Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination

: Biomass burning is a global phenomenon and systematic burned area mapping is of increasing importance for science and applications. With high spatial resolution and novelty in band design, the recently launched Sentinel-2A satellite provides a new opportunity for moderate spatial resolution burned area mapping. This study examines the performance of the Sentinel-2A Multi Spectral Instrument (MSI) bands and derived spectral indices to differentiate between unburned and burned areas. For this purpose, five pairs of pre-fire and post-fire top of atmosphere (TOA reflectance) and atmospherically corrected (surface reflectance) images were studied. The pixel values of locations that were unburned in the first image and burned in the second image, as well as the values of locations that were unburned in both images which served as a control, were compared and the discrimination of individual bands and spectral indices were evaluated using parametric (transformed divergence) and non-parametric (decision tree) approaches. Based on the results, the most suitable MSI bands to detect burned areas are the 20 m near-infrared, short wave infrared and red-edge bands, while the performance of the spectral indices varied with location. The atmospheric correction only significantly influenced the separability of the visible wavelength bands. The results provide insights that are useful for developing Sentinel-2 burned area mapping algorithms

Algorithm for detecting deforestation and forest degradation using vegetation indices

In forestry sector, the remote sensing technology hold a key role on forest inventory and monitoring their changes. This paper describes the algorithm for detecting deforestation and forest degradation using high resolution satellite imageries with knowledge-based approach. The main objective of the study is to develop a practical technique for monitoring deforestation and forest degradation occurred within the mangrove and swamp forest ecosystem.  The SPOT 4, 5, and 6 images acquired in 2007, 2012 and 2014 were transformed into three vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Green-Normalized Difference Vegetation index (GNDVI) and Normalized Green-Red Vegetation index (NRGI).  The study found that deforestation was well detected and identified using the NDVI and GNDVI, however the forest degradation could be well detected using NRGI, better than NDVI and GNDVI.  The study concludes that the strategy for monitoring deforestation, biomass-based forest degradation as well as forest growth could be done by combining the use of NDVI, GNDVI and NRGI respectively

Monitoring vegetation using remote sensing time series data: a review of the period 1996-2017

Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources.Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources

Generación de mapas de áreas quemadas a partir de imágenes Landsat 8 OLI y Sentinel 2 MSI

La respuesta temprana a emergencias es uno de los principales desafíos que enfrenