Copernicus Sentinel-2 (MSI) Mission Research Papers (original) (raw)

The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonoše Mts. National Park. We compared classification results (accuracy, maps) of pixel-based (Maximum Likelihood, Suport Vector Machine and Neural Net) and object-based approaches. The best classification results (overall accuracy 84.3%, Kappa coefficient = 0.81) were achieved for AISA Dual data using per-pixel SVM classifier for 40 PCA bands. The best classification results of APEX though were only 1.7 percentage points lower. To get comparable results for Sentinel-2A classification legend had to be simplified. With the simplified legend the accuracy using MLC classifier reached 77.7%.

- by and +1
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- Remote Sensing, Hyperspectral remote sensing, Vegetation, Hyperspectral Imagery

Forests are of great importance for the sustainability of the ecosystem as well as for the mankind. The poplar species in the forest ecosystems are one of the most valuable and beneficial species for the society and environment. Turkey is a very rich country in terms of cultivated poplar species. The determination of poplar areas in Turkey is usually based on field studies. Although these methods require high cost, time and labor need, the results obtained vary and are insufficient in terms of accuracy. Determination of poplar cultivated areas and mapping of their spatial locations play an important role for decision-makers and planners to enhance the economic and ecological value of poplar trees. The main goal of this study is to map Poplar (P.deltoides) cultivated areas in Akyazi district of Sakarya, Turkey province using the Sentinel-2 satellite imagery. For this purpose, object-based classification based on multi-resolution segmentation algorithm was utilized to create image objects and then, two outstanding ensemble learning algorithms, Random Forest (RF) and Rotation Forest (RotFor) were applied to produce thematic maps. In order to analyze the effects of the spectral bands of the Sentinel-2 image on the object-based classification performance, two datasets consisting of different spectral band combinations (i.e. four 10m bands and 10m ten pan-sharpened bands) were used. The performance evaluation results showed that the RotFor classifier produced superior classification performances compared to the RF classifier for the band combinations considered in this study. It was also observed that the difference in classification accuracy reached to approximately 6% in terms of overall accuracy. Moreover, results that Sentinel-2 image dataset having 4-band dataset and 10-band dataset was effective for determination of poplar areas and the class level accuracy reached to ~99% in terms of F-score.

The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Moreover, past landscape reconstruction enables a better understanding of human resilience to climatic and environmental changes in different periods and locations, and illustrates examples of sustainable development in the past. Free and open-source (FOSS) datasets of satellite imagery offer considerable opportunities for landscape heritage stakeholders both for recording and monitoring activities. In this research, a completely FOSS-cloud procedure to enhance the detection of palaeo-landscape features is presented. Sentinel - 2 satellite imagery has been retrieved in the Google Earth Engine dataset collection and analysed through a Python script code realized in Google Colaboratory. A multi-temporal approach has been adopted to investigate the potential of satellite imagery to detect buried features along with Spectral Index (i.e., RGB, False Short Wave Infrared Colour and Bare Soil Index) and Spectral Decomposition analysis (i.e., Hue, Saturation and Value, Tasselled Cap Transformation and Principal Component Analysis). This procedure has been tested in the Po Plain (Northern Italy), chosen because it is characterized by human-landscape interaction since the Mid-Holocene. Thanks to its complex settlement and land-management history, the Po Plain represents an ideal laboratory to assess the potentiality of satellite imagery to enhance riverscapes’ palaeo-features.  The outputs obtained can be visualized directly in the Google Colaboratory browser or downloaded via Google Drive for further graphical applications or spatial analysis. The buried features detected have been checked through the available geomorphological and archaeological literature; published case studies interpreting the occurrence of buried features served as a benchmark to validate the script code developed. This research represents one of the first applications of the GEE Python API in landscape studies. The main advantages of this procedure consist of: i) being FOSS, all the software used here are open-licensed; ii) working in cloud, no powerful hardware is necessary to run the script code; iii) high adaptability, changing the ROI is possible to calculate SI and SD outputs for any area of the world; iv) very basic coding skills are required to adapt the code to a ROI with different environmental characteristics. The development of FOSS-cloud procedures could support the identification, conservation and management of cultural and natural heritage anywhere around the world. In remote areas or where local heritage is threatened as a result of political instability, climate change or other factors, FOSS-cloud protocols can facilitate the application of new scientific methods and enable the dissemination of and access to scientific information.

- by Guillem Domingo-Ribas
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- Computer Science, Landscape Archaeology, Open Source Software, Python

The mangrove is one of the most endangered ecosystems on the planet. Indeed, despite all the benefits that it recognizes, namely the reduction of the risk of coastal erosion, the breeding and nursery grounds for fish or the sequestration of carbon, nevertheless are alarming loss of mangrove area in the world. This study focuses on the establishment of a GIS and an analysis of the dynamics and typology of mangroves in Libreville and its surroundings. Landsat high spatial resolution satellite imagery (30 m) mapped the dynamics of mangroves and land cover. As a result, mangroves evolve in crescendo and decrescendo in time and space. The mangrove areas of the selected dates are: 920 km², 1051.2 km², 997.3 km² and 1048.64 km², respectively in 1990, 2000, 2014 and 2018. This change is partly due to the cloudy disturbance of the different images and, above all, the ever-increasing evolution of the built-up space, and therefore human and also industrial pressures. In order to analyze the typology of mangroves in Libreville and its surroundings, three maps were combined (mangrove species, mangrove height and mangrove cover). This analysis is a prerequisite in the study of the estimation of mangrove biomass. The results produced were introduced in a GIS with ArcGis, which itself should be managed by an environmental observatory.

