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)
Correction
Environmental Sciences
A. Dong, J. Dou, Y. Fu, R. Zhang, K. Xing
GEOCARTO INTERNATIONAL
(2023)
Review
Environmental Sciences
David Sengani, Abel Ramoelo, Emma Archer
Summary: This paper examines a feature-level fusion framework for detecting and mapping land degradation using optical sensors and Synthetic Aperture Radar (SAR) satellite data. The study reviews 78 research articles published over the past 24 years and discusses the potential of image fusion in remote sensing applications. It also highlights the importance of SAR and optical image fusion and quality metrics for objectively assessing fusion performance.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Makgabo Johanna Mashala, Timothy Dube, Kingsley Kwabena Ayisi, Marubini Reuben Ramudzuli
Summary: Population growth and environmental shifts have increased the pressure on land use and cover (LULC), requiring crucial management and adaptive strategies to preserve the balance between ecosystem services and human well-being in watersheds. This study used Google Earth Engine (GEE) to analyze 31 years of LULC changes in Letaba watershed, mapping LULC classes with high accuracy across four timeframes. The trends revealed population-driven shifts, emphasizing the need for adaptive strategies and the importance of embracing climate-smart agriculture for long-term food and environmental security in Letaba watershed.
GEOCARTO INTERNATIONAL
(2023)
Review
Environmental Sciences
Frane Gilic, Mateo Gasparovic, Martina Baucic
Summary: Data on land cover are crucial in assessing human impact on nature and the environment and vice versa. Earth observation (EO) satellite images have long been utilized to produce global and continental land cover maps, but challenges persist in the production process. This research analyzes recent land cover products, identifies the main steps in map production, highlights existing challenges, and provides directions for future research. Additionally, it presents an overview of EO satellite missions and classification algorithms commonly used for moderate resolution land cover mapping (10-30 m).
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Guixi Tang, Zhice Fang, Yi Wang
Summary: In this study, an AutoML-based global landslide susceptibility prediction framework was proposed and achieved good performance. The framework made predictions at two spatial resolutions and used the global prediction results at 90 m to improve regional predictions. The results showed that the model outperformed the original global predictions and can reliably promote the use of intelligent learning methods in global landslide susceptibility prediction.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Vikas Dugesar, Manish K. Pandey, Prashant K. Srivastava, George P. Petropoulos, Sanjeev Kumar Srivastava, Virendra Kumar Kumra
Summary: This study evaluates the accuracy of SNAP-Sentinel-2 Prototype Processor (SL2P) derived Leaf Area Index (LAI) and proposes a new method for generating new LAI datasets through data fusion. The results show a good correlation and low error between SNAP-derived LAI and ground-observed LAI. After implementing data fusion, both SNAP-derived LAI and Global LAI products exhibit improved performance statistics with ground observed data sets.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Michael P. Bishop, Brennan W. Young, Jeffrey D. Colby
Summary: Remote sensing of mountain environments is challenging due to complex atmosphere-topography-landcover interactions. This study evaluates the topographic effects and identifies discrepancies in commonly used parameterization schemes, providing new insights into the modulation of radiation transfer in complex terrain.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Endeg Aniley, Temesgen Gashaw, Tesfalem Abraham, Sintayehu Fetene Demessie, Haimanote Kebede Bayabil, Abeyou W. Worqlul, Pieter R. van Oel, Yihun T. Dile, Abebe Demissie Chukalla, Amare Haileslassie, Gizachew Belay Wubaye
Summary: This study evaluated the performance of CHIRPS and MSWEP precipitation products in different agro-ecological zones of Ethiopia, and found that CHIRPS performed better in estimating rainfall totals at different temporal scales, while MSWEP showed superior performance in detecting daily rainfall occurrences.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Mucella Ozbay Karakus
Summary: This study utilized a unique meteorological dataset and employed a deep hybrid Convolutional Neural Network-Support Vector Regression approach to predict hydropower generation. The comparison results showed that the CNN-SVR model outperformed other models in predicting the net head and hydroelectric power generation.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Daehyun Kim, Insang Song, Lorrayne Miralha, Daniel R. R. Hirmas, Ryan W. W. McEwan, Tom G. G. Mueller, Pavel Samonil
Summary: In this study, we investigated the impact of inherent spatial structure in soil properties on the accuracy of machine learning approaches in predicting soil variability. We compared the performance of four machine learning algorithms and two non-machine learning algorithms. The results showed that none of the machine learning algorithms outperformed the non-machine learning approaches in terms of residual values and spatial autocorrelation. We recommend using random forest for weakly autocorrelated soil variables (Moran's I < 0.1) and spatial filtering regression for relatively strongly autocorrelated variables (Moran's I > 0.4). This research provides a framework for selecting appropriate model algorithms based on spatial autocorrelation criteria for input variables.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Asnake Enawgaw Amognehegn, Asmare Belay Nigussie, Anteneh Yayeh Adamu, Gerawork Feleke Mulu
Summary: The study aims to analyze future drought characteristics in the Kessie watershed, upper Blue Nile Basin, under the impact of climate change. Three drought indices were used: Reconnaissance Drought Index (RDI), Streamflow Drought Index (SDI), and Agricultural Standardized Precipitation Index (aSPI). The results indicate that the research area will experience high magnitude and increasing frequency of meteorological, agricultural, and hydrological droughts, with a stronger association between hydrological and agricultural droughts as the accumulation period lengthens.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Nitin Liladhar Rane, Anand Achari, Ali Hashemizadeh, Samruddhi Phalak, Chaitanya B. Pande, Monica Giduturi, Mohd Yawar Ali Khan, Abebe Debele Tolche, Nissren Tamam, Mohamed Abbas, Krishna Kumar Yadav
Summary: Evaluating the suitability of areas for sustainable urban settlement growth is essential. This study used a multi-influence factor approach based on a geographic information system to identify ideal locations for future urban settlement in Nashik. The results showed that most suitable sites were located near existing residential areas and major roads.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Hua Wang, Qiaonan Wan, Wei Huang, Jiqiang Niu
Summary: We conducted a scientific quantitative analysis of the spatiotemporal evolution and driving factors of land use in Henan Province, and found disparities in land use across different regions, providing suggestions for sustainable development decisions.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Matthias Weigand, Michael Wurm, Ariane Droin, Thomas Stark, Jeroen Staab, Jurgen Rauh, Hannes Taubenboeck
Summary: Green space is an important resource in urban areas, but its availability is not evenly distributed. This study compares the availability of green land cover and public green space on a national scale in Germany, analyzing the differences in accessibility and equity. The results show that public green space is less evenly distributed among the population compared to green land cover, and the equity of green space varies between rural and urban areas.
GEOCARTO INTERNATIONAL
(2023)
