International Journal of Applied Earth Observation and Geoinformation国际应用地球观测与地理信息杂志
International Journal of Applied Earth Observation and Geoinformation(英文缩写 INT J APPL EARTH OBS),ISSN 1569-8432,eISSN 1872-826X,中文译名:国际应用地球观测与地理信息杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-28 至 2026-09-28，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 7.672 | Q1 |
| 2022 | 7.500 | Q1 |
| 2023 | 7.600 | Q1 |
| 2024 | 8.600 | Q1 |
| 2025 | 8.200 | Q1 |
International Journal of Applied Earth Observation and Geoinformation 最新收录文献
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1. A cloud-computing framework for downscaled global 300 m SIF retrieval from Sentinel-3 and TROPOSIF.
PMID:日期:2026-06-01Sun-induced chlorophyll fluorescence (SIF) is a critical indicator of photosynthetic activity. Yet, existing satellite SIF products typically suffer from coarse spatial resolutions, generally coarser than 500 m, which limits their utility for fine-scale ecosystem studies. Here, we present a cloud-computing framework designed for the generation of a downscaled SIF product (S3-SIF) derived from Sentinel-3 (S3) Ocean and Land Colour Instrument (OLCI), with a spatiotemporal resolution of 300 m and 4 days. Our approach uses the Google Earth Engine (GEE) cloud-computing platform to integrate SIF produced from TROPOspheric Monitoring Instrument measurements within the 743-758 nm retrieval ( ), S3 radiances, S3-based vegetation traits, latitude and longitude within a Random Forest (RF) regression framework. Model training over Europe achieved robust performance against reference data ( = 0.767, RMSE = 0.137 mW m sr ), and the approach was subsequently extended globally. Validation against ground-based tower observations confirmed that S3-SIF effectively reproduces seasonal dynamics across diverse ecosystems. Comparisons against demonstrated strong spatial consistency in temperate agricultural regions, with the highest values in croplands and lower agreement in sparsely vegetated or persistently cloudy regions. Global mapping revealed coherent patterns of photosynthetic activity, with peak values in tropical rainforests and major agricultural zones. Importantly, S3-SIF reduces retrieval noise relative to and provides unprecedented insights into sub-kilometer spatial heterogeneity. By combining S3's rich spectral capabilities with GEE's scalable computing environment, our approach bridges the gap between current coarse-resolution SIF products and ESA's upcoming FLEX mission, offering a flexible and operational pathway for high-resolution monitoring of terrestrial photosynthesis.
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2. Detecting gaps between urban expansion and lighting infrastructure growth using daytime and nighttime satellite imagery.
PMID:日期:2026-02-01Characterizing the evolution of urban settlements is vital for informed urban planning that mitigates associated risks. Urban development has traditionally been examined in two dimensions using Earth observation: land cover change, monitored through daytime optical remote sensing, and lighting infrastructural change, observed using nighttime remote sensing. However, these two types of change have often been analyzed in isolation, limiting a comprehensive understanding of their combined impacts on urbanization. This study bridges this gap by simultaneously analyzing monthly Black Marble nighttime light (NTL) data and World Settlement Footprint data to compare lighting and urban land cover change in the Mediterranean region. Our findings reveal that 80% of urbanization-associated pixels display either urban land expansion or lighting growth, but not both. Confusion matrix highlights regional variations: commission errors are particularly high in West Asia (74%), indicating increases in nightlights driven by densification or road improvements without corresponding land conversion. Conversely, omission errors are higher in Western Europe (52%) and North Africa (47%), where urban land expansion occurs without observable lighting infrastructure growth, reflecting phenomena such as informal settlement growth, industrial infill, and energy-saving practices. This study enhances our understanding of the urbanization process through satellite observations, emphasizing the need for a more comprehensive monitoring approach that captures the diverse dimensions of urban growth.
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3. {"_":"Predicting environmental suitability and future spread range of in the Greater Horn of Africa using remote sensing and ensemble modeling.","i":["An. stephensi"]}
PMID:日期:2026-02-01Malaria, a life-threatening disease, remains a major global health challenge, particularly in Africa. While has long been the primary vector in Africa, the recent invasion of -an urban malaria vector native to South Asia, poses a growing threat to malaria control and elimination efforts. Understanding environmental suitability and its spread dynamics is critical for designing effective surveillance and vector control strategies. Although previous studies have mapped potential environmental suitability for , most have focused on temperature or environmental variables, overlooking other critical factors affecting mosquito life cycles. Moreover, little is known about the species' historical spread speed or projected expansion. While is already spreading in the region, this study aims to enhance predictive modeling of suitable habitats and identify areas at ongoing or future risk of invasion. Our approach integrates meteorological, environmental, geophysical, and socioeconomic variables, alongside an expanded occurrence dataset and ecologically constrained pseudo-absence sampling. The model achieved an accuracy of 0.93 in predicting locations during the 2021-2024 test period, outperforming previous studies in the region. We analyzed historical spread patterns, revealing a rapid increase in spread speed from 20 km/year in 2012 to over 120 km/year by 2024. Future spread was projected using environmental suitability, road connectivity, and population density, with the spread model achieving a temporal correlation of 0.66. Projections suggest continued expansion into western Ethiopia, southern Somalia, and southern Kenya, with climate change likely to increase environmental suitability in highland regions. This high-resolution, spatiotemporal framework provides actionable insights for current and future transmission hotspots and supports urgent, targeted interventions to mitigate the spread of under a changing climate.
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4. How accurately does L band vegetation optical depth predict aboveground biomass?