- by Geldin Derrick Voubou
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- Geography, Geomatics, Remote Sensing, Environmental Studies

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R2 = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha−1 (average = 87.67 Mg ha−1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.

- by Tien Dat Pham
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- Tropical forest biomass, REDD+, Remote Sensing and Mangrove, Mangrove forest

Invasive plant species can pose major threats to biodiversity, ecosystem functioning and services. Satellite based remote sensing has evolved as an important technology to spatially map the occurrence of invasive species in space and time. With the new era of the Sentinel missions, Synthetic Aperture Radar (SAR) and multispectral data are now freely available and repeatedly acquired on a high spatial and temporal resolution for the entire globe. However, the high potential of such sensors for automatic mapping procedures cannot be fully harnessed without sufficient and appropriate reference data for model calibration. Reference data are commonly acquired in field surveys, which however, are often relatively expensive and affected by sampling and observer bias. Moreover, a direct transferability to the remote sensing perspective and scale is difficult. Accordingly, we firstly assess the potential of Unmanned Aerial Vehicles (UAV) for semi-automatic reference data acquisition on species cover of three woody invasive species Pinus radiata, Ulex europaeus and Acacia dealbata occurring in Chile. Secondly, we test the upscaling of the estimated species cover to the spatial scale of Sentinel-1 and Sentinel-2. The proposed workflow includes the visual sampling of respective canopies in UAV orthomosaics and the subsequent spatial extrapolations using MaxEnt with spectral (RGB, Hyperspectral), textural (2D) and canopy structural (3D) predictors derived from UAV-based photogrammetry. These UAV-based maps are then used to train random forest models with multitemporal Sentinel-1 and Sentinel-2 data to map the invasive species cover on large spatial scales. Our results show that the semi-automatic UAV-based mapping of the three invasive species results in accurate predictions. Depending on the predictor combination, the correlation was 0.70, 0.77 and 0.90 for Pinus radiatia, Ulex europaeus, Acacia dealbata, respectively. Among the three species, we observed clear differences in the model performance between the tested photogrammetric predictors and their combinations (spectral, 2D texture or 3D structure). For scaling up the UAV-based estimates to the satellite-scale, the Sentinel-2 data (multispectral) were more important than Sentinel-1 data (SAR). An independent validation revealed that the R 2 of the upscaling accounted for 0.78 or higher for all species and RMSE lower than 12%. Our results hence demonstrate that UAV-based reference data acquisitions are a promising alternative to traditional field surveys if the target species are directly identifiable in the UAV data.

- by Teja Kattenborn and +1
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- Remote Sensing, Invasive species ecology, Invasive Species, Multispectral & Hyperspectral Images Classification

Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine’s (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information.

- by Prof. Anselme Muzirafuti and +1
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- Agricultural Engineering, Food Science, Agribusiness, Agricultural Economics

Illegal excavations in archaeological heritage sites (namely "looting") are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

- by Tapete D.
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- Multispectral Imaging, Pattern Recognition, Optical Imaging, Syria (Archaeology)

The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite-2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R 2), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R 2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R 2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R 2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R 2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

- by Tien Dat Pham and +1
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- Machine Learning, Biomass, Support Vector Machines, Copernicus Sentinel-2 (MSI) Mission

This paper is focused on the use of satellite Sentinel-2 data for assessing their capability in
the identification of archaeological buried remains. We selected the “Tavoliere delle Puglie” (Foggia,
Italy) as a test area because it is characterized by a long human frequentation and is very rich in
archaeological remains. The investigations were performed using multi-temporal Sentinel-2 data
and spectral indices, commonly used in satellite-based archaeology, and herein analyzed in known
archaeological areas to capture the spectral signatures of soil and crop marks and characterize their
temporal behavior using Time Series Analysis and Spectral Un-mixing. Tasseled Cap
Transformation and Principal Component Analysis have been also adopted to enhance
archaeological features. Results from investigations were compared with independent data sources
and enabled us to (i) characterize the spectral signatures of soil and crop marks, (ii) assess the
performance of the diverse spectral channels and indices, and (iii) identify the best period of the
year to capture the archaeological proxy indicators. Additional very important results of our
investigations were (i) the discovery of unknown archaeological areas and (ii) the setup of a
database of archaeological features devised ad hoc to characterize and categorize the diverse
typologies of archaeological remains detected using Sentinel-2 Data.

The repetitive and destructive nature of floods across the globe causes significant economic damage, loss of human lives, and leaves the people living in flood-prone areas with fear and insecurity. With enough literature projecting an increase in flood frequency, severity, and magnitude in the future, there is a clear need for effective flood management strategies and timely implementation. The earth observatory satellites of the European Space Agency's Sentinel series, Sentinel-1, Sentinel-2, and Sentinel-3, have a great potential to combat these disastrous floods by their peerless surveillance capabilities that could assist in various phases of flood management. In this article, the technical specifications and operations of the microwave synthetic aperture radar (SAR) onboard Sentinel-1, optical sensors onboard Sentinel-2 (Multispectral Instrument) and Sentinel-3 (Ocean and Land Color Instrument), and SAR altimeter onboard Sentinel-3 are described. Moreover, the observational capabilities of these three satellites and how these observations can meet the needs of researchers and flood disaster managers are discussed in detail. Furthermore, we reviewed how these satellites carrying a range of technologies that provide a broad spectrum of earth observations stand out among their predecessors and have bought a step-change in flood monitoring, understanding, and management to mitigate their adverse effects. Finally, the study is concluded by highlighting the revolution this fleet of Sentinel satellites has brought in the flood management studies and applications.

