Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 217, P. 149 - 164

Published: Aug. 29, 2024

Language: Английский

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Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102702 - 102702

Published: July 3, 2024

Twenty years ago, the Spectral Variation Hypothesis (SVH) was formulated as a means to link between different aspects of biodiversity and spatial patterns spectral data (e.g. reflectance) measured from optical remote sensing. This hypothesis initially assumed positive correlation variations computed raster in environment, which would turn correlate with species richness: following SVH, areas characterized by high heterogeneity (SH) should be related higher number available ecological niches, more likely host when combined. The past decade has witnessed major evolution progress both terms remotely sensed available, techniques analyze them, questions addressed. SVH been tested many contexts variety sensing data, this recent corpus highlighted potentials pitfalls. aim paper is review discuss methodological developments based on leading knowledge well conceptual uncertainties limitations for application estimate dimensions biodiversity. In particular, we systematically than 130 publications provide an overview ecosystems, characteristics (i.e., spatial, temporal resolution), metrics, tools, applications strength association SH metrics reported each study. conclusion, serves guideline researchers navigating complexities applying offering insights into current state future research possibilities field estimation data.

Language: Английский

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Quantifying Cloud-Free Observations from Landsat Missions: Implications for Water Environment Analysis

Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 4

Published: Jan. 1, 2024

Since the launch of Landsat missions, they have been widely employed for monitoring water environments. However, designed revisiting period satellites is 16 days, leading to large uncertainties when tracking long-term changes in environmental parameters characterized by high spatiotemporal dynamics. Given this challenge, comprehensive assessments global distribution cloud-free observations (NCOs) obtained from missions and their applications environments hydrology are currently unavailable. In study, we utilized >4.8 million images acquired Landsat-5, Landsat-7, Landsat-8 quantify analyze variations NCOs on a scale. Our findings indicate that while demonstrate substantial spatial temporal heterogeneities, provides nearly twice as many mean annual (21.8 ± 14.7 year −1 ) compared Landsat-7 (10.8 4.8 Landsat-5 (8.3 5.6 ). Moreover, examined how overlap area adjacent orbits contributes improving NCOs, noting all observation areas above 45°N covered overlapping paths east–west direction. Additionally, conducted an analysis potential arising obtaining trends various parameters, including total suspended sediment (TSS) concentration, level, surface temperature (WST), ice cover phenology. The results revealed uncertainty quality (i.e., TSS) much higher than hydrological level WST). quantification assessment impact parameter estimations contribute enhancing our understanding limitations opportunities associated with utilizing data studies.

Language: Английский

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Classifying raw irregular time series (CRIT) for large area land cover mapping by adapting transformer model

Science of Remote Sensing, Journal Year: 2024, Volume and Issue: 9, P. 100123 - 100123

Published: Feb. 9, 2024

For Landsat land cover classification, the time series observations are typically irregular in number of a period (e.g., year) and acquisition dates due to cloud variations over large areas plan long periods. Compositing or temporal percentile calculation usually used transform regular variables so that machine deep learning classifiers can be applied. Recognizing composite calculations have information loss, this study presents method directly Classifying Raw Irregular Time (CRIT) ('raw' means good-quality surface reflectance without any derivation) by adapting Transformer. CRIT uses day year as classification input align also takes satellite platform (Landsat 5, 7 8) address inter-sensor differences. The was demonstrated classifying analysis ready data (ARD) acquired across one for three years (1985, 2006 2018) Conterminous United States (CONUS) with both spatial availability. 20,047 training 4949 evaluation 30-m pixel were where each annotated seven classes year. compared 16-day percentiles 1D convolution neural network (CNN) method. Results showed trained samples had 1.4–1.5% higher overall accuracies less computation than composites 2.3–2.4% percentiles. advantages pronounced developed (0.05 F1-score) cropland (0.02 mixed boundary pixels. This reasonable only on average 7.02, 16.49 15.78 good quality years, respectively, contrast 7.89, 27.72, 26.60 raw series. CNN not simply filling positions no zeros while masking mechanism rule out their contribution. take coordinates DEM which further increased 1.1–2.6% achieved 84.33%, 87.54% 87.01% 1985, 2018 classifications, respectively. maps shown consistent USGS Land Change Monitoring, Assessment, Projection (LCMAP) maps. codes, made publicly available.