PMID:日期:2025-11-01L-band Vegetation Optical Depth (L-VOD) has emerged as a critical remote sensing proxy for monitoring global aboveground biomass (AGB) dynamics. Persistent methodological ambiguities, including the absence of standardized protocols for deriving AGB from L-VOD and the use of space-for-time assumption underpinning temporal predictions, pose challenges to the reliability of AGB estimates. In this study, we conducted a comprehensive evaluation on the current methodology using the SMOS-ICV2 L-VOD dataset and five AGB reference datasets. We find that all the existing fitting methods generally capture the AGB spatial variation, achieving 69-77 % variance explanation. Yet integrating tree cover significantly improves the AGB predictions in regions with small and medium L-VOD values. However, all methods fail to capture the spatial variation of AGB references in dense rainforests with L-VOD > 1, where the AGB reference data also show large discrepancies. Testing on space-for-time assumption reveals that spatial AGB sensitivities to L-VOD tend to be larger than temporal sensitivities. We suggest incorporating long-term in situ observations and remotely sensed vegetation structural data to understand the discrepancy between the AGB-L-VOD sensitivities and to improve AGB predictions. By providing a comprehensive evaluation of fitting methods, our results offer a cautionary tale to the use of L-VOD data to infer AGB dynamics and the necessity of developing long-term field-based biomass change datasets for further constraining and evaluating AGB predictions from remote sensing observations.
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5. Geospatial impact evaluation of a low-cost agricultural intervention for enhancing environmental resilience.
PMID:日期:2025-07-01Land degradation poses a significant threat to ecosystems and livelihoods, particularly in disaster-prone regions. In these settings, the promotion of certain agricultural practices with economic incentives, such as sugarcane () farming, offers a potential solution for enhancing economic stability and mitigating environmental degradation. Despite its promise, the effectiveness of sugarcane as an agricultural intervention remains understudied, especially regarding its environmental benefits. Our study evaluates the impact of sugarcane cultivation in western Nepal, a region highly vulnerable to soil erosion and riverbank degradation due to the presence of flood-prone landscapes. We conducted a geospatial impact evaluation (GIE), which integrated remote sensing data and econometric techniques, including optimal full matching (OFM) and difference-in-differences (DID). We assessed the causal impact of a program promoting sugarcane farming on its adoption and environmental outcomes, measured using multi-temporal satellite imagery, crop phenology, and clustering algorithms, along with machine learning and visual interpretation methods. Our results show that sugarcane adoption increased significantly in both treated and spillover communities, highlighting its potential as a sustainable agricultural practice. However, while uptake was evident, the expected environmental outcomes, such as soil erosion control and riverbank stabilization, did not materialize. This study demonstrates the potential of GIE in evaluating low-cost interventions for sustainable development and provides insights into the role of sugarcane cultivation in promoting climate resilience. The findings underscore the need for complementary interventions and extended timeframes to realize long-term environmental benefits, contributing valuable evidence for policymakers and development practitioners.
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6. Optimizing the detection of emerging infections using mobility-based spatial sampling.
PMID:日期:2024-07-01Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.
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7. Unraveling near real-time spatial dynamics of population using geographical ensemble learning.
PMID:日期:2024-06-01Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.
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8. The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type.
PMID:日期:2024-04-01To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain bird species richness, recent studies suggest that unclassified remote-sensed images can provide equally good or better results. In our study, we aimed to investigate whether unclassified multispectral data from Landsat 8 can replace image classification for bird diversity modeling. Moreover, we also tested the Spectral Variability Hypothesis. Using the Atlas of Breeding Birds in the Czech Republic 2014-2017, we modeled species richness at two spatial resolutions of approx. 131 km (large squares) and 8 km (small squares). As predictors of the richness, we assessed 1) classified land cover data (Corine Land Cover 2018 database), 2) spectral heterogeneity (computed in three ways) and landscape composition derived from unclassified remote-sensed reflectance and vegetation indices. Furthermore, we integrated information about the landscape types (expressed by the most prevalent land cover class) into models based on unclassified remote-sensed data to investigate whether the landscape type plays a role in explaining bird species richness. We found that unclassified remote-sensed data, particularly spectral heterogeneity metrics, were better predictors of bird species richness than classified land cover data. The best results were achieved by models that included interactions between the unclassified data and landscape types, indicating that relationships between bird diversity and spectral heterogeneity vary across landscape types. Our findings demonstrate that spectral heterogeneity derived from unclassified multispectral data is effective for assessing bird diversity across the Czech Republic. When explaining bird species richness, it is important to account for the type of landscape and carefully consider the significance of the chosen spatial scale.
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9. Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.
PMID:日期:2023-09-01Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.
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10. A deep learning approach for automatic identification of ancient agricultural water harvesting systems.
PMID:日期:2023-04-01Despite the harsh climatic conditions in the Central Negev Desert, Israel, thousands of dry stonewalls were built across ephemeral streams between the fourth and seventh centuries CE to sustain productive agricultural activity. Since 640 CE, many of these ancient terraces have remained untouched but buried by sediments, covered by natural vegetation, and partially destroyed. The main goal of the current research is to develop a procedure for the automatic recognition of ancient water harvesting systems by incorporating two remote sensing datasets (a high-resolution color orthophoto and LiDAR-derived topographic variables) and two advanced processing methods (an object-based image analysis (OBIA) and a deep convolutional neural networks (DCNN) model). A confusion matrix of object-based classification revealed an overall accuracy of 86% and a Kappa coefficient of 0.79. The DCNN model achieved a Mean Intersection over Union (MIoU) value for testing datasets of 53. The individual IoU values of terraces and sidewalls were 33.2 and 30.1, respectively. The current study demonstrates how incorporating OBIA, aerial photographs, and LiDAR in the context of DCNN improves the identification and mapping of archaeological structures.