- by Sai Kiran Kuntla
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- Remote Sensing, Flood Risk Management, Synthetic Aperture Radar, Floods

The Causse of El Hajeb belongs to the Tabular Middle Atlas (TMA), in which thousands of karst landforms have been identified. Among them, collapse dolines and dissolution sinkholes have been highlighted as a source of environmental risks and geo-hazards. In particular, such sinkholes have been linked to the degradation of water quality in water springs located in the junction of the TMA and Saïss basin. Furthermore, the developments of collapse dolines in agricultural and inhabited areas enhance the risk of life loss, injury, and property damage. Here, the lack of research on newly formed cavities has exacerbated the situation. The limited studies using remote sensing or geophysical methods to determine the degree of karstification and vulnerability of this environment fail to provide the spatial extent and depth location of individual karst cavities. In order to contribute to the effort of sinkhole risk reduction in TMA, we employed remote sensing and geophysical surveys to integrate electrical resistivity tomography (ERT) and self-potential (SP) for subsurface characterization of four sinkholes identified in the Causse of El Hajeb. The results revealed the existence of sinkholes, both visible and non-accessible at the surface, in carbonate rocks. The sinkholes exhibited distinct morphologies, with depths reaching 35 m. Topography, geographic coordinates and land cover information extracted on remote sensing data demonstrated that these cavities were developed in depressions in which agricultural activities are regularly performed. The fusion of these methods benefits from remote sensing in geophysical surveys, particularly in acquisition, georeferencing, processing and interpretation of geophysical data. Furthermore, our proposed method allows identification of the protection perimeter required to minimize the risks posed by sinkholes.

Remote Sensing has greatly evolved with the launch of several Earth Observation satellites from multiple international operators from U.S. to Europe and Asia. More images are available with better temporal, spatial and spectral resolution. Despite increasing number of images, the free data policy should be one of the main catalyst for the development of remote sensing services in particular with Landsat and Sentinel programs. The analysis conducted aimed to make an assessment of the new EO open data from optical domain, identifying the potential new remote sensing services and also to investigate the feasibility to produce rapid mapping from Landsat-8 and Sentinel-2, such as land use and land cover maps. This assessment targets in Portugal Continental. The evaluation of Landsat8 data was performed using images from two seasons since this satellite is making acquisitions since 2013. The evaluation of Sentinel-2 was performed comparatively with Landsat-8 since for the time being data is not yet available and only simulated data exists from specific regions. The analysis of the results indicate that it is feasible to quickly obtain land cover maps, based on the automation of the production process and using pre-defined training data. The quality of products has to be analysed. Sentinel-2 will introduce some improvements, with more bands in the red edge and infrared frequency range, in addition to better spatial 10 meters resolution. Whereas both programs have the vision to ensure continuity of its missions, the implementation of a rapid mapping service based on open data can make sense to support geo-decision systems in specific application domains.

- by Nuno Duro and +1
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- Remote Sensing, Open Data, Land-use/ Land-cover Change, Copernicus Sentinel-2 (MSI) Mission

- by Federico Zaina
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- Archaeology, Computer Science, Remote Sensing, Cultural Heritage

This paper presents the results of a crop type mapping exercise conducted in two study areas in Bulgaria and based on data from the Sentinel-2 (S2) satellites. A multi-date maximum likelihood classification approach was used in which nine spectral bands from three cloud-free images, well distributed across the growing season, were used. Validation was performed using field data collected as part of the study and data from the Integrated Administration and Control System (IACS) dataset. Depending on the validation dataset and the study area, an overall accuracy of 74-95% was achieved after the crop type maps were post-processed by mode filtering. Further increase in accuracy may be obtained if parcel boundaries, as defined in the IACS dataset, are used to aggregate the per-pixel classification to a parcel level.

- by Lachezar Filchev and +1
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- Remote Sensing, Crop Science, Small Area Estimation, Vegetation

The construction of the Pan-European Corridor 10 is one of the major projects in the Republic of Serbia, and it enters the final phase. A vast natural area suffered a significant change to complete the project and therefore is the existence of a need to monitor those changes. Nature requires adequate and accurate detection of environmental stresses which inevitably arise after implementation of such large construction projects. Conversely to traditional field monitoring of the environment, this paper will present the remote sensing method which includes usage of European Space Agency's Sentinel 2A optical satellite data processed with different Machine Learning algorithms. An accuracy assessment is performed on land cover map results, and change detection carried out with best resulting data.

The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current methods take a holistic approach to landscape heritage and promote an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the natural and cultural landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. Satellite remote sensing technologies have enabled significant improvements in landscape research. The advent of the cloud-based platform of Google Earth Engine has allowed the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. In this paper, the use of Sentinel-2 satellite data in the identification of palaeo-riverscape features has been assessed in the Po Plain, selected because it is characterized by human exploitation since the ...

