scholarly journals Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)

2021 ◽  
Vol 13 (8) ◽  
pp. 1530
Author(s):  
Valentina Olmo ◽  
Enrico Tordoni ◽  
Francesco Petruzzellis ◽  
Giovanni Bacaro ◽  
Alfredo Altobelli
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Standard Procedure ◽  
Abies Alba ◽  
Recovery Process ◽  
Short Wave ◽  
Short Period ◽  
North Eastern ◽  
Friuli Venezia Giulia ◽  
Sentinel 2

On the 29th of October 2018, a storm named “Vaia” hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the necessity of a standard procedure for windthrow detection and monitoring based on satellite data as an alternative to foresters’ fieldwork. The proposed methodology was applied in Carnic Alps (Friuli Venezia Giulia, NE Italy) in natural stands dominated by Picea abies and Abies alba. We used images from the Sentinel-2 mission: 1) to test vegetation indices performance in monitoring the vegetation dynamics in the short period after the storm, and 2) to create a windthrow map for the whole Friuli Venezia Giulia region. Results showed that windthrows in forests have a significant influence on visible and short-wave infrared (SWIR) spectral bands of Sentinel-2, both in the short and the long-term timeframes. NDWI8A and NDWI were the best indices for windthrow detection (R2 = 0.80 and 0.77, respectively) and NDVI, PSRI, SAVI and GNDVI had an overall good performance in spotting wind-damaged areas (R2 = 0.60–0.76). Moreover, these indices allowed to monitor post-Vaia forest die-off and showed a dynamic recovery process in cleaned sites. The NDWI8A index, employed in the vegetation index differencing (VID) change detection technique, delimited damaged areas comparable to the estimations provided by Regional Forest System (2545 ha and 3183 ha, respectively). Damaged forests detected by NDWI8A VID ranged from 500 m to 1500 m a.s.l., mainly covering steep slopes in the south and east aspects (42% and 25%, respectively). Our results suggested that the NDWI8A VID method may be a cost-effective and accurate way to produce windthrow maps, which could limit the risks associated with fieldwork and may provide a valuable tool to plan tree removal interventions in a more efficient way.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Cost Effective ◽  
Spatiotemporal Analysis ◽  
Short Wave ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Data Volume ◽  
Sentinel 2

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

scholarly journals Using satellite images to assess the state of arable fields on the example of the East Kazakhstan region

2021 ◽  
Vol 82 (4) ◽  
pp. 179-186
Author(s):  
A. S. Tlebaldinova ◽  
◽  
Ye. V. Ponkina ◽  
M. Ye. Mansurova ◽  
S. Sh. Ixanov ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Satellite Data ◽  
Satellite Images ◽  
Vegetation Index ◽  
The State ◽  
Experimental Station ◽  
Arable Fields ◽  
East Kazakhstan ◽  
Expert Analysis ◽  
Sentinel 2

This article proposes a methodology for assessing the state of arable fields based on the use of Sentinel 2 satellite data. The essence of this methodology is cluster analysis of NDVI vegetation index profiles for a number of years, as well as expert analysis of the obtained results. The proposed method for assessing the state of arable fields has been tested on the example of arable lands of East Kazakhstan Agricultural Experimental Station LLP. This method can be used to optimize crop placement.

SiDroForest  Siberian Drone-mapped Forest inventory

2021 ◽  
Author(s):  
Femke van Geffen ◽  
Birgit Heim ◽  
Ulrike Herzschuh ◽  
Luidmila Pestryakova ◽  
Evgenii Zakharov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Forest Structure ◽  
Vegetation Index ◽  
Point Clouds ◽  
Training Data ◽  
Data Repository ◽  
Surface Model ◽  
Marine Research ◽  
Sentinel 2

