scholarly journals Time-Series of Vegetation Indices (VNIR/SWIR) Derived from Sentinel-2 (A/B) to Assess Turgor Pressure in Kiwifruit

2020 ◽  
Vol 9 (11) ◽  
pp. 641
Author(s):  
Alberto Jopia ◽  
Francisco Zambrano ◽  
Waldo Pérez-Martínez ◽  
Paulina Vidal-Páez ◽  
Julio Molina ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Management Practices ◽  
Water Status ◽  
Vegetation Indices ◽  
Turgor Pressure ◽  
Security Risk ◽  
Vegetative Development ◽  
Water Sensor ◽  
Sentinel 2

For more than ten years, Central Chile has faced drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out on kiwifruit trees, located in the O’Higgins region, Chile for season 2018–2019 and 2019–2020. We evaluate the time-series of nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to establish how much variability in the canopy water status there was. Over the study’s site, eleven sensors were installed in five trees, which continuously measured the leaf’s turgor pressure (Yara Water-Sensor). A strong Spearman’s (ρ) correlation between turgor pressure and vegetation indices was obtained, having −0.88 with EVI and −0.81 with GVMI for season 2018–2019, and lower correlation for season 2019–2020, reaching −0.65 with Rededge1 and −0.66 with EVI. However, the NIR range’s indices were influenced by the vegetative development of the crop rather than its water status. The red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit’s water conditions and incorporate the sensitivity of different wavelengths.

scholarly journals Time-Series of Vegetation Indices (VNIR/SWIR) Derived from Sentinel-2 (A/B) to Assess Turgor Pressure in Kiwifruit

2020 ◽  
Author(s):  
Alberto Jopia ◽  
Francisco Zambrano ◽  
Waldo Pérez-Martínez ◽  
Paulina Vidal-Páez ◽  
Julio Molina ◽  
...  
Keyword(s):  
Crop Production ◽  
Management Practices ◽  
Water Status ◽  
Vegetation Indices ◽  
Turgor Pressure ◽  
Security Risk ◽  
Vegetative Development ◽  
Water Sensor ◽  
Use Efficiency ◽  
Sentinel 2

For more than ten years, Central Chile faces drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out in kiwifruit trees, located in the O’Higgins region, Chile; for season 2018-2019 and 2019-2020. We evaluate nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to know how much variability in the canopy water status could explain. Over the study's site were installed sensors that continuously measure the leaf's turgor pressure (Yara Water-Sensor). A strong correlation between turgor pressure and vegetation indices was obtained with the Spearman's rho coefficient ($\rho$). However, the NIR range's indices were influenced by the vegetative development of the crop rather than its water status. Red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit's water conditions and incorporate the sensitivity of different wavelengths.

scholarly journals Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

2021 ◽  
Vol 13 (9) ◽  
pp. 1837
Author(s):  
Eve Laroche-Pinel ◽  
Sylvie Duthoit ◽  
Mohanad Albughdadi ◽  
Anne D. Costard ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Potential ◽  
Large Scale ◽  
Water Status ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Severe Drought ◽  
Stem Water Potential ◽  
Stem Water ◽  
Sentinel 2

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).

scholarly journals Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 820 ◽  
Author(s):  
Haifeng Tian ◽  
Ni Huang ◽  
Zheng Niu ◽  
Yuchu Qin ◽  
Jie Pei ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Windows ◽  
Vegetation Indices ◽  
Optical Images ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Winter Crop ◽  
Winter Crops ◽  
Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Improving Dryland Crop Simulation by Assimilating Soil Moisture and Vegetation Data

2020 ◽  
Author(s):  
Yang Lu ◽  
Justin Sheffield
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Management Practices ◽  
Water Productivity ◽  
Vegetation Indices ◽  
Screening Method ◽  
Model Performance ◽  
State Parameter ◽  
Aquacrop Model