The present research is part of the project “From Aquileia to Singidunum: reconstructing the paths of the Roman travelers—RecRoad”, developed at the Université Bordeaux Montaigne, thanks to a Marie Skłodowska-Curie fellowship. One of the goals of the project was to detect and reconstruct the Roman viability between the Roman cities of Aquileia (Aquileia, Italy) and Singidunum (Belgrade, Serbia), using different sources and methods, one of which is satellite remote sensing. The research project analyzed and combined several data, including images produced by the Sentinel-2 mission, funded by the European Commission Earth Observation Programme Copernicus, in which satellites were launched between 2015 and 2017. These images are freely available for scientific and commercial purposes, and constitute a constantly updated gallery of the whole planet, with a revisit time of five days at the Equator. The technical specifications of the satellites’ sensors are particularly suitable for archaeological mapping purposes, and their capacities in this field still need to be fully explored. The project provided a useful testbed for the use of Sentinel-2 images in the archaeological field. The study compares traditional Vegetation Indices with experimental trials on Sentinel images applied to the Srem District in Serbia. The paper also compares the results obtained from the analysis of the Sentinel-2 images with WorldView-2 multispectral images. The obtained results were verified through an archaeological surface survey.

- by Sara Zanni and +1
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- Satellite remote sensing, Roman roads, Roman Archaeology, Copernicus Sentinel-2 (MSI) Mission

Abstrak Pertumbuhan lahan terbangun serta padatnya aktivitas manusia berkendara menyebabkan semakin besarnya produksi CO2 dibumi, sehingga berdampak pada penurunan kualitas udara dan efek rumah kaca. Hal ini juga ditandai dengan berkurangnya vegetasi hutan yang menyebabkan terbatasnya penyerapan karbon, ekosistem perairan memiliki kemampuan penyerapan karbonnya lebih tinggi dari penyerapan yang ada di darat sekitar 55% karbon yang ada di atmosfer dan digunakan untuk proses fotosintesis. Blue karbon merupakan penyimpan karbon terbesar yang berada disekitar pesisir pantai ditandai dengan adanya keberadaan mangrove, padang lamun, dan terumbu karang. Tujuan penelitian ini ialah memantau keberadaan ekosistem padang lamun dan terumbu karang menggunakan citra Sentinel-2A melalui transformasi algoritma lyzenga dalam mendeteksi dan mengetahui kemampuan menyimpan karbon pada pulau Kudingarenglompo. Hasil perhitungan ditemukan luasan yang lebih dominan berupa padang lamun (122ha) kemudian luasan terumbu karang mencapai (77ha) rubbel (27ha) dan pasir (18ha) serta potensi penyimpanan blue karbon tergantung kerapatan dan luas persebaran padang lamun. Tingkat uji akurasi citra Sentinel-2A dalam mendeteksi persebaran ekosistem padang lamun dan terumbu karang menghasilkan nilai sebesar 87,60%, nilai akurasi tersebut menunjukkan bahwa citra Sentinel-2A mampu digunakan dalam memantau ekosistem padang lamun dan terumbu karang atau substrat perairan dangkal. Kata kunci : padang lamun, blue karbon, lyzenga, sentinel-2A. Abstract The growth of built land as well as the density of human activity driving causes greater CO2 production on the earth, thus impacting on air quality degradation and the greenhouse effect. It is also characterized by reduced forest vegetation which also has an impact on carbon sequestration, aquatic ecosystems have a higher carbon sequestration capacity than the existing sequestration around 55% of carbon in the atmosphere and are used for photosynthesis. Blue carbon is the largest carbon storage around the coast characterized by the presence of mangroves, seagrass beds, and coral reefs. In this study monitoring the presence of seagrass and coral reef ecosystems using Sentinel-2A imagery through lyzenga algorithm transformation in detecting and knowing the ability to store carbon on the island of Kudingarenglompo. The results of the calculation found a more dominant area in the form of seagrass beds (122ha) and then the area of coral reefs reached (77ha) rubbel (27ha) and sand (18ha) and the potential for storing blue carbon depending on the density and extent of seagrass distribution. The level of testing of Sentinel-2A's image accuracy in detecting the distribution of seagrass and coral reef ecosystems yields a value of 87,60%, the accuracy value indicates that Sentinel-2A imagery is capable of being used in monitoring seagrass and coral reef or shallow water substrate ecosystems.

- by Yuliana Safitri and +1
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- Seagrass ecology, Blue Carbon, Copernicus Sentinel-2 (MSI) Mission, lyzenga

- by dieudonne mouketou
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- Forestry, Remote Sensing, Tropical Ecology, Environmental Remote Sensing

The "From Aquileia to Singidunum: reconstructing the paths of the Roman travellers-RecRoad" project was developed at Université Bordeaux, Montaigne in collaboration with the Sremska Mitrovica Institute for Protection of Cultural Monuments. Its main goal was to detect and map the Roman thoroughfare connecting the Roman cities of Aquileia (Aquileia, Italy) and Singidunum (Belgrade, Serbia) using different sources and methods, including Sentinel-2 multispectral images, historical maps and surface survey results. This paper focuses on the methodologies applied to identify buried archaeological features and on the results obtained combining data coming from different kind of sources in the Pusta Dreispitz site (Vojvodina, Serbia): in this area, a multi-layered archaeological site was identified through remote sensing analysis, while its chronological framing was determined thanks the surface surveys on the ground. The pottery fragments collected show a time-span going from proto-history to the Roman period as well as recent findings from the 18-19th centuries, confirming once more the necessity of integrating remote-sensing and digital techniques with field research and verification. The project provides a first useful test-bed for Sentinel-2A images in archaeology for detecting the presence of buried archaeological sites and remains of Roman roads, with remarkable results in the Srem district (Serbia). The research workflow integrates remote sensing analysis with the interpretation of historical maps, namely the Josephinische Landesaufnahme (1763-1787), the Franziszeische Landesaufnahme (1808-1869), the Franzisco-Josephinische Landesaufnahme (1869-1887) and the Spezialkarte der Osterreichisch-Ungarischen Monarchie (1877-1914). The historical maps are geo-referenced and overlain on the satellite images within a GIS platform to interpret the anomalies detected in the Sentinel-2A images. Finally, a surface survey is performed to check the actual presence of the Roman road traces and of other buried sites.

