Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(18), P. 3062 - 3062

Published: Sept. 18, 2020

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use cover (LULC) provide a new perspective in remote sensing analysis. Jointly, these sources permit researchers to improve operational classification change detection, guiding better reasoning landscape intrinsic processes, as deforestation agricultural expansion. However, the results their have not yet been synthesized order coherent guidance on effect different well identify promising approaches issues which affect performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, future possibilities L8/OLI S2/MSI LULC mapping detection. particular, highlight possibility using medium-resolution (Landsat-like, 10–30 m) time series multispectral optical provided by harmonization between sensors cube architectures analysis-ready that are permeated publicizations, open policies, science principles. We also reinforce potential exploring more spectral bands combinations, especially three Red-edge two Near Infrared Shortwave S2/MSI, calculate vegetation indices sensitive phenological variations were less frequently applied long time, but turned since mission. Summarizing peer-reviewed papers can guide scientific community data, enable detailed knowledge detection landscapes, natural scenarios.

Language: Английский

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Differentiable modelling to unify machine learning and physical models for geosciences

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(8), P. 552 - 567

Published: July 11, 2023

Language: Английский

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Where Are Global Vegetation Greening and Browning Trends Significant?

Geophysical Research Letters, Journal Year: 2021, Volume and Issue: 48(6)

Published: Feb. 5, 2021

Abstract Global greening trends have been widely reported based on long‐term remote sensing data of terrestrial ecosystems. Typically, a hypothesis test is performed for each grid cell; this leads to multiple testing and false positive trend detection. We reanalyze global account issue with novel statistical method that allows robust inference regions. Based leaf area index (LAI) data, our methods reduce the detected from 35.2% 15.3% land surface; reduction most notable in nonwoody vegetation. Our results confirm several regions (China, India, Europe, Sahel, North America, Brazil, Siberia), are also supported by independent products. report evidence an increasing seasonal amplitude LAI north 35°N. Considering widespread use spatially replicated tests change research, we recommend adopting proposed procedure control outcomes.

Language: Английский

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Limited climate change mitigation potential through forestation of the vast dryland regions

Science, Journal Year: 2022, Volume and Issue: 377(6613), P. 1436 - 1439

Published: Sept. 22, 2022

Forestation of the vast global drylands has been considered a promising climate change mitigation strategy. However, its actual climatic benefits are uncertain because forests' reduced albedo can produce large warming effects. Using high-resolution spatial analysis drylands, we found 448 million hectares suitable for afforestation. This area's carbon sequestration potential until 2100 is 32.3 billion tons (Gt C), but 22.6 Gt C that required to balance The net equivalent would offset ~1% projected medium-emissions and business-as-usual scenarios over same period. Focusing forestation only on areas with cooling effects use half area double emissions offset. Although such smart clearly important, limited reinforce need reduce rapidly.

Language: Английский

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A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: April 8, 2023

Abstract Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led a high demand for catalogues and tools their computation. However, most these resources either closed-source, outdated, unconnected catalogue or lacking common Application Programming Interface (API). Here we present “Awesome Indices” (ASI), standardized research. ASI provides comprehensive machine readable indices, which is linked Python library. delivers broad set attributes each index, including names, formulas, source references. can be extended by the user community, ensuring that remains current enabling wider range scientific applications. Furthermore, library enables application real-world data thereby facilitates efficient use in multiple domains.

Language: Английский

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Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities

IEEE Geoscience and Remote Sensing Magazine, Journal Year: 2022, Volume and Issue: 10(2), P. 172 - 200

Published: Jan. 14, 2022

The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments a plethora applications are already changing way humanity will face challenges our planet. This article reviews current DL approaches for EO data, along with their toward monitoring achieving SDGs most impacted by rapid development in EO. We systematically review case studies achieve zero hunger, create sustainable cities, deliver tenure security, mitigate adapt climate change, preserve biodiversity. Important societal, economic, environmental implications covered. Exciting times coming when algorithms data can help endeavor address crisis more development.

Language: Английский

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CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: Dec. 24, 2022

Abstract Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase necessity to improve cloud detection methods for imagery acquired by Sentinel-2 satellites. However, lack of consensus transparency existing reference datasets hampers benchmarking current methods. Exploiting analysis-ready data offered Copernicus program, we created CloudSEN12, new multi-temporal global dataset foster research shadow detection. CloudSEN12 has 49,400 image patches, including (1) level-1C level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar (3) auxiliary remote sensing products, (4) different hand-crafted annotations label presence thick thin shadows, (5) results from eight state-of-the-art algorithms. At present, exceeds all previous efforts terms annotation richness, scene variability, geographic distribution, metadata complexity, quality control, number samples.

Language: Английский

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Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation

PeerJ, Journal Year: 2023, Volume and Issue: 11, P. e15593 - e15593

Published: June 23, 2023

The global potential distribution of biomes (natural vegetation) was modelled using 8,959 training points from the BIOME 6000 dataset and a stack 72 environmental covariates representing terrain current climatic conditions based on historical long term averages (1979-2013). An ensemble machine learning model stacked regularization used, with multinomial logistic regression as meta-learner spatial blocking (100 km) to deal autocorrelation points. Results cross-validation for classes show an overall accuracy 0.67 R2logloss 0.61, "tropical evergreen broadleaf forest" being class highest gain in predictive performances (R2logloss = 0.74) "prostrate dwarf shrub tundra" lowest -0.09) compared baseline. Temperature-related were most important predictors, mean diurnal range (BIO2) shared by all base-learners (i.e.,random forest, gradient boosted trees generalized linear models). next used predict future periods 2040-2060 2061-2080 under three climate change scenarios (RCP 2.6, 4.5 8.5). Comparisons predictions epochs (present, 2061-2080) that increasing aridity higher temperatures will likely result significant shifts natural vegetation tropical area (shifts forests savannas up 1.7 ×105 km2 2080) around Arctic Circle tundra boreal 2.4 2080). Projected maps at 1 km resolution are provided probability hard IUCN (six aggregated classes). Uncertainty (prediction error) also should be careful interpretation projections.

Language: Английский

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Biodiversity and Climate Extremes: Known Interactions and Research Gaps

Earth s Future, Journal Year: 2024, Volume and Issue: 12(6)

Published: June 1, 2024

Abstract Climate extremes are on the rise. Impacts of extreme climate and weather events ecosystem services ultimately human well‐being can be partially attenuated by organismic, structural, functional diversity affected land surface. However, ongoing transformation terrestrial ecosystems through intensified exploitation management may put this buffering capacity at risk. Here, we summarize evidence that reductions in biodiversity destabilize functioning facing extremes. We then explore if impaired could, turn, exacerbate argue only a comprehensive approach, incorporating both ecological hydrometeorological perspectives, enables us to understand predict entire feedback system between altered This ambition, however, requires reformulation current research priorities emphasize bidirectional effects link ecology atmospheric processes.

Language: Английский

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Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

Information Fusion, Journal Year: 2020, Volume and Issue: 63, P. 256 - 272

Published: July 9, 2020

Language: Английский

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