<p>Global population is projected to keep increasing rapidly in the next 3 decades, particularly in dryland regions of the developing world, making it a global imperative to enhance crop production. However, improving current crop production in these regions is hampered by yield gaps due to poor soils, lack of irrigation and other management practices. Here we develop a crop modelling capability to help understand gaps, and apply to dryland regions where data for parametrizing and testing models is generally lacking. We present a data assimilation framework to improve simulation capability by assimilating in-situ soil moisture and vegetation data into the FAO AquaCrop model. AquaCrop is a water-driven model that simulates canopy growth, biomass and crop yield as a function of water productivity. The key strength of AquaCrop lies in the low requirement for input data thanks to its simple structure. A global sensitivity analysis is first performed using the Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method to identify the key influential parameters on the model outputs. We begin with state-only updates by assimilating different combinations of soil moisture and vegetation data (vegetation indices, biomass, etc.), and different filtering/smoothing assimilation strategies are tested. Based on the state-only assimilation results, we further evaluate the utility of joint state-parameter (augmented-states) assimilation in improving the model performance. The framework will eventually be extended to assimilate remote sensing estimates of soil moisture and vegetation data to overcome the lack of in-situ data more generally in dryland regions.</p>

scholarly journals POTENCIAL HÍDRICO FOLIAR E DESENVOLVIMENTO VEGETATIVO DO CAFEEIRO CONILON SOB DIFERENTES LÂMINAS DE IRRIGAÇÃO NA REGIÃO E CAMPOS DOS GOYTACAZES - RJ

Irriga ◽  
2021 ◽  
Vol 26 (1) ◽  
pp. 13-28
Author(s):  
Diego Albani Furlan ◽  
Elias Fernandes De Sousa ◽  
José Carlos Mendonça ◽  
Claudio Luiz Melo De Souza ◽  
Romildo Domingos Gottardo ◽  
...  
Keyword(s):  
Water Potential ◽  
Leaf Water Potential ◽  
Water Status ◽  
Turgor Pressure ◽  
Leaf Water ◽  
Vegetative Development ◽  
Crown Diameter ◽  
Biometric Parameters ◽  
The Moment ◽  
Water Supplementation

POTENCIAL HÍDRICO FOLIAR E DESENVOLVIMENTO VEGETATIVO DO CAFEEIRO CONILON SOB DIFERENTES LÂMINAS DE IRRIGAÇÃO NA REGIÃO E CAMPOS DOS GOYTACAZES - RJ     DIEGO ALBANI FURLAN1; ELIAS FERNANDES DE SOUSA2; JOSÉ CARLOS MENDONÇA3; CLAUDIO LUIZ MELO DE SOUZA4; ROMILDO DOMINGOS GOTTARDO 5 E RODOLLPHO ARTUR DE SOUSA LIMA6   1Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Laqmedo, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil, [email protected] 2Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Laqmedo, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil, [email protected]  3Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Laqmedo, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil, [email protected]  4Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Laqmedo, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil, [email protected]  5Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Laqmedo, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil, [email protected]  6Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Laqmedo, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil, [email protected]     1 RESUMO   Na atividade cafeeira, o Brasil se destaca como maior produtor mundial, porém verifica-se que a sua produtividade é afetada de forma negativa pela seca, o que torna a produção dependente de complementação hídrica. Este trabalho tem como objetivo determinar estresse hídrico e o desenvolvimento do café Conilon em diferentes lâminas de irrigação. O delineamento experimental foi constituído de blocos casualizados, com três repetições, distribuídos em cinco tratamentos, sendo estes as lâminas de água de 0, 25, 50, 100 e 125% da ET0­. Cada parcela foi constituída de seis plantas, sendo as duas primeiras plantas de cada bloco consideradas bordadura. O potencial hídrico foliar foi determinado pela  medição da pressão de turgescência da folha, utilizando a bomba de Scholander, em uma planta por bloco e por tratamento. A altura da planta, secção transversal do caule e diâmetro da copa foram avaliados em três plantas por bloco, utilizando régua e paquímetro graduados. Os valores para o potencial hídrico foliar realizado na antemanhã variaram ente –0,15 a -1,18 MPa e, ao meio dia, de -1,17 a -2,3 MPa. As lâminas de irrigação equivalentes a 100 e 125% da ET0 apresentaram maiores valores ao longo do desenvolvimento da cultura até o momento da avaliação.    Palavras-Chave: cafeeiro, bomba de Scholander, status hídrico, parâmetros biométricos.     FURLAN, D. A.; SOUSA, E.F.; MENDONÇA, J. C.; SOUZA, C. L. M.; GOTTARDO, R. D.  E LIMA, R. A. S. POTENTIAL LEAF WATER AND VEGETATIVE DEVELOPMENT OF COFFEE CONILON UNDER DIFFERENT IRRIGATION DEPTHS IN THE REGION OF CAMPOS DOS GOYTACAZES - RJ     2 ABSTRACT   In the coffee production, Brazil stands out as the world's largest producer, but its productivity is negatively affected by drought, which makes production dependent on water supplementation. This work aims to determine water stress and the development of Conilon coffee in different irrigation depths. The experimental design consisted of randomized blocks with three replicates, distributed in five treatments, the irrigation depths of 0, 25, 50, 100 and 125% of ET0 -. Each plot was constituted of six plants, being the first two plants of each block considered border. The leaf water potential was is determined by measuring leaf turgor pressure using the Scholander pump in a plant per block and by treatment. The plant height, stem cross-section and crown diameter were evaluated in three plants per block using a graduated ruler and pachymeter. The values ​​for leaf water potential performed in the morning ranged from -0.15 to -1.18 MPa and, for noon, from -1.17 to -2.3 MPa. The irrigation depths equivalent to 100 and 125% of the ET0 presented higher values ​​throughout the development of the culture until the moment of the evaluation.   Keywords: coffee, Scholander pump, water status, biometric parameters.