- by Sara Zanni and +2
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- Archaeology, Remote Sensing, Archaeological survey, Serbian Archaeology

The destruction of cultural heritage caused by dams represents a major issue especially in an age of climate change and narrowly focused development policies. To counteract this phenomenon, archaeologists and cultural heritage experts have relied upon rescue archaeology practices generally limited to fieldwork methodologies, while remote sensing of satellite imagery remains under-considered. To bridge this gap, we build on a multidisciplinary collaboration exploring the potential of Synthetic Aperture Radar (SAR) and open access multispectral satellite imagery, for quantifying the archaeological evidence located within a prospective reservoir area before dam construction. Based on previous research by Marchetti (2020) claiming the necessity for ad hoc protocols to document and monitor the impact of dams on cultural heritage, we selected two complementary situations: the planned dam of Halabiyeh in Syria and the under construction Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. These case studies were analyzed with state-of-the-art methodologies to develop a feasible workflow that may contribute to fostering the use of satellite imagery in operational contexts such as those represented by these particular cases, and be replicated by archaeologists in other areas. The workflow is designed to be integrated to ground-truthing methodologies into two dedicated protocols named Pre-Construction Archaeological Risk Assessment (PCARA) and Pre-Flooding Rescue Archaeological Program (PFRAP) which could eventually become a standard procedure for rescue archaeology in dams areas.

- by Tapete D.
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- Archaeology, Cultural Heritage, Multispectral Imaging, Cultural Heritage Conservation

- by Dr. Aqil Tariq
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- Agricultural Engineering, Food Science, Agribusiness, Agricultural Economics

This research tested the use of geographic information systems using Sentinel 1 and Sentinel 2 satellite data to estimate biomass mangrove forest in Vinh Quang commune, Tien Lang district, Hai Phong province. 15 sample plots (10 m × 10 m) in the field were established for making models and evaluation, the satellite images for processing in 2017 were provided freely by ESA Corporation. The study created land cover and biomass maps from field allometric equations and estimated results from the model by maximum likelihood classification and the regression model, respectively. For land cover accuracy assessment, Kappa index was employed with 93% accuracy. NDVI, SAVI were representative indices of optical Sentinel 2 images, similarly, VV and VH backscatter, VV/VH and VH/VV from Sentinel 1A images. The study showed that Sentinel 1 backscatters were unable to generate model due to quite low R 2. Compare to optical images, the NDVI index was used for biomass estimating, the total biomass was about 67,983.12 tons, average: 153.94 ± 27.01 ton/ha, maximum: 223.14 ton/ha. By comparing real numbers and estimated numbers, the results were acceptable, 23.8% average. We conclude that the optical Sentinel 2 has been more suitable to make estimating the model for mangrove biomass at a small-scale level, especially for commune level.

Systematic quantification and monitoring of forest biophysical and biochemical variables is required to assess the response of ecosystems to climate change and gain a deeper understanding of the carbon cycle. Red-Edge Position (REP) is a hyperspectrally detectable parameter, which is sensitive to Chlorophyll (Chl) content. In the current study, REP was modelled for Norway spruce Forest canopy Reflectance and Transmittance (FRT) using Radiative Transfer Modelling (RTM) (resampled to HyMap and Sentinel-2 spectral resolution) as well as calculated from the real HyMap and simulated Sentinel-2 image data. Different REP extraction methods (PF, LE, 4PLI and its optimized versions for HyMap and Sentinel-2 spectral resolution) were assessed. The lowest differences in REP values calculated from image-extracted spectra and from the theoretical RTM simulations were found for the 4PLI method including its HyMap and Sentinel-2 optimized versions (4PLIH and 4PLIS). Despite its simplicity, the 4PLI REP extraction technique demonstrated its potential usefulness for estimating canopy chlorophyll (Chl × LAI) content using both airborne hyperspectral (HyMap) data as well as space-borne Sentinel-2 image data.

Satellite technologies are increasingly used to track looting in remote and inaccessible archaeological sites and assess damage to heritage. Evidence gathered in our study proves a growing user uptake of these technologies, beyond the specialist remote sensing community, but also that a more synergistic use of optical and radar data is required. The advantages of such an approach to satellite monitoring are demonstrated on Apamea, Syria. Current limitations and future perspectives are outlined, as an entry point to a comprehensive review published by the authors in the referenced journal article, that the readers are encouraged to refer to for a more in-depth and specialist discussion.

Environmental degradation associated with coal mining is one of the serious environmental issues in South Africa that is expected to continue with increasing energy demands. Mapping and monitoring contamination in mining areas are necessary to guide rehabilitation activities. Rapid monitoring systems are needed to develop effective rehabilitation plans. The advent of multispectral remote sensing data has proven to be effective in mapping and detecting mine related soil contamination. An integrated approach of soil geochemistry and remotely sensed data to characterise contamination in Emalahleni coal fields is presented in this study. Aster data was acquired and several band combinations were developed to identify patterns and occurrence of soil contamination. For geochemical assessment, the Nemerow index and the pollution loading index were calculated to evaluate the mining activity contamination. The classified aster images showed that contamination varies with land use. Residential areas and mining areas showed similar trends of contamination. Geochemical results showed that iron, vanadium and chromium are the most abundant elements in the study area. The findings of contamination indices reveal that the overall level of metal contamination in the study area is between moderate to heavily contaminated. The most polluted areas are concentrated in mining areas and along major transport intersections. The ASTER band ratios for silica and clay phases correspond with classified contamination indices indicating that remote sensing can successfully be used to assess pollution.

