Korekce lokálního dopadového úhlu SAR dat pro analýzu časových řad: metoda specifická pro krajinný pokryv

To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested...K zajištění co nejvyššího možného časového rozlišení dat SAR je nutné použít všechny dostupné dráhy družic nad daným územím. To může představovat výzvu v hornatém terénu, kde boční snímání družic SAR v kombinaci s různými sklony a aspekty terénu může silně ovlivnit intenzitu zpětného radarového rozptylu. Tyto chyby způsobené terénem je třeba odstranit pro možné porovnání dat v čase. Ačkoli v literatuře jsou popsány metody, které se zabývají tímto problémem, žádná z těchto metod není připravena na operativní a snadno přístupnou analýzu časových řad v horských oblastech. Tato studie se zabývá metodou korekce lokálního dopadového úhlu (LIA) pro analýzu časových řad lesů v horských oblastech. Metodika je založena na použití lineární závislosti mezi radarovým zpětným rozptylem a LIA, který se počítá pro každý satelitní snímek zvlášť. Použitím kombinace databází CORINE a Hansen Global Forest můžeme pro každý jednotlivý snímek získat širokou škálu různých LIA pro konkrétní typ lesa. Algoritmus korekce byl připraven v cloudové platformě Google Earth Engine (GEE) s využitím volně dostupných dat Sentinel-1, digitálního modelu terénu SRTM a databází CORINE a Hansen Global...Katedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyFaculty of SciencePřírodovědecká fakult

Estimating optical vegetation indices with Sentinel-1 SAR data and AutoML

Current optical vegetation indices (VIs) for monitoring forest ecosystems are widely used in various applications. However, continuous monitoring based on optical satellite data can be hampered by atmospheric effects such as clouds. On the contrary, synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series due to signal penetration through clouds and day and night acquisitions. The goal of this work is to overcome the issues affecting optical data with SAR data and serve as a substitute for estimating optical VIs for forests using machine learning. Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using multitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating a paired multi-temporal and multi-modal dataset in Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, digital elevation model (DEM), weather and land cover datasets (MMT-GEE). The use of ancillary features generated from DEM and weather data improved the results. The open-source Automatic Machine Learning (AutoML) approach, auto-sklearn, outperformed Random Forest Regression for three out of four VIs, while a 1-hour optimization length was enough to achieve sufficient results with an R2 of 69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was also found for selected case studies in the time series analysis and in the spatial comparison between the original and estimated SAR-based VIs. In general, compared to VIs from currently freely available optical satellite data and available global VI products, a better temporal resolution (up to 240 measurements/year) and a better spatial resolution (20 m) were achieved using estimated SAR-based VIs. A great advantage of the SAR-based VI is the ability to detect abrupt forest changes with a sub-weekly temporal accuracy.Comment: Full research article. 30 pages, 13 figures, 8 table

A correction of the local incidence angle of SAR data: a land cover specific approach for time series analysis

To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested..

Hodnocení metod a vstupních dat pro účely klasifikace krajinné pokrývky bývalých vojenských újezdů Brdy a Ralsko

Taking advantage of Earth Observation (EO) data for monitoring land cover has attracted the attention of a broad spectrum of researchers and end-users in recent decades. The main reason of increased interest in EO can be found mainly in open data of Landsat and Sentinel archive. The main objective of this study is to evaluate the accuracy of the classification algorithms Maximum Likelihood (ML) and Support Vector Machine (SVM) using Landsat 8 and Sentinel-2 data in the case studies of the former military training areas Brdy and Ralsko, which have undergone a very specific land cover development. The study evaluates the land cover in both case studies in 2016 and based on the obtained results discussing a usefulness of the selected data and methods. The results of the land cover classification achieved satisfactory accuracy - the overall accuracy was higher than 85 %. Based on the expectation, the results of accuracy based on SVM algorithm are higher than results obtained by ML algorithm. The highest accuracy has reached in the land cover classes of water bodies and coniferous forests, on the contrary, the lowest accuracy in built-up areas, sparse vegetation and bare soil. Keywords: Earth Observation, Support Vector Machine, Maximum Likelihood, Czechia, Sentinel-2, Landsat 8Využitie dát diaľkového prieskumu Zeme (DPZ) na mapovanie krajinnej pokrývky upútalo pozornosť širokej škály vedeckých výskumníkov a praktikov v posledných desaťročiach. Vplyv na zvýšenie záujmu o DPZ malo hlavne otvorenie archívu dát družíc Landsat a Sentinel. Hlavným cieľom tejto štúdie je vyhodnotiť presnosť bežne využívaných algoritmov Maximum Likelihood (ML) a Support Vector Machine (SVM) nad voľne dostupnými dátami Landsat 8 a Sentinel-2 na príklade území bývalých vojenských obvodov Ralsko a Brdy, ktoré prešli špecifickým vývojom krajinnej pokrývky. Štúdia hodnotí krajinnú pokrývku v oboch záujmových územiach k roku 2016 a na základe dosiahnutých výsledkov poukazuje na použiteľnosť vybraných dát a metód. Dosiahnuté výsledky klasifikácií krajinnej pokrývky dosahovali uspokojivé výsledky - celkové presnosti boli vyššie ako 85 %. Na základe očakávania, algoritmus SVM celkovou presnosťou prekonal algoritmus ML. Najlepšie presnosti klasifikácie dosahovali triedy vodných plôch a ihličnatých lesov, naopak, najviac chýb generovali triedy zastavané plochy, riedka vegetácia a plochy bez vegetácie. Kľúčové slová: diaľkový prieskum Zeme, Support Vector Machine, Maximum Likelihood, Česko, Sentinel-2, Landsat 8Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

Evaluation of methods and input data for land cover classification: case study of the former military areas Ralsko and Brdy

Taking advantage of Earth Observation (EO) data for monitoring land cover has attracted the attention of a broad spectrum of researchers and end-users in recent decades. The main reason of increased interest in EO can be found mainly in open data of Landsat and Sentinel archive. The main objective of this study is to evaluate the accuracy of the classification algorithms Maximum Likelihood (ML) and Support Vector Machine (SVM) using Landsat 8 and Sentinel-2 data in the case studies of the former military training areas Brdy and Ralsko, which have undergone a very specific land cover development. The study evaluates the land cover in both case studies in 2016 and based on the obtained results discussing a usefulness of the selected data and methods. The results of the land cover classification achieved satisfactory accuracy - the overall accuracy was higher than 85 %. Based on the expectation, the results of accuracy based on SVM algorithm are higher than results obtained by ML algorithm. The highest accuracy has reached in the land cover classes of water bodies and coniferous forests, on the contrary, the lowest accuracy in built-up areas, sparse vegetation and bare soil. Keywords: Earth Observation, Support Vector Machine, Maximum Likelihood, Czechia, Sentinel-2, Landsat

Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks

This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged by wind and bark beetle calamities, which strongly influenced the health state of the forest vegetation at the end of the 20th and beginning of the 21st century. The analysis of the time series was based on the ten selected vegetation indices in different types of localities selected according to the type of forest disturbances. The Landsat data CDR (Climate Data Record/Level 2) was normalized using the PIF (Pseudo-Invariant Features) method and the results of the Time Series were validated by in-situ data. The results confirmed the high relevance of the vegetation indices based on the SWIR bands (e.g., NDMI) for the purpose of evaluating the individual stages of the disturbance (especially the bark beetle calamity). Usage of the normalized Landsat data Climate Data Record (CDR/Level 2) in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability of the corrected data

Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community

Random Forest Classification of Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data—A Case Study of Czechia

Land use, land-use change and forestry (LULUCF) is a greenhouse gas inventory sector that evaluates greenhouse gas changes in the atmosphere from land use and land-use change. This study focuses on the development of a Sentinel-2 data classification according to the LULUCF requirements on the cloud-based platform Google Earth Engine (GEE). The methods are tested in selected larger territorial regions (two Czech NUTS 2 units) using data collected in 2018. The Random Forest method was used for classification. In terms of classification accuracy, a combination of these parameters was tested: The Number of Trees (NT), the Variables per Split (VPS) and the Bag Fraction (BF). A total of 450 combinations of different parameters were tested. The highest accuracy classification with an overall accuracy = 89.1% and Cohen’s Kappa = 0.84 had the following combination: NT = 150, VPS = 3 and BF = 0.1. For classification purposes, a mosaic was created using the median method. The resulting mosaic consisted of all Sentinel-2 bands in 10 and 20 m spatial resolution. Altitude values derived from SRTM and NDVI variance values were also included in the classification. These added bands were the most significant in terms of Gini importance

Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment

Thermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spatial resolution of the derived land surface temperature (LST) is limited to around 100 m. This constrains the applications of such thermal satellite sensors in which finer detail of LST spatial pattern is relevant, especially in an urban environment where the land cover structure is complex. Among the missions deployed on the Earth’s orbit, NASA’s TIRS sensor onboard Landsat 8 and Landsat 9, and ASTER onboard Terra provide the highest spatial resolution of the thermal band. On the other hand, ESA’s Sentinel-2 multispectral imagery is collected at a higher spatial resolution of 10 m with a 5-day temporal resolution, but scanning in the TIR band is not available. This study makes use of the known relationship between LST and land cover metrics, such as the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI). We define a multiple linear regression model based on the spectral indices and LST derived from Landsat 8 data to inform the same model in which the equivalent spectral indices derived from Sentinel-2 are used to predict LST at 10 m resolution. Results of this approach are demonstrated in a case study for Košice city, Slovakia, where the multiple linear model based on Landsat 8 data achieved an R2 of 0.642. The correlation between the observed Landsat 8 LST and predicted LST from Sentinel-2 aggregated to the same resolution as the observed LST was high (r = 0.91). Despite the imperfections of the downscaling model, the derived LST at 10 m resolution provides a better perception of the LST field that can be easily associated with land cover features present in urban environment. The LST downscaling approach was implemented into Google Earth Engine. It provides a user-friendly online application that can be used for any city or urban region for generating a more realistic spatial pattern of LST than can be directly observed by contemporary Earth observation satellites. The tool aids in urban decision making and planning on how to mitigate overheating of cities to improve the life quality of their citizens

Sentinel-2 data in an evaluation of the impact of the disturbances on forest vegetation

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q(2)-Q(3): 0.63-0.71; NDMI Q(2)-Q(3): 0.10-0.19) and the TCW index had negative values (Q(2)-Q(3): -0.06--0.05)). The analysis was performed with a cloud-based tool-Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.Web of Science1212art. no. 191