Article
Environmental Sciences
Divesh Pangtey, Hitendra Padalia, Rahul Bodh, Ishwari Datt Rai, Subrata Nandy
Summary: Global decline in biodiversity requires systematic monitoring. This study assessed the performance of Rao's Q index derived from multi-date Sentinel-2 NDVI in estimating tree diversity in seasonal tropical forests. The approach showed good correlation with tree diversity, especially during the leaf flushing period.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Muhammad Asif, Jamil Hasan Kazmi, Aqil Tariq, Na Zhao, Rufat Guluzade, Walid Soufan, Khalid F. Almutairi, Ayman El Sabagh, Muhammad Aslam
Summary: We used the CA-Markov integrated technique to study LULC changes in the Cholistan and Thal deserts in Punjab, Pakistan. The data from Landsat satellites were classified using the Random Forest methodology. The projection for 2038 showed increased urbanization and development in croplands and residential centers.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
V. F. Sales, D. C. Zanotta, A. Marques Jr, G. Racolte, M. Muller, C. L. Cazarin, D. Ibanez, L. Gonzaga Jr, M. R. Veronez
Summary: Accurate characterization of carbonate rocks is essential for evaluating potential outcrops in the oil industry. Remote sensing instruments have played a key role in estimating mineral characteristics at various scales. However, existing mathematical expressions for converting radiance values to mineral information are often limited to specific regions and general solutions tend to yield poor results. This paper proposes an adaptive approach using OLI-Landsat 8 image data to estimate subpixel amounts of carbonate rocks. The method is derived from extensive spectral analysis and further optimized through a genetic algorithm, demonstrating its adaptability and superior performance compared to existing carbonate indices.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Songyang Xiang, Zhanghua Xu, Wanling Shen, Lingyan Chen, Zhenbang Hao, Lin Wang, Zhicai Liu, Zenglu Li, Xiaoyu Guo, Huafeng Zhang
Summary: In this study, different features from Sentinel-1 SAR and Sentinel-2 optical images were evaluated to extract bamboo forest information from bright and shadow areas. The combination of spectral, texture and backscatter features yielded the highest overall classification accuracy and Kappa coefficient, indicating the effectiveness of these features in bamboo forest identification. This study has the potential to refine remote sensing of bamboo forest identification in complex terrain areas.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Laura Alonso, J. C. Porto-Rodriguez, J. Picos, J. Armesto
Summary: This study examines the usefulness of a land cover map produced automatically using Sentinel-2 images as a complement to the official Spanish forest map in Galicia. The results show that the Sentinel-2 map has a higher ability to identify harvestings or disturbances and map trees outside the forest compared to the official cartography. This indicates that Sentinel-2 based maps could be a powerful tool to reduce the information gap and update the official forest map more frequently.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Enkhmanlai Amarsaikhan, Nyamjargal Erdenebaatar, Damdinsuren Amarsaikhan, Munkhdulam Otgonbayar, Batbileg Bayaraa
Summary: The aim of this study is to determine an appropriate method to estimate and map pasture biomass in a forest-steppe area of Mongolia. Machine learning methods such as random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) were compared. The PLSR method demonstrated the highest accuracy.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Vida Atashi, Taufique H. Mahmood, Kabir Rasouli
Summary: The study revealed four distinct phases of variation in surface water area in the Red River Basin over the past few decades, with two phases experiencing strong wetting and the other two phases being relatively dry. These findings have implications for the assessment of nutrient concentration in lakes and wetlands.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Rizwan Niaz, Anwar Hussain, Mohammed M. A. Almazah, Ijaz Hussain, Zulfiqar Ali, A. Y. Al-Rezami
Summary: This study focuses on the characteristics of spatiotemporal and inter-seasonal meteorological drought. The Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to analyze the drought in selected stations. The results show that an increase in moisture conditions during the spring season will decrease the probability of drought in the summer, while in the summer-to-autumn transition, there is a 6.73% chance of being in a higher category.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Yuzeng Yao, Zining Li, Jing Liu, Jianfei Fu, Tingting Hou, Yongli Zhang
Summary: In this study, quadratic polynomial, cubic spline, and quartic polynomial methods were used to interpolate the absorption wavelength near 2250 nm. The result of the quadratic polynomial is continuous without data overlapping or intervals, making it the most suitable for discriminating chlorites. Field validation shows that the spatial distribution of Fe-chlorite is consistent with high-grade magnetite ore bodies and the recently discovered concealed iron bonanza.