- by Mafuza Maya
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- ALOS/PALSAR, Copernicus Sentinel-2 (MSI) Mission

Very high-resolution (VHR) optical satellite imagery (≤5 m) is nowadays an established source of information to monitor cultural and archaeological heritage that is exposed to hazards and anthropogenic threats to their conservation, whereas few publications specifically investigate the role that regularly acquired images from high-resolution (HR) satellite sensors (5-30 m) may play in this application domain. This paper aims to appraise the potential of the multispectral constellation Sentinel-2 of the European Commission Earth observation programme Copernicus to detect prominent features and changes in heritage sites, during both ordinary times and crisis. We test the 10 m spatial resolution of the 3 visible spectral bands of Sentinel-2 for substantiation of single local events-that is, wall collapses in the UNESCO World Heritage site of the Old City of Aleppo (Syria)-and for hotspot mapping of recurrent incidents-that is, the archaeological looting in the archaeological site of Apamea (Syria). By screening long Sentinel-2 time series consisting of 114 images for Aleppo and 57 images for Apamea, we demonstrate that changes of textural properties and surface reflectance can be logged accurately in time and space and can be associated to events relevant for conservation. VHR imagery from Google Earth was used for the validation and identification of trends occurring prior to the Sentinel-2 launch. We also demonstrate how to exploit the Sentinel-2 short revisiting time (5 days) and large swath (290 km) for multi-temporal tracking of spatial patterns of urban sprawl across the cultural landscape of the World Heritage Site of Cyrene (Libya), and the three coastal ancient Greek sites of Tocra, Ptolemais, and Apollonia in Cyrenaica. With the future development of tailored machine learning approaches of feature extraction and pattern detection, Sentinel-2 can become extremely useful to screen wider regions with short revisiting times and to undertake comparative condition assessment analyses of different heritage sites.

- by Tapete D.
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- Remote Sensing, Cultural Heritage, Syria, Libya

Indonesian National Institute of Aeronautics and Space (LAPAN) started building its experimental microsatellite back in 2007 and finally able to launch its first microsatellite dubbed as LAPAN-A1/LAPAN-Tubsat. With the launch of LAPAN-A3/LAPAN-IPB, Indonesian experimental satellite programme hit its third generation. LAPAN-A3 is carrying multiple payloads including multispectral push-broom imager, digital matrix camera, as well as video camera. This paper aims to highlight the spectral differences between LAPAN-A3 and the well-established Sentinel-2A multispectral to investigate the potential of using LAPAN-A3 data to complement the other well-established medium resolution satellite data. Comparisons between corresponding bands and band transformations were performed over a dataset. Three areas of interest were chosen as the test sites. Linear regression and Pearson correlation coefficient were then calculated between the corresponding bands. The preliminary results showed a moderate correlation between the two sensors with Pearson correlation coefficient ranging from 0.39 to 0.65. Some issues were found regarding the radiometric quality over the whole scene of LAPAN-A3.

- by Zylshal Zylshal
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- Remote Sensing, Digital Image Processing, Correlation, Spectral analysis

- by Lachezar Filchev
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- Remote Sensing, Crop Science, Small Area Estimation, Vegetation

This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.

- by Adam Waśniewski and +1
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- Forestry, Remote Sensing, Tropical Ecology, Environmental Remote Sensing

Los fenómenos geográficos se desarrollan de manera continúa sobre una extensión de la superficie terrestre (en, sobre o bajo ella), y ha sido un desafío constante crear un modelo de representación tan simple para ser almacenado, procesado y visualizado con facilidad, tan complejo que permita perder el mínimo de información crítica y versátil que permita mantener el nivel de detalle proporcional a la riqueza del mundo real. Ese modelo se ha denominado modelo ráster y es el tema principal del presente libro, su origen, fundamentos, propiedades y algunos usos serán descritos en detalle. Para ello, se basa en la Plataforma R y RStudio, unas de las herramientas informáticas más poderosas en la actualidad. Desde la instalación de las aplicaciones, pasando por una guía básica de su utilización, hasta una detallada descripción del trabajo con dicho modelo. En sus casi 700 páginas encuentras principios teóricos y ejercicios práct...

- by Danilo A Verdugo Chaura
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- Remote Sensing (Earth Sciences), MODIS, RASTER, Raster analysis

El estudio de la distribución y superficie ocupada por diferentes tipos de vegetación permite disponer de información relevante para el desarrollo de planes de manejo y ordenamiento del territorio. El noreste de la provincia de Salta presenta una cobertura de suelo muy heterogénea, ocasionando que las tareas de relevamiento resulten de muy difícil y costosa realización. Mediante el uso de sensores remotos es posible obtener información referida a características biofísicas de la cubierta territorial, que da cuenta de aspectos generales de la misma, y permiten realizar una descripción a nivel regional. El objetivo del presente trabajo fue actualizar al año 2016 la cartografía de la cobertura del suelo de un sector del noreste de la provincia de Salta de difícil acceso. Para ello se generó un compuesto de máximo valor de índice de vegetación normalizado (NDVI) sobre un mosaico de imágenes satelitales Sentinel 2B-1C corregidas al tope de la atmósfera del año 2016, utilizando la plataforma informatica Google Earth Engine para análisis geoespacial. Posteriormente se realizó una clasificación no supervisada por clusters. Las clases resultantes fueron identificadas de acuerdo al sistema de clasificación de cobertura del suelo Land Cover Classification System (LCCS) en bosques y arbustales con diferentes porcentajes de cobertura, suelos desnudos y cuerpos de agua. La validación de la cartografía fue realizada utilizando datos recopilados en campo, obteniendo como resultado una confiabilidad global del 83%. Este producto resulta un aporte importante para la resolución de conflictos relacionados al ordenamiento del territorio, a fin de prevenir el deterioro de los recursos naturales y, consecuentemente, la calidad de vida de los pobladores locales.

