An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(8), P. 1863 - 1863

Published: April 13, 2022

As satellite observation technology develops and the number of Earth (EO) satellites increases, observations have become essential to developments in understanding its environment. However, current impacts remote sensing community different EO data possible future trends applications not been systematically examined. In this paper, we review use based on an analysis from 15 whose are widely used. Articles that reference missions included Web Science core collection for 2020 were analyzed using scientometric meta-analysis. We found following: (1) publications citations referencing is increasing exponentially; however, articles AVHRR, SPOT, TerraSAR tending decrease; (2) papers related concentrated a small journals: 43.79% reviewed published only 13 journals; (3) impact factor (RSIF), new index, was constructed measure predict their data. Landsat, Sentinel, MODIS, Gaofen, WorldView be most significant MODIS widest range applications. Over next five years (2021–2025), it expected Sentinel will mission with greatest influence.

Language: Английский

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A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(3), P. 562 - 562

Published: Jan. 25, 2022

Central Europe was hit by several unusually strong periods of drought and heat between 2018 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast areas Germany. We present the first assessment canopy cover loss Germany for period January 2018–April 2021. Our approach makes use dense Sentinel-2 Landsat-8 time-series data. computed disturbance index (DI) from tasseled cap components brightness, greenness, wetness. Using quantiles, we generated monthly DI composites calculated anomalies a reference (2017). From resulting map, statistics administrative entities. results show 501,000 ha Germany, large regional differences. The losses largest central reached up two-thirds coniferous some districts. map has high spatial (10 m) temporal (monthly) resolution can be updated at any time.

Language: Английский

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National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 304, P. 114069 - 114069

Published: Feb. 24, 2024

Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation the assessment of ecosystem services. In many countries, attributes are monitored regularly through sample-based inventories. combination with satellite imagery, data from such inventories have a great potential developing large-area maps. Here, high temporal resolution Sentinel-1 Sentinel-2 has been useful extracting vegetation phenology, that may also be valuable improving mapping. The objective this study was to map main in Germany using combined time series, identify address challenges related use National Forest Inventory (NFI) remote sensing applications. We generated cloud free series 5-day intervals imagery combine those monthly backscatter composites. Further, we incorporate topography, meteorology, climate account environmental gradients. To NFI training machine learning models, following challenges: 1) link pixels variable radius plots, which precise area unknown, 2) efficiently utilize mixed-species plots model validation. past, accuracies pixel-level maps were often estimated solely homogeneous pure-species stands. study, assess how well generalize mixed plot conditions. Our results show mapping large, environmentally diverse landscapes. Classification accuracy pure stands ranged between 72% 97% (F1-score) five dominant species, while less frequent remained challenging. When including assessment, decreased by 4–14 percentage points most groups. highlights importance mixed-forest when validating Based these results, discuss potentials remaining at national level. findings allow further improve national-level medium provide guidance similar approaches other countries where ground-based inventory available.

Language: Английский

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Mapping Dominant Tree Species of German Forests

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(14), P. 3330 - 3330

Published: July 11, 2022

The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making site-adapted selection. An accurate assignment in relation to their location allows conclusions about potential resilience or vulnerability biotic abiotic factors. Identifying areas risk helps the long-term strategy conversion towards natural, diverse, climate-resilient forest. In framework inventory (NFI) Germany, data on are collected sample plots, but there is lack full coverage map distribution. NFI were used train test machine-learning approach that classifies dense Sentinel-2 time series with result dominant German forests seven main classes. model’s accuracy type classification showed weighted average F1-score deciduous (Beech, Oak, Larch, Other Broadleaf) between 0.77 0.91 non-deciduous (Spruce, Pine, Douglas fir) 0.85 0.94. Two additional plausibility checks independent stand inventories statistics from show conclusive agreement. results provided public via web-based interactive map, order initiate broad discussion limitations satellite-supported management.