GEOCARTO INTERNATIONAL
(2023)
Article
Geography, Physical
Shuai Hong, Jie Shen, Guonian Lue, Xiaoyan Liu, Yirui Mao, Nina Sun, Long Tang
Summary: Most existing virtual scenarios for the protection of Chinese classical private gardens are not able to reflect the aesthetic significance of their historical period due to their modern expression style. This study proposes a deep neural network that transfers the aesthetic style from landscape paintings to virtual scenarios of classical private gardens, based on the commonality between traditional Chinese landscape paintings and classical private gardens.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
You Xu, Dongjie Fu, Hao Yu, Fenzhen Su, Vincent Lyne, Rong Fan, Bin He, Tingting Pan, Jiasheng Tang
Summary: Accurate high-resolution maps of oil palm plantations are essential for effective management of their environmental and socio-economic impacts. However, current statistics and maps do not include young industrial and small-holder plantations. In this study, global oil palm plantations in 2020 were classified into four subclasses using satellite data and an image-oriented classification algorithm. The results provide valuable information for future planning and monitoring of oil palm-related development in major palm-growing countries.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Review
Geography, Physical
Xiao-Peng Song
Summary: Land change science has advanced with the development of remote sensing technology. This review summarizes the milestones in global land cover and change mapping, showcasing the significant progress in monitoring global land change from space. It also provides a critical overview of recent advancements in land change research, focusing on technical aspects and practical applications. Furthermore, it offers insights into the future directions of land change monitoring, emphasizing the importance of generating analysis ready data and applying artificial intelligence algorithms.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Zhimei Zhang, Yanguo Fan, Zhijun Jiao, Bowen Fan, Junwei Zhou, Zhanhao Li
Summary: Urban population explosion can have a negative impact on humans' mental and physical performance due to ecological discomfort. Therefore, it is crucial to detect and monitor vegetation and predict its ecological benefits. The study introduces an optimized hyperspectral image-based vegetation index (OHSVI) to overcome the interference caused by the complex composition of urban ground objects. Additionally, a new vegetation ecological benefits index (VEBI) based on the 3D structure of vegetation is proposed for accurately predicting its ecological benefits in relation to surrounding buildings. The results show promising accuracy and suggest the efficiency of VEBI when combined with remote sensing and lidar datasets.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Review
Geography, Physical
Shuyue Yang, Xiaotong Zhang, Shikang Guan, Wenbo Zhao, Yanjun Duan, Yunjun Yao, Kun Jia, Bo Jiang
Summary: Surface incident shortwave radiation (R-s) is crucial for substance and energy circulation, and its accurate estimation is significant for climate studies. This study validates R-s estimates from seven representative products using ground measurements from multiple data sources. The results show that the selected products generally overestimate R-s globally, with biases ranging from 0.48 to 21.27 W/m(2). Satellite retrievals demonstrate relatively better accuracy compared to ground measurements, but all selected products perform poorly at high-latitude regions with RMSEs greater than 50 W/m(2).
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Mohammad Mansourmoghaddam, Iman Rousta, Haraldur Olafsson, Przemyslaw Tkaczyk, Stanislaw Chmiel, Piotr Baranowski, Jaromir Krzyszczak
Summary: This study examined the variations of carbon monoxide (CO) and urban environmental indices and their correlations using Sentinel-5, MODerate resolution Imaging Spectroradiometer (MODIS), and Landsat-8 images. Correlations were found among the assessed indices for 932 Iranian cities. The highest CO levels occurred in the spring of 2019 and 2020, with a slight decrease in 2021. The Enhanced Vegetation Index (EVI) showed relatively high values in the spring of 2019 and 2020. Abnormally high values of the Absorbing Aerosol Index (AAI) and Urban Index (UI) were correlated with spikes in CO level.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Mengmeng Hong, Juanle Wang, Baomin Han
Summary: Under the pressure of SDG15.3.1 compliance, addressing land salinization degradation in the Yellow River Basin is crucial due to its role as China's granary. A salinization inversion model was constructed for each zoning unit in the basin based on geographical factors, and a feature space inversion process was utilized for automatic salinization inversion using the Google Earth Engine platform. The resulting salinization distribution maps of the basin in 2015 and 2020 were analyzed, along with the causes and spatiotemporal variations of salinization, leading to proposed prevention and control suggestions. This study can be expanded to larger and more complex geographical regions.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Sanaz Saidi, Shamsollah Ayoubi, Mehran Shirvani, Kamran Azizi, Shuai Zhao
Summary: In this study, different machine learning models (Cu, RF, SVM, GPR) were used to predict soil phosphorous sorption parameters (PSPs). The results showed that using topographic attributes alone was not sufficient for accurate prediction of PSPs, but combining remote sensing data with soil properties reliably predicted PSPs. The RF model had the lowest RMSE values for MBC, the SVM model for PBC, the Cubist model for SPR, and the RF model for SBC. The study concluded that remote sensing data was an easily obtainable dataset that could reliably predict PSPs in the study area.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Review
Geography, Physical
Jianghong Zhao, Yi Wang, Xintong Dou, Xin Wang, Ming Guo, Ruiju Zhang, Haimeng Li
Summary: This paper presents a comprehensive survey of research on spatiotemporal graph neural networks (ST-GNNs) in the prediction domain. It introduces the background and computational paradigm of ST-GNNs, and thoroughly reviews 59 well-known models in recent years. The paper also summarizes the categories and application fields of spatiotemporal graph data, and analyzes the performance and efficiency of some models. Finally, it summarizes the evolution history and future direction of ST-GNNs, facilitating future researchers to understand the current state of prediction research by ST-GNNs.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Xuexin Wei, Yang Liu, Lin Qi, Jilong Chen, Guoqin Wang, Linxiu Zhang, Ronggao Liu
Summary: Africa's extensive woodlands, savannas, and rainforests have been experiencing significant changes in tree cover over the past few decades. This study assessed the spatio-temporal trend of African forests from 2000 to 2020 using a high-resolution tree cover product, revealing an increase in forest area at a rate of 3.59 million ha/year. The study also identified hotspots of forest gain in the north belt of woodlands and savannas, while forest loss was concentrated in East and South Africa. This research is crucial for monitoring forest change and promoting sustainable development in African countries.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Chen Zhang, Biao He, Renzhong Guo, Ding Ma
Summary: The study provides a practical texture baking processing pipeline for generating 3D models to reduce the model complexity and preserve visually pleasing details. By applying mesh simplification and texture baking, a simplified model with baked textures is obtained, which has a pleasing visualization effect. This approach is useful for real-time rendering with limited rendering hardware as no additional memory or computing capacity is required to properly preserve the relief details of the model.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