- by cecilia morales
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- Cobertura, Teledeteccion y SIG, Copernicus Sentinel-2 (MSI) Mission

Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.

- by Mateo Gašparović and +1
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- Remote Sensing, Remote sensing and GIS applications in Landscape Research, Remote sensing and GIS, Image fusion

“Tells” are archaeological mounds formed by deposition of large amounts of anthropogenic material and sediments over thousands of years and are the most important and prominent features in Near and Middle Eastern archaeological landscapes. In the last decade, archaeologists have exploited free-access global digital elevation model (DEM) datasets at medium resolution (i.e., up to 30 m) to map tells on a supra-regional scale and pinpoint tentative tell sites. Instead, the potential of satellite DEMs at higher resolution for this task was yet to be demonstrated. To this purpose, the 3 m resolution imaging capability allowed by the Italian Space Agency’s COSMO-SkyMed Synthetic Aperture Radar (SAR) constellation in StripMap HIMAGE mode was used in this study to generate DEM products of enhanced resolution to undertake, for the first time, a systematic mapping of tells and archaeological deposits. The demonstration is run at regional scale in the Governorate of Wasit in central Iraq, where the literature suggested a high density of sites, despite knowledge gaps about their location and spatial distribution. Accuracy assessment of the COSMO-SkyMed DEM is provided with respect to the most commonly used SRTM and ALOS World 3D DEMs. Owing to the 10 m posting and the consequent enhanced observation capability, the COSMO-SkyMed DEM proves capable to detect both well preserved and levelled or disturbed tells, standing out for more than 4 m from the surrounding landscape. Through the integration with CORONA KH-4B tiles, 1950s Soviet maps and recent Sentinel-2 multispectral images, the expert-led visual identification and manual mapping in the GIS environment led to localization of tens of sites that were not previously mapped, alongside the computation of a figure as up-to-date as February 2019 of the survived tells, with those affected by looting. Finally, this evidence is used to recognize hot-spot areas of potential concern for the conservation of tells. To this purpose, we upgraded the spatial resolution of the observations up to 1 m by using the Enhanced Spotlight mode to collect a bespoke time series. The change detection tests undertaken on selected clusters of disturbed tells prove how a dedicated monitoring activity may allow a regular observation of the impacts due to anthropogenic disturbance (e.g., road and canal constructions or ploughing).

- by Tapete D.
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- Near Eastern Archaeology, Landscape Archaeology, Radar, Iraq

The research presented is part of the project “From Aquileia to Singidunum: reconstructing the paths of the Roman travelers – RecRoad”, developed at Université Bordeaux Montaigne in collaboration with the Institute for Protection of Cultural Monuments of Sremska Mitrovica. The main goal of the project is the detection and reconstruction of the Roman viability connecting the Roman cities of Aquileia (Aquileia, Italy) and Singidunum (Belgrade, Serbia) using different sources and methods, among which are Sentinel-2 multispectral imageries, historical maps and surface survey results. The project provided a first useful testbed for the use of Sentinel-2A images in the archaeological field to detect the presence of buried archaeological sites and remains of Roman roads, with outstanding results in the Srem district (Serbia). The research workflow integrated the remote sensing analysis with the interpretation of historical maps, such as the Josephinische Landesaufnahme (1763-1787), the Franziszeische Landesaufnahme (1808-1869), the Franzisco-Josephinische Landesaufnahme (1869-1887) and the Spezialkarte der Osterreichisch-Ungarischen Monarchie (1877-1914). The historical maps were geo-referenced and overlaid to the satellite imageries inside a GIS platform to succeed in the interpretation of the anomalies detected in the Sentinel-2A images. Finally, a surface survey was performed to check the effective presence of the Roman road traces and of other buried sites. This paper will specifically present the identification of a buried archaeological site connected to the road where it was possible to collect pottery that will allow to date the human activities on the area.

- by Sara Zanni and +2
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- Landscape Archaeology, Satellite remote sensing, Serbian Archaeology, Roman Archaeology

- by Alexandru Badea
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- Environmental Science, Computer Science, Remote Sensing, Cultural Heritage
- by Arianna Traviglia
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- Near Eastern Archaeology, Remote Sensing, Landscape Archaeology, Radar

Pocket beaches (PBs) are among the most attractive tourist sites and economic development contributors in coastal areas; however, they are negatively impacted by the combined effects of climate change and anthropogenic activities. Generally, research on PBs is conducted from the beach towards offshore. Studies on the land use/land cover (LULC) of PBs are limited and currently lacking. Such studies deserve more investigation due to the importance of LULC in PBs’ functioning. In this study, supervised classification methods were investigated for LULC mapping of the PBs located in the province of Messina. Sentinel-2B satellite images were analyzed using maximum likelihood (MaL), minimum distance (MiD), mahalanobis distance (MaD) and spectral angle mapper (SAM) classification methods. The study was conducted mainly in order to determine which classification method would be adequate for small scale Sentinel-2 imagery analysis and provide accurate results for the LULC mapping of PBs. In addition, an occurrence-based filter algorithm in conjunction with OpenStreetMap data and Google Earth imagery was used to extract linear features within 500 m of the inland buffer zone of the PBs. The results demonstrate that information on the biophysical parameters, namely surface cover fractions, of the coastal area can be obtained by conducting LULC mapping on Sentinel-2 images.

- by Prof. Anselme Muzirafuti
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- Economics, Tourism Studies, Coastal Management, Climate Change

Vegetation biomass is a globally important climate-relevant terrestrial carbon pool and also drives local hydrological systems via evapotranspiration. Vegetation biomass of individual vegetation types has been successfully estimated from active and passive remote sensing data. However, for many tasks, landscape-level biomass maps across several vegetation types are more suitable than biomass maps of individual vegetation types. For example, the validation of ecohydrological models and carbon budgeting typically requires spatially continuous biomass estimates, independent from vegetation type. Studies that derive biomass estimates across multiple vegetation or land-cover types to merge them into a single landscape-level biomass map are still scarce, and corresponding workflows must be developed. Here, we present a workflow to derive biomass estimates on landscape-level for a large watershed in central Chile. Our workflow has three steps: First, we combine field plot-based biomass estimates with spectral and structural information collected from Sentinel-2, TanDEM-X and airborne LiDAR data to map grassland, shrubland, native forests and pine plantation biomass using random forest regressions with an automatic feature selection. Second, we predict all models to the entire landscape. Third, we derive a land-cover map including the four considered vegetation types. We then use this land-cover map to assign the correct vegetation type-specific biomass estimate to each pixel according to one of the four considered vegetation types. Using a single repeatable workflow, we obtained biomass predictions comparable to earlier studies focusing on only one of the four vegetation types (Spearman correlation between 0.80 and 0.84; normalized-RMSE below 16 % for all vegetation types). For all woody vegetation types, height metrics were amongst the selected predictors, while for grasslands, only Sentinel-2 bands were selected. The land-cover was also mapped with high accuracy (OA = 83.1 %). The final landscape-level biomass map spatially agrees well with the known biomass distribution patterns in the watershed. Progressing from vegetation-type specific maps towards landscape-level biomass maps is an essential step towards integrating remote-sensing based biomass estimates into models for water and carbon management.

- by Javier Lopatin
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- Remote Sensing, Biomass, Copernicus Sentinel-2 (MSI) Mission

The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.

- by D. Smiraglia
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- Earth Observation, Forest ecosystems, Vegetation, Plant Phenology

- by shilan felegari
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- Agricultural Engineering, Food Science, Agribusiness, Agricultural Economics

The capabilities of satellite remote sensing technologies and their derived data for the analysis of archaeological sites have been demonstrated in a large variety of studies over the last decades. Likewise, the Earth Observation (EO) data contribute to the disaster management process through the provision of updated information for areas under investigation. In addition, long term studies may be performed for the in-depth analysis of the disaster-prone areas using archive satellite imagery and other cartographic materials. Hence, satellite remote sensing represents an essential tool for the study of hazards in cultural heritage sites and landscapes. Depending on the size of the archaeological sites and considering the fact that some parts of the site might be covered, the main concern regards the suitability of satellite data in terms of spatial and spectral resolution. Using a multi-temporal Sentinel-2 dataset between 2016 and 2019, the present study focuses on the hazard risk identification for the Micia and Germisara archaeological sites in Romania as they are endangered by industrialisation and major infrastructure works and soil erosion, respectively. Furthermore, the study includes a performance assessment of remote sensing vegetation indices for the detection of buried structures. The results clearly indicate that Sentinel-2 imagery proved to be fundamental in meeting the objectives of the study, particularly due to the extensive archaeological knowledge that was available for the cultural heritage sites. The main conclusion to be drawn is that satellite-derived products may be enhanced by integrating valuable archaeological context, especially when the resolution of satellite data is not ideally fitting the peculiarities (e.g., in terms of size, underground structures, type of coverage) of the investigated cultural heritage sites.

- by Cătălin Cristescu and +1
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- Remote Sensing, Cultural Heritage, Disaster Management, Earth Observation

This paper presents an innovative use of Sentinel-2 datasets to manage and organize archaeological surveys. Knowledge of the territory is customary for the process of organizing and carrying out fieldwalking surveys with excellent results. Good datasets will help to answer archaeological and historical questions present at the roots of any research project.
This method was tested in different survey projects carried out in three different countries (Italy, Portugal and Spain) 3 during the late-winter and early spring of 2017 and 2018, a period in which cereal crops are in a crucial stage of its cycle and still differences in growth can be detected by means of examining the spectral footprint. This paper introduces some of the basic methodological questions while an in-depth study of results and its relationship with broader questions of visibility and survey is in preparation.
The method proposed is based on locating, prior to the fieldwork, the field plots that show a lower presence of vegetation cover, or preferably bare soil areas, in a straightforward way. In order to do so, we used multispectral images of the Sentinel-2 mission, whose temporal and spatial resolution, together with its free distribution, make it a good tool not only for the identification of archaeological elements, but also for the organization of field surveys.

Présentation de la méthodologie et des données du projet de recherche RecRoad "Reconstructing the paths of the Roman travelers from Aquileia to Singidunum (Belgrade)", à l'intérieure du colloque international "Tiens bien la Route! Routes,... more

- by Sara Zanni
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- Archaeological GIS, Historical maps, Roman roads, Remote sensing and GIS

- by dieudonne mouketou
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- Environmental Science, Forestry, Remote Sensing, Tropical Ecology