scholarly journals Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

2021 ◽  
Vol 13 (17) ◽  
pp. 3488
Author(s):  
Keren Goldberg ◽  
Ittai Herrmann ◽  
Uri Hochberg ◽  
Offer Rozenstein
Keyword(s):  
Time Series ◽  
Mean Shift ◽  
Vegetation Indices ◽  
Gradient Boosting ◽  
Support Vector ◽  
Object Based ◽  
Accuracy Measure ◽  
Extreme Gradient Boosting ◽  
Accuracy Measures ◽  
Sentinel 2

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.

scholarly journals INTEGRATION OF HUMAN PARTICIPATORY SENSING AND ARCHIVES OF REMOTE SENSING OBSERVATIONS FOR FIELD LEVEL CROP PHENOLOGY ESTIMATION

2018 ◽  
Vol XLII-4 ◽  
pp. 547-550
Author(s):  
S. A. Sawant ◽  
J. D. Mohite ◽  
S. Pappula
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Specific Information ◽  
Crop Phenology ◽  
Field Level ◽  
Sentinel 2

<p><strong>Abstract.</strong> The rise in global population has increased food and water demand thereby causing excessive pressure on existing resources. In developing countries with fragmented land holdings there exists constant pressure on available water and land resources. Obtaining field scale crop specific information is challenging task. Advent of open freely available multi-temporal remote sensing observations with improved radiometric resolution the possibilities for near real / real time applications has increased. In this study and an attempt has been made to establish operational model for field level crop growth monitoring using integrated approach of crowd sourcing and time series of remote sensing observations. The time series of Sentinel 2 (A and B) satellite has been used to estimate crop growth related components such as vegetation indices and crop growth stage and crop phenology. In initial stage high valued cereal crop Wheat has been selected. The field level information (i.e. 108 Wheat fields) collected using mobile based agro-advisory platform mKRISHI&amp;reg; has been used to extract time series of Sentinel 2 observations (44 scenes for year 2016 and 2018). The moving average has been used for filling gaps in the time series of vegetation indices. The BFAST and GreenBrown package in R were used for detecting breaks in vegetation index time series and estimating crop growth stages. Analysis shows that the estimated crop phenology parameters were in better agreement with the field observations. In future more crops from different agro-climatic conditions will be considered for providing field level crop management advisory.</p>

scholarly journals Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series

2020 ◽  
Vol 12 (8) ◽  
pp. 1298 ◽  
Author(s):  
Ewa Grabska ◽  
Paweł Hawryło ◽  
Jarosław Socha
Keyword(s):  
Time Series ◽  
Scots Pine ◽  
Vegetation Indices ◽  
Short Wave ◽  
Pine Forest ◽  
Forest Stands ◽  
Forest Change ◽  
Short Wave Infrared ◽  
Scots Pine Forest ◽  
Sentinel 2

