time scales. The results showed a decrease in pollution levels in 2020 compared to the baseline period, but a smaller reduction in 2021 and an increase in PM2.5 and PM10 levels. Satellite-based concentrations varied compared to the pre-lockdown year, with consistent trends in AOD during dust events in March and April.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Jaakko Putkonen, Taufique H. Mahmood
Summary: A buried ice mass in Antarctica dating back to 3-5 million years has been discovered, demonstrating the potential for long-term preservation of ancient ice. By analyzing over 8,000 high-resolution satellite images, we have identified 22 new sites in the Transantarctic Mountains that likely conceal massive buried ice bodies.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Rana Muhammad Adnan, Hong-Liang Dai, Reham R. Mostafa, Abu Reza Md. Towfiqul Islam, Ozgur Kisi, Salim Heddam, Mohammad Zounemat-Kermani
Summary: The accurate assessment of groundwater levels is critical, especially in arid and semi-arid areas. This study compares the performance of new extreme learning machines (ELM) methods tuned with metaheuristic algorithms in groundwater level estimation.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Yuzhen Zhang, Jingjing Liu
Summary: This study aims to investigate the improvement of forest aboveground biomass (AGB) prediction accuracy through the utilization of temporal features extracted from satellite data. The results demonstrate that using all annual features leads to the most accurate AGB prediction, while information extracted by other methods gives rise to less accurate results. The study highlights the necessity of utilizing annual time-series data, particularly the annual surface reflectance data, for AGB prediction.
GEOCARTO INTERNATIONAL
(2023)
Editorial Material
Environmental Sciences
Kamlesh Lulla, Brad Rundquist, Szilard Szabo
GEOCARTO INTERNATIONAL
(2023)