<p>To gain a better understanding of global carbon storage and albedo feedback mechanisms it is important to have insights into high latitude vegetation change. Boreal forest compositions are changing in response to changes in climate, which in turn can lead to feedbacks in regional and global climate through altered carbon cycles and albedo dynamics. Circumpolar boreal forests represent close to 30% of all forested area on the planet, between 900 and 1,200 million ha. These forests are located primarily in Alaska, Canada, and Russia. Due to the remote location of these forests and the short seasons without snow, data collected on the boreal vegetation is limited. </p><p>The proposed dataset is an attempt to remedy data scarcity whilst providing adjusted data for machine learning practices.We present a dataset containing diverse formats of forest structure information that covers two important vegetation transition zones in Siberia: the Evergreen - Summergreen transition zone in Central Yakutia and the northern treeline in Chukotka (NE Siberia).</p><p>This dataset contains data from the locations covered by fieldwork was performed by the Alfred Wegener Institute for Polar and Marine research, (AWI) and the North-Eastern Federal University of Yakutsk​ (NEFU). The fieldwork upscaled through the addition of Red Green Blue(RGB) UAV (Unmanned Aerial Vehicle) camera data and Sentinel-2 satellite data cropped to a 5 km radius around the fieldwork sites. The dataset is created with the aim of providing ground truth validation and training data to be used in various vegetation related machine learning tasks .</p><p>The dataset contains:</p><p>1.Labelled individual trees per 30x30 m plot assigned in field work with additional data on species, height, crown width, and biomass.</p><p>2.Structure from Motion (SfM)point clouds that provide 3D information about the forest structure, included generated Canopy Height Model (CHM), Digital Elevation Model (DEM) and a Digital Surface Model (DSM) per 50x50 m.</p><p>3.Multispectral Sentinel-2 satellite data (10 m ) cropped to a 5km radius with generated a NDVI(normalized difference vegetation index), available in three seasons: Early Summer, Peak Summer and Late Summer.</p><p>4.Extracted tree crowns with species information and a synthetically generated large (10.000 samples) dataset for training machine leaning algorithms.</p><p>The dataset will be made publicly available on the data repository PANGAEA.</p>

scholarly journals Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

2020 ◽  
Vol 12 (12) ◽  
pp. 1914 ◽  
Author(s):  
Josef Lastovicka ◽  
Pavel Svec ◽  
Daniel Paluba ◽  
Natalia Kobliuk ◽  
Jan Svoboda ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Satellite Data ◽  
National Park ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Forest Vegetation ◽  
Forest Recovery ◽  
Vegetation Changes ◽  
Tasseled Cap ◽  
Sentinel 2

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

scholarly journals Land cover monitoring as part of a survey on wetland ecosystem conservation in the Negovan village area using remote sensing tools

2019 ◽  
pp. 175-188
Author(s):  
Kameliya Radeva ◽  
Emiliya Velizarova ◽  
Adlin Dancheva
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Satellite Data ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Vegetation Period ◽  
Wetland Ecosystem ◽  
Temporal Space ◽  
Temporal Intervals ◽  
Sentinel 2

The main purpose of the present survey is to apply remote sensing data to the investigation of different components of a wetland ecosystem, situated in the area of the village of Negovan (Sofia region), such as soil, vegetation and water, and their variation for certain temporal intervals including the vegetation period. This survey represents the process of interim ecological monitoring (IEM) implementation on the studied ecosystem. Data for the current condition of different ecosystem components - soil, vegetation and water components, and their variations within the selected time period of 5 years (2014-2018) have been obtained. Specific relations among wetland actual components conditions such as soil wetness and vegetation vs climate factors within the respective temporal intervals of wetland monitoring process have been established. Aerospace data with different temporal, space and spectral resolution, satellite data from Sentinel 2, MSI and aerophoto with a very high resolution have been used. The results for ?Brightness?, ?Greenness? and ?Wetness? components obtained on the basis of orthogonalization of satellite data from Sentinel 2 have been introduced. The results reflect the value of Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI 2), Normalized Difference Greenness Index (NDGI) and Normalized Difference Water Index (NDWI), which are of great importance for the relationship between soil health indexes and ecosystem sustainability. Thematic maps are generated based on the results obtained by surveying land cover components. Data received for the current condition of Negovan wetland ecosystem and established variations of different parameters, including soil component could be used while assessing wetland ecosystem services.

scholarly journals Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1869
Author(s):  
Yangyang Zhang ◽  
Jian Yang ◽  
Lin Du
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Hyperspectral Data ◽  
Quantitative Detection ◽  
Inversion Method ◽  
Vegetation Monitoring ◽  
Area Index ◽  
Band Combination ◽  
Band Combinations ◽  
Sentinel 2

Leaf area index (LAI) is a key biophysical variable to characterize vegetation canopy. Accurate and quantitative LAI estimation is significant for monitoring vegetation growth status. ZhuHai-1 (ZH-1), which is a commercial remote sensing micro-nano satellite, provides a possibility for quantitative detection of vegetation with high spatial and spectral resolution. However, the band characteristics of ZH-1 are closely related to the accuracy of vegetation monitoring. In this study, a simulation dataset containing 32 bands of ZH-1 was generated by using the PROSAIL model, which was used to analyze the performance of 32 bands for LAI estimation by using the hybrid inversion method. Meanwhile, the effect of different band combinations on LAI estimation was discussed based on sensitivity analysis and the correlation between bands. Then, the optimal band combination from ZH-1 hyperspectral satellite data for LAI estimation was obtained. LAI estimation was performed based on the selected optimal band combination of ZH-1 satellite images in Xiantao city, Hubei province, and compared with the Sentinel-2 normalized difference vegetation index (NDVI) values and LAI product. The results demonstrated that the obtained LAI map based on the optimal band combination of ZH-1 was generally consistent with the overall distribution of Sentinel-2 NDVI and the LAI product, but had a moderate correlation with Sentinel-2 LAI (R = 0.60), which may not favorably indicate the validity of indirect validation. However, the method of this study on the analysis of hyperspectral data bands has application potential to provide a reference for selecting appropriate bands of hyperspectral satellite data to estimate LAI and improve the application of hyperspectral data such as ZH-1 in vegetation monitoring.

