Article
Geography, Physical
Hong Jiang, Yong Zhang, Jinglan Lin, Xiaogan Zheng, Hui Yue, Yunzhi Chen
Summary: In this study, a new method using a specific composite image was developed to enhance the extra-high-voltage transmission line corridor (EHVTLC) in green mountains. By applying this method to satellite images, the EHVTLC becomes clearly visible in false-color synthesis. Spatial analysis revealed a significant difference between the EHVTLC and the buffer zone, with landslides and soil erosion identified in the buffer zone. The developed method can be used for enhancement and recognition of transmission line corridors in similar areas.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Yuxi Wang, Tiantian Chen, Qiang Wang, Li Peng
Summary: Based on the reconstructed vegetation index and climate data, this study examined the spatiotemporal pattern of vegetation and drought in China. It found that while some vegetated areas showed a browning trend, overall greening occurred, especially in northwestern China. Drought intensity increased over time and had a significant impact on vegetation growth, with a shorter response time. Additionally, anthropogenic activities had time-lagged effects on vegetation, indicating a lack of synchronization between human activities and ecosystem response.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Yue Jing, Long Pan, Yanling Sun
Summary: Due to the uneven distribution of environmental monitoring sites, there are data gaps in concentrations of PM2.5 obtained using traditional methods. Satellite products, such as MODIS AOD, can be used as an alternative data source. However, there are data gaps in winter. This study used VIIRS AOD to supplement MODIS AOD and developed a three-stage model to estimate PM2.5 with high accuracy.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Bei An, Yanan Jiang, Changcheng Wang, Peng Shen, Tianyi Song, Chihao Hu, Kui Liu
Summary: Shanghai Pudong International Airport (PDIA) is prone to uneven foundation settlement due to weak geological conditions, which affects its safe operation. Therefore, dynamic subsidence monitoring, especially in the runway area, is crucial. In this study, surface deformation inversion based on PS-InSAR and improved SBAS-InSAR techniques was conducted using radar images from August 2016 to June 2018. The results confirmed the reliability of time-series InSAR technique for monitoring surface deformation in coastal zone infrastructures.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Julia Fischer, Lukas Egli, Juliane Groth, Caterina Barrasso, Steffen Ehrmann, Heiko Figgemeier, Christin Henzen, Carsten Meyer, Ralph Mueller-Pfefferkorn, Arne Ruemmler, Michael Wagner, Lars Bernard, Ralf Seppelt
Summary: Geospatial data are essential in various domains related to global change and sustainability. However, users often face barriers accessing information about data quality and provenance. To address this issue, we conducted an interdisciplinary process involving data users, producers, and software developers, and identified the major needs for effective fitness-for-use assessments. Our approaches include providing structured metadata, developing efficient workflows and tools for data producers, and increasing the availability of quality and provenance information. These approaches increase transparency, facilitate fitness-for-use assessments, and improve research quality.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geography, Physical
Wenbo Wang, Huijun Zhou, Senyuan Zheng, Guonian Lue, Liangchen Zhou
Summary: This paper aims to estimate global ocean surface current using a global isotropic hexagonal grid from satellite remote sensing data. The gridded satellite altimeter data and wind data are interpolated into the centre of the global isotropic hexagonal grid. Geostrophic and Ekman currents components are estimated according to the Lagerlof Ocean currents theory. The results show that the ocean surface currents estimated based on the global isotropic hexagonal grid have considerable accuracy, with improvement over rectangular lat/lon grids.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Correction
Environmental Sciences
A. Dong, J. Dou, Y. Fu, R. Zhang, K. Xing
GEOCARTO INTERNATIONAL
(2023)
Article
Geography, Physical
Lijun Wang, Yang Bai, Jiayao Wang, Zheng Zhou, Fen Qin, Jiyuan Hu
Summary: This study improved the histogram matching method for color correction of multi-temporal images and tested the performance and accuracy of three semantic segmentation models based on weak samples for crop classification. The experimental results showed that the improved UNet++ model outperformed other models and achieved optimal accuracy in crop classification transfer cases.
GISCIENCE & REMOTE SENSING
(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
Remote Sensing
Mame Henriette Astou Sambou, Jean Albergel, Expedit Wilfrid Vissin, Stefan Liersch, Hagen Koch, Zoltan Szantoi, Wassim Baba, Mousse Landing Sane, Ibrahima Toure
Summary: Land use and Land cover change (LULCC) is a major global problem, and understanding LULCCs at the watershed level is important for transboundary river basin management. This study analyzed the past and future LULCCs in two significant watersheds of the Senegal River basin in West Africa using Landsat images and the Random Forest classification method. The results showed changes in vegetation, settlement, agricultural areas, water, and bareground over time in both watersheds, with different trends between the two periods in Faleme. The study also used the Multilayer Perceptron and Marcov Chain model to predict LULCCs in 2050 under business-as-usual assumptions.
EUROPEAN JOURNAL OF REMOTE SENSING
(2023)
Article
Remote Sensing
Lei Huang, Jinqing You, Xiongjian Zhu, Tao Shuai, Yongfu Liao
Summary: In this paper, an object-oriented method is proposed for fully polarimetric synthetic aperture radar (SAR) image classification. The method combines a pixel-based classifier and a region growing technique to extract homogeneous areas and assign class labels. Experimental results demonstrate that the proposed classification scheme achieves high accuracy and provides classification maps with more homogeneous regions.
EUROPEAN JOURNAL OF REMOTE SENSING
(2023)
Article
Remote Sensing
Zhengzhong Lai, Mengyu Hao, Weizeng Shao, Wei Shen, Yuyi Hu, Xingwei Jiang
Summary: A method for wind field reconstruction from dual-polarized Sentinel-1 SAR images during tropical cyclones is proposed in this paper, which does not require external information. The validation results show that the method performs comparably to existing meteorological products in some aspects but still requires improvement.
EUROPEAN JOURNAL OF REMOTE SENSING
(2023)
Article
Remote Sensing
Elham Shafeian, Fabian Ewald Fassnacht, Hooman Latifi
Summary: Detecting forest decline using remote sensing in arid and semi-arid regions is crucial for effective forest management. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests, with random forest being the most accurate approach. The classification results were unaffected by the Landsat acquisition times, indicating that additional environmental variables may be necessary to compensate for the limitations and challenges in identifying declining forest patches in semi-arid regions.
EUROPEAN JOURNAL OF REMOTE SENSING
(2023)
Article
Remote Sensing
Endre Hansen, Julius Wold, Michele Dalponte, Terje Gobakken, Lennart Noordermeer, Hans Ole Orka
Summary: The study applied area-based approaches to predict rot occurrence, rot severity, and rot volume. Random Forest models were built and validated using remotely sensed data and ground reference data. The results showed that rot volume models performed better due to the correlation between timber volume and rot volume.
EUROPEAN JOURNAL OF REMOTE SENSING
(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)
