Assessment of physiological status and spatial distribution of emergent macrophytes based on imaging spectroscopy

Wetlands are ecosystems encountered at the land-water ecotone and hence inheriting rich complexity and biodiversity. Emergent macrophytes are a prime example of this variability manifested by the co-occurrence of vegetation associations at a very fine spatial level. From 1960s onwards an abrupt deterioration of reed beds in Europe has been systematically observed and denoted as the ‘reed die-back’. Since then, earth observation has been utilized mainly to map the extent of reed beds based on multispectral information. Hyperspectral remote sensing has frequently been employed in vegetation related studies, however the spectral information content of macrophytes has not been widely investigated. This study focuses on assessing the potential of imaging spectroscopy for assessing the ecophysiology of lake shore vegetation at leaf level and mapping macrophytes species associations from airborne imagery. Concurrently acquired spectroscopic, chlorophyll fluorescence and chlorophyll content information from field samples around Lake Balaton, Hungary are employed to identify spectral regions and propose narrowband indices which can aid the identification of reed ecophysiological status based solely on vegetation spectral characteristics. Macrophyte species as well as Phragmites in different phenological states have concretely separate spectral responses, however stable and die-back reed are not crucially different. Bathymetry regulates consistently the spectral response of Phragmites. Narrow band ratio 493/478 (0.65***) correlates with the ETR, the latter being an indication of the photosynthetic activity of the plant, and hence the vegetation physiological status. Most indices correlating with fluorometric parameters are located in the optical domain. Based on R2 graphs, the ratios 699/527 and 612/516 can be used to estimate Y(II) from AISA hyperspectral data. Estimation of the photophysiological parameters of a reed bed is possible based solely on airborne hyperspectral imagery. Simultaneously acquired airborne AISA Eagle, Hawk and discrete return LiDAR data are combined in order to stress out the potential of each dataset in classifying the reed bed in terms of species associations. An application of SVM on noise-reduced Eagle data, at the chlorophyll and near infrared absorption spectral regions, provides the most concrete results in terms of overall accuracy (89%). SVM outperforms ML and infrared sensors as well as LiDAR data do not improve the categorization of macrophyte classes. While airborne data inherit a superior spectral and spatial resolution, they are difficult to acquire in an operational context. Upcoming satellites will provide imagery with progressively higher spatial and spectral capabilities. A simulation of Sentinel-2 image over a reed bed in a nature protected area indicates the potential of satellite imagery in mapping macrophytes. Main classes can be distinguished, despite the fact that inter-class separability is becoming vague. Given the very large swath of Sentinel-2 (290km) an operational categorization of main macrophytes is foreseen achievable

Burn Scar Mapping in Attica, Greece using the dNBR (differenced Normalised Burn Ratio) Index on Landsat TM/ETM+ Satellite Imagery

This paper presents an attempt to map burn scars from 1984 to present around the city of Athens, Greece from a remote sensing perspective. Fine spatial resolution Landsat TM/ETM+ imagery was used favoured by an extensive available archive. The input data were processed based on a methodology integrating the bi-temporal differenced Normalised Burn Ratio (dNBR) index and a fixed thresholding technique. Three major scars have been detected in the years 1985, 2007 and 2009. The total burnt area was estimated 19,607ha during the periods 1984-1991 and 2003-2009. Accuracy assessment was carried out with reference to a highly accurate validated source. High levels of accuracy were attributed principally to the dNBR index performance. This study comes to underpin the strong capabilities of the dNBR index in burnt area mapping and confirms the suitability of the methodology for applications in Mediterranean climate regions

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails

Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s

The Corona satellite program was a historic reconnaissance mission which provided high spatial resolution panchromatic images during the Cold War era. Nevertheless, and despite the historic uniqueness and importance of the dataset, efforts to extract tangible information from this dataset have primarily focused on visual interpretation. More sophisticated approaches have been either hampered or unrealized, often justified by the primitive quality of this early satellite product. In the current study we attempt to showcase the usability of Corona imagery outside the context of visual interpretation. Using a 1968 Corona image acquired over the city municipality of Plovdiv, Bulgaria, we reconstruct a panchromatic 1.8 m spatial resolution georegistered image with a relative displacement Root Mean Square Error (RMSE) of 6.616 (for x dimension) and 1.886 (for y dimension) and employ segmentation and texture analysis to discern agricultural parcels and settlements’ footprints. Population statistics of this past era are retrieved from national census and related to settlements’ footprints. An exponential relationship between the two variables was identified by applying a semi-log regression. The high adjusted R2 value found (76.54%) indicates that Corona images offer a unique opportunity for population data analysis of the past. Overall, we showcase that the Corona images’ usability extends beyond the visual interpretation, and features of interest extracted through image analysis can be subsequently used for further geographical and historical research