Language: Английский

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TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

Earth system science data, Journal Year: 2023, Volume and Issue: 15(2), P. 681 - 695

Published: Feb. 8, 2023

Abstract. Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated benefits of multiple sensors automated tree classification. However, transferable deep learning approaches applications still lacking. This gap motivated us to create a novel dataset classification in central Europe based multi-sensor from aerial, Sentinel-1 Sentinel-2 imagery. In this paper, we introduce TreeSatAI Benchmark Archive, which contains labels 20 European (i.e., 15 genera) derived administration federal state Lower Saxony, Germany. We propose models guidelines application latest machine techniques task with multi-label data. Finally, provide benchmark experiments showcasing information can be different including artificial neural networks tree-based methods. found that residual (ResNet) perform sufficiently well weighted precision scores up 79 % only by using RGB bands aerial result indicates spatial content present within 0.2 m resolution very informative With incorporation imagery, performance improved marginally. sole use allows 74 either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since real-world reference data, it high class imbalances. attribute negatively affects models' performances many underrepresented classes scarce species). class-wise best-performing late fusion model reached values ranging 54 (Acer) 88 (Pinus). Based our results, conclude imagery could considerably support forestry provision maps at plan challenges driven global environmental change. The original used paper shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation dataset, refer article.

Language: Английский

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Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: June 23, 2023

Bark beetle infestations are among the most substantial forest disturbance agents worldwide. Moreover, as a consequence of global climate change, they have increased in frequency and size number affected areas. Controlling bark outbreaks requires consistent operational monitoring, is possible using satellite data. However, while many satellite-based approaches been developed, full potential dense, multi-sensor time series has yet to be fully explored. Here, for first time, we used all available multispectral data from Landsat Sentinel-2, Sentinel-1 SAR data, combinations thereof detect Bavarian Forest National Park. Based on multi-year reference dataset annual infested areas, assessed separability between healthy forests various vegetation indices calculated We two compute infestation probability different datasets: Bayesian conditional probabilities, based best-separating index each type, random regression, type. Five sensor configurations were tested their detection capabilities: alone, Sentinel-2 combined, types combined. The best overall results terms spatial accuracy achieved with (max. accuracy: 0.93). detections also closest onset estimated year. detected areas larger contiguous patches higher reliability compared smaller patches. somewhat inferior those 0.89). While yielding similar results, combination did not provide any advantages over or alone 0.87), was unable 0.62). combined three achieve satisfactory either 0.67). Spatial accuracies typically probabilities than forest-derived but latter resulted earlier detections. approach presented herein provides flexible pipeline well-suited monitoring outbreaks. Furthermore, it can applied other types.

Language: Английский

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Daytime cooling efficiencies of urban trees derived from land surface temperature are much higher than those for air temperature

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(4), P. 044037 - 044037

Published: March 6, 2024

Abstract Accurately capturing the impact of urban trees on temperature can help optimize heat mitigation strategies. Recently, there has been widespread use remotely sensed land surface ( T s ) to quantify cooling efficiency (CE) trees. However, reflects emitted radiation from an object seen point view thermal sensor, which is not a good proxy for air perceived by humans. The extent CEs derived reflect true experiences residents debatable. Therefore, this study systematically compared -based CE (CE with in 392 European clusters. and were defined as reductions , respectively, every 1% increase fractional tree cover (FTC). results show that FTC substantial reducing most cities during daytime. at night, response increased appears be much weaker ambiguous. On average, cities, daytime reaches 0.075 °C % −1 significantly higher (by order magnitude) than corresponding 0.006 . In contrast, average nighttime are similar, both approximating zero. Overall, lower temperatures, but magnitude their effect notably amplified when using estimates situ measurements, important consider accurately constraining public health benefits. Our findings provide critical insights into realistic efficiencies alleviating through planting.