Language: Английский

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Deep artificial intelligence applications for natural disaster management systems: A methodological review

Ecological Indicators, Journal Year: 2024, Volume and Issue: 163, P. 112067 - 112067

Published: May 6, 2024

Deep learning techniques through semantic segmentation networks have been widely used for natural disaster analysis and response. The underlying base of these implementations relies on convolutional neural (CNNs) that can accurately precisely identify locate the respective areas interest within satellite imagery or other forms remote sensing data, thereby assisting in evaluation, rescue planning, restoration endeavours. Most CNN-based deep-learning models encounter challenges related to loss spatial information insufficient feature representation. This issue be attributed their suboptimal design layers capture multiscale-context failure include optimal during pooling procedures. In early CNNs, network encodes elementary representations, such as edges corners, whereas, progresses toward later layers, it more intricate characteristics, complicated geometric shapes. theory, is advantageous a extract features from several levels because generally yield improved results when both simple maps are employed together. study comprehensively reviews current developments deep methodologies segment images associated with disasters. Several popular models, SegNet U-Net, FCNs, FCDenseNet, PSPNet, HRNet, DeepLab, exhibited notable achievements various applications, including forest fire delineation, flood mapping, earthquake damage assessment. These demonstrate high level efficacy distinguishing between different land cover types, detecting infrastructure has compromised damaged, identifying regions fire-susceptible further dangers.

Language: Английский

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Underestimated nutrient from aquaculture ponds to Lake Eutrophication: A case study on Taihu Lake Basin

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130749 - 130749

Published: Jan. 25, 2024

Language: Английский

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Land use and land cover changes in Morocco: trends, research gaps, and perspectives

GeoJournal, Journal Year: 2025, Volume and Issue: 90(1)

Published: Feb. 12, 2025

Language: Английский

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Evaluation of Landsat image compositing algorithms

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 285, P. 113375 - 113375

Published: Dec. 6, 2022

Language: Английский

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Estimating and mapping forest age across Canada's forested ecosystems

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 290, P. 113529 - 113529

Published: March 15, 2023

Forest age is an important variable for assessments of biodiversity and habitat, sustainable forest land management, as well carbon science modeling. Tree stand are typically measured directly on site, or estimated through visual photo interpretation, with spatially explicit maps not often produced over large areas. Remote sensing enables the generation wall-to wall, disturbance events within satellite record; however, relatively rare landscape in a given year, additional means determining required. As reviewed herein, estimation using optical Earth observation data challenging due to limited spectral link attribute interest, especially forests get older. The temporally dictated multi-method approach outlined herein acknowledges these limitations, by applying that best suited quality information available, depending epoch interest. In this research, we combine three approaches estimate at 30-m spatial resolution Landsat data. first uses change detection protocols detect from 1985 2019, time since used proxy age. second surface reflectance composites identify pixels exhibiting evidence recovery occurred twenty years prior 1985, allowing extension estimates 1965. Finally, understanding linkage between canopy height, inverted allometric equations coupled structure productivity metrics model those show no maximum 150 years, acknowledging uncertainty increases increasing Combining approaches, made every treed pixel found 650 Mha forested ecosystems Canada. Nationwide, mean ≤150 old (representing 94.1% area) was 70 (standard deviation = 32.1 years). For confidence building, were compared reported National Inventory (NFI) both aspatially. Nationally, 5.9% area be older than while 9.5% NFI sample recorded years. median 68 73 regional variability matching expectations related regimes productivity. Spatially provide can inform wide range policy, science, management needs.

Language: Английский

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Brazilian Amazon indigenous territories under deforestation pressure

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: April 10, 2023

Abstract Studies showed that Brazilian Amazon indigenous territories (ITs) are efficient models for preserving forests by reducing deforestation, fires, and related carbon emissions. Considering the importance of ITs conserving socio-environmental cultural diversity recent climb in we used official remote sensing datasets to analyze deforestation inside outside within Brazil's biome during 2013–2021 period. Deforestation has increased 129% since 2013, followed an increase illegal mining areas. In 2019–2021, was 195% higher 30% farther from borders towards interior than previous years (2013–2018). Furthermore, about 59% dioxide (CO 2 ) emissions (96 million tons) occurred last three analyzed years, revealing magnitude increasing climate impacts. Therefore, curbing must be a priority government secure these peoples' land rights, ensure forests' protection regulate global climate.

Language: Английский

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