Climate change and severe extreme events, i.e., changes in precipitation and higher drought frequency, have a large impact on forests. In Poland, particularly Norway spruce and Scots pine forest stands are exposed to disturbances and have, thus experienced changes in recent years. Considering that Scots pine stands cover approximately 58% of forests in Poland, mapping these areas with an early and timely detection of forest cover changes is important, e.g., for forest management decisions. A cost-efficient way of monitoring forest changes is the use of remote sensing data from the Sentinel-2 satellites. They monitor the Earth’s surface with a high temporal (2–3 days), spatial (10–20 m), and spectral resolution, and thus, enable effective monitoring of vegetation. In this study, we used the dense time series of Sentinel-2 data from the years 2015–2019, (49 images in total), to detect changes in coniferous forest stands dominated by Scots pine. The simple approach was developed to analyze the spectral trajectories of all pixels, which were previously assigned to the probable forest change mask between 2015 and 2019. The spectral trajectories were calculated using the selected Sentinel-2 bands (visible red, red-edge 1–3, near-infrared 1, and short-wave infrared 1–2) and selected vegetation indices (Normalized Difference Moisture Index, Tasseled Cap Wetness, Moisture Stress Index, and Normalized Burn Ratio). Based on these, we calculated the breakpoints to determine when the forest change occurred. Then, a map of forest changes was created, based on the breakpoint dates. An accuracy assessment was performed for each detected date class using 861 points for 46 classes (45 dates and one class representing no changes detected). The results of our study showed that the short-wave infrared 1 band was the most useful for discriminating Scots pine forest stand changes, with the best overall accuracy of 75%. The evaluated vegetation indices underperformed single bands in detecting forest change dates. The presented approach is straightforward and might be useful in operational forest monitoring.

scholarly journals Sen2Grass: A Cloud-Based Solution to Generate Field-Specific Grassland Information Derived from Sentinel-2 Imagery

2021 ◽  
Vol 3 (1) ◽  
pp. 118-137
Author(s):  
Tom Hardy ◽  
Lammert Kooistra ◽  
Marston Domingues Franceschini ◽  
Sebastiaan Richter ◽  
Erwin Vonk ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Management Practices ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Grassland Management ◽  
Time Range ◽  
Computing Platform ◽  
Robust Prediction ◽  
Sentinel 2

Grasslands are important for their ecological values and for agricultural activities such as livestock production worldwide. Efficient grassland management is vital to these values and activities, and remote sensing technologies are increasingly being used to characterize the spatiotemporal variation of grasslands to support those management practices. For this study, Sentinel-2 satellite imagery was used as an input to develop an open-source and automated monitoring system (Sen2Grass) to gain field-specific grassland information on the national and regional level for any given time range as of January 2016. This system was implemented in a cloud-computing platform (StellaSpark Nexus) designed to process large geospatial data streams from a variety of sources and was tested for a number of parcels from the Haus Riswick experimental farm in Germany. Despite outliers due to fluctuating weather conditions, vegetation index time series suggested four distinct growing cycles per growing season. Established relationships between vegetation indices and grassland yield showed poor to moderate positive trends, implying that vegetation indices could be a potential predictor for grassland biomass and chlorophyll content. However, the inclusion of larger and additional datasets such as Sentinel-1 imagery could be beneficial to developing more robust prediction models and for automatic detection of mowing events for grasslands.

Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

2021 ◽  
Author(s):  
Markus Löw ◽  
Tatjana Koukal
Keyword(s):  
Time Series ◽  
Forest Management ◽  
Time Series Analysis ◽  
Bark Beetle ◽  
Management Practices ◽  
Forest Ecosystems ◽  
Forest Disturbance ◽  
Spatial Scales ◽  
Forest Disturbances ◽  
Sentinel 2

&lt;p&gt;Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. Therefore, the monitoring of large and small scale vegetation changes such as those caused by natural events (e.g., pest infestation, higher mortality due to altering site conditions) or forest management practices (e.g., thinning or selective timber extraction) becomes more and more crucial. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales.&lt;/p&gt;&lt;p&gt;We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky&amp;#8211;Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria.&lt;/p&gt;&lt;p&gt;In addition to spatiotemporal disturbance maps, we produce zonal statistics on different spatial scales that provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances, for example to facilitate sustainable forest management.&lt;/p&gt;&lt;p&gt;At a forest stand level, we reconstruct the origin date of forest disturbances (FDD &amp;#8211; Forest Disturbance Date). Theses FDD outputs show the spatiotemporal patterns and the development of damages and indicate that most dynamics are caused by recurring and spreading bark beetle infestation. The validation results based on field data confirm a high detection rate and show that the derived temporal information is reliable. In total, 23400 hectares, i.e., on average 2.8% of the forest area in the study area, are found to be affected by forest disturbance. The zonal statistic maps point out hotspots of significant forest disturbances, where adequate forest management measures are highly needed. Furthermore, this study highlights the TSA&amp;#8217;s potential to also depict and monitor minor human impacts on forests, such as thinning, selective timber extraction or other moderate forest management practices.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&amp;#160; &lt;/strong&gt;&lt;em&gt;forest disturbance; forest monitoring; bark beetle infestation; forest management; time series analysis; phenology modelling; remote sensing; satellite imagery; Sentinel-2&lt;/em&gt;&lt;/p&gt;

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