Language: Английский

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Remote sensing forest health assessment – a comprehensive literature review on a European level

Central European Forestry Journal, Journal Year: 2025, Volume and Issue: 71(1), P. 14 - 39

Published: Feb. 1, 2025

Abstract Forest health assessments (FHA) have been carried out at European level since the 1980s in order to identify forest damage. The annual surveys are usually conducted without use of remote sensing tools. However, increasing availability observations potentially allows conduct FHA more wide-spread, often, or comprehensive and comparable way. This literature review systematically evaluated 110 studies from 2015 2022 that for Europe. purpose was determine (1) which tree species were studied; (2) what types damage evaluated; (3) whether levels distinguished according standard International Co-operative Program on Assessment Monitoring Air Pollution Effects Forests (ICP-Forest); (4) automation; (5) findings applicable a systematic FHA. results show spruce is most studied species. Damage caused by bark beetles drought predominantly studied. In only 2 classified. Only four able perform identifying individual trees, classifying their levels. None investigated suitability approach assessments. result surprising programs such as SEMEFOR analyzed potential already 1990s. We conclude new satellite systems advances artificial intelligence machine learning should be translated into practice ICP standards.

Language: Английский

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Application of Remote Sensing Data for Locust Research and Management—A Review

Insects, Journal Year: 2021, Volume and Issue: 12(3), P. 233 - 233

Published: March 9, 2021

Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation caused massive damage endangering food security. Unusual heavy rainfalls in habitats of desert (Schistocerca gregaria) lack monitoring due to political conflicts or inaccessibility those lead swarms migrating over Arabian Peninsula, East Africa, India Pakistan. At same time, Moroccan (Dociostaurus maroccanus) some Central Asian countries Italian (Calliptamus italicus) Russia China crops despite developed ongoing control measurements. These recent events underline that risk by pests is as present ever affects 100 million human lives technical progress monitoring, prediction approaches. Remote sensing has become one most important data sources management. Since 1980s, remote applications accompanied many management activities contributed an improved more effective plagues. open-access archives well cloud computing provide unprecedented opportunity for sensing-based research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects a faster control. Nevertheless, full capacity available possibilities not been exploited yet. This review paper provides comprehensive quantitative overview international research articles focusing on application We reviewed 110 published last four decades, categorized them into different aspects main topics summarize achievements gaps further development. The results reveal strong focus three species—the locust, migratory (Locusta migratoria), Australian plague (Chortoicetes terminifera)—and corresponding regions interest. There still studies other pest species such American piceifrons), South cancellata), brown (Locustana pardalina) red (Nomadacris septemfasciata). In terms applied sensors, utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) Landsat mainly land cover mapping. Application geomorphological metrics radar-based soil moisture comparably rare previous acknowledgement their importance outbreaks. Despite great advance usage resources, we identify several potential future improve understanding capacities use supporting outbreak-

Language: Английский

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Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Estimation over Broadleaf and Needleleaf Forests

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(6), P. 1522 - 1522

Published: March 10, 2023

The GEDI LiDAR system was specifically designed to detect vegetation structure and has proven be a suitable tool for estimating forest biophysical parameters, especially canopy height, at global scale. This study compares the relative height metric (RH100) over different types, deciduous broadleaf evergreen coniferous located in Thuringia, Germany, understand how structural differences affect estimation. A model that produced using digital terrain surface models (DTM DSM) derived from airborne laser scanning data is used as reference data. Based on result, needleleaf slightly more accurate (RMSE = 6.61 m) than 8.30 mixed 7.94 forest. Evaluation of acquisition parameters shows beam type, sensitivity, time do not significantly accuracy heights, forests. Considering foliage condition impacts estimation, contrasting result observed dataset acquired during winter when species shed their leaves (the so-called leaf-off dataset), outperforms leaf-on but less results effect plant area index (PAI) divided into two sets with low high PAI values threshold median 2. show (median < 2) corresponds non-growing season (autumn winter) better performance 7.40 compared growing 8.44 vice versa, 6.38 7.24 line leaf-off/leaf-on dataset. Although slight improvement estimation either or forest, approach filtering based such seasonal recommended retrieving pure stands species, sufficient available.

Language: Английский

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