A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand

Forest fires are abrupt transformations of the natural ecosystem and management authorities are required to take preventive measures to tackle fire events. Geographic information system (GIS) is a powerful tool for providing information with a spatial context and analytical hierarchy process (AHP) is a well-established technique for multiple criteria decision making. In this study, GIS and AHP are combined to analyse seven fire-related factors related to climate, topography and human influence. Fire risk for a peat swamp forested area in Kuan Kreng, Nakorn Sri Thammarat province, Thailand is estimated in five categories. 705 historic fire events from 2006 to 2017 are used to validate our approach. 82% of the historic fire incidents occurred within the highest fire risk class categories while only a few omission errors were recorded. The combined approach of GIS and AHP techniques can yield useful fire risk maps, which can consequently be used for future planning and management of fire prone areas

Assessing the Spectral Information of Sentinel-1 and Sentinel-2 Satellites for Above-Ground Biomass Retrieval of a Tropical Forest

Earth Observation (EO) spectral indices have been an important tool for quantifying and monitoring forest biomass. Nevertheless, the selection of the bands and their combination is often realized based on preceding studies or generic assumptions. The current study investigates the relationship between satellite spectral information and the Above Ground Biomass (AGB) of a major private forest on the island of Java, Indonesia. Biomass-related traits from a total of 1517 trees were sampled in situ and their AGB were estimated from species-specific allometric models. In parallel, the exhaustive band combinations of the Ratio Spectral Index (RSI) were derived from near-concurrently acquired Sentinel-1 and Sentinel-2 images. By applying scenarios based on the entire dataset, the prevalence and monodominance of acacia, mahogany, and teak tree species were investigated. The best-performing index for the entire dataset yielded R2 = 0.70 (R2 = 0.78 when considering only monodominant plots). An application of eight traditional vegetation indices provided, at best, R2 = 0.65 for EVI, which is considerably lower compared to the RSI best combination. We suggest that an investigation of the complete band combinations as a proxy of retrieving biophysical parameters may provide more accurate results than the blind application of popular spectral indices and that this would take advantage of the amplified information obtained from modern satellite systems

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM_2.5 Concentrations Nationwide over Thailand

Atmospheric pollution has recently drawn significant attention due to its proven adverse effects on public health and the environment. This concern has been aggravated specifically in Southeast Asia due to increasing vehicular use, industrial activity, and agricultural burning practices. Consequently, elevated PM2.5 concentrations have become a matter of intervention for national authorities who have addressed the needs of monitoring air pollution by operating ground stations. However, their spatial coverage is limited and the installation and maintenance are costly. Therefore, alternative approaches are necessary at national and regional scales. In the current paper, we investigated interpolation models to fuse PM2.5 measurements from ground stations and satellite data in an attempt to produce spatially continuous maps of PM2.5 nationwide over Thailand. Four approaches are compared, namely the inverse distance weighted (IDW), ordinary kriging (OK), random forest (RF), and random forest combined with OK (RFK) leveraging on the NO2, SO2, CO, HCHO, AI, and O3 products from the Sentinel-5P satellite, regulatory-grade ground PM2.5 measurements, and topographic parameters. The results suggest that RFK is the most robust, especially when the pollution levels are moderate or extreme, achieving an RMSE value of 7.11 μg/m3 and an R2 value of 0.77 during a 10-day long period in February, and an RMSE of 10.77 μg/m3 and R2 and 0.91 during the entire month of March. The proposed approach can be adopted operationally and expanded by leveraging regulatory-grade stations, low-cost sensors, as well as upcoming satellite missions such as the GEMS and the Sentinel-5

Correction: Han et al. Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand. Atmosphere 2022, 13, 161

In the original publication [...

Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation

Above-ground Biomass (AGB) represents the largest amount of biomass found on earth. Passive and active remote sensors have been a useful tool in estimating AGB for this purpose; nevertheless, both data sources suffer from saturation problems in dense vegetation. A combination of optical and radar data could potentially increase the accuracy of AGB estimation. In this study we evaluate the synergistic use of Sentinel-1 and Sentinel-2 for assessing AGB in a private forest in Yogyakarta, Indonesia. Forty five sample plots of 20 m x 20 m were used as ground truth data. AGB correlated with Sentinel-1 backscatter and Sentinel-2 derived variables with R2 = 0.34 and R2 = 0.82, respectively; nevertheless, the synergistic use of Sentinel-1 and Sentinel-2 yielded the highest accuracy (i.e., R2 = 0.84). The results indicate that AGB in Yogyakarta is most accurately estimated based on the synergy of optical and radar satellite images