scholarly journals DURANKULAK LAKE ACTUAL STATE AND MONITORING USING SENTINEL 2 SATELLITE DATA

2019 ◽  
pp. 53-58
Author(s):  
Iva Ivanova ◽  
Iliyana Gigova ◽  
Temenuzhka Spassova ◽  
Nataliya Stankova
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Orthogonal Transformation ◽  
Transformation Model ◽  
Actual State ◽  
The European Union ◽  
Ramsar Convention ◽  
Natural Habitats ◽  
Sentinel 2

Durankulak Lake is one of the most important wetlands in Bulgaria and Europe. It is included in the Ramsar Convention and is recognized as an important bird area of world importance. The subject of protection within the protected zone is the condition of the natural habitats and the habitats of the species, including the natural species composition, the typical species and the conditions of the environment. Remote sensing methods provide opportunities for characterization and monitoring of the wetland on various scales that have not been done so far. In the present study satellite multispectral images from the European Union Copernicus Satellite Program, Sentinel 2 are used for assessment and monitoring of the actual state of the lake. Based on these satellite images, the boundaries of the protected wetland are derived. An index classification of the wetland was made. Normalized Difference Vegetation Index (NDVI) is used to classify sites within the protected area. Sentinel-2 satellite data to implement the orthogonal transformation model called Tasseled Cap Transformation (TCT) has also been used. The model is an effective method for classifying and analyzing of the processes related to the dynamics of changes, affecting the main components of the earth's surface: soil, water and vegetation. The spring survey of 2019 was selected for the present study. The results will show successful mapping and monitoring of the wetland, which will give a real idea of the state of the Durankulak Lake and the need to take conservation measures to protect it. Key words: monitoring, satellite data, wetlands, habitats

scholarly journals A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery

2019 ◽  
Vol 11 (17) ◽  
pp. 2043 ◽  
Author(s):  
Jia ◽  
Wang ◽  
Wang ◽  
Mao ◽  
Zhang
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Vegetation Indices ◽  
Short Wave ◽  
Mangrove Forests ◽  
Red Edge ◽  
Jensen Shannon Divergence ◽  
Sentinel 2

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.

scholarly journals A Multi Sensor Approach to Forest Type Mapping for Advancing Monitoring of Sustainable Development Goals (SDG) in Myanmar

2020 ◽  
Vol 12 (19) ◽  
pp. 3220
Author(s):  
Sumalika Biswas ◽  
Qiongyu Huang ◽  
Anupam Anand ◽  
Myat Su Mon ◽  
Franz-Eugen Arnold ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Satellite Data ◽  
Classification System ◽  
Sustainable Development Goals ◽  
Near Infrared ◽  
Short Wave ◽  
Landsat 8 ◽  
Forest Types ◽  
Development Goals ◽  
Sentinel 2

Monitoring forests is important for measuring overall success of the 2030 Agenda because forests play an essential role in meeting many Sustainable Development Goals (SDG), especially SDG 15. Our study evaluates the contribution of three satellite data sources (Landsat-8, Sentinel-2 and Sentinel-1) for mapping diverse forest types in Myanmar. This assessment is especially important because Myanmar is currently revising its classification system for forests and it is critical that these new forest types can be accurately mapped and monitored over time using satellite imagery. Our results show that using a combination of Sentinel-1 and Sentinel-2 yields the highest accuracy (89.6% ± 0.16 percentage point(pp)), followed by Sentinel-2 alone (87.97% ± 0.11 pp) and Landsat-8 (82.68% ± 0.13 pp). The higher spatial resolution of Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands enhances accuracy by 4.83% compared to Landsat-8. The addition of the Sentinel-2 Near Infrared and three Vegetation Red Edge bands further improve accuracy by 0.46% compared to using only Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands. Adding the radar information from Sentinel-1 further increases the accuracy by 1.63%. We were able to map the two major forest types, Upper Moist and Upper Dry Mixed Deciduous Forest, which comprise 90% of our study area. Accuracies for these forest types ranged from 77 to 96% depending on the sensors used, demonstrating the feasibility of using satellite data to map forest categories from a newly revised classification system. Our results advance the ongoing development of the National Forest Monitoring System (NFMS) by the Myanmar Forest Department and United Nations-Food and Agriculture Organization (UN-FAO) and facilitates future monitoring of progress towards the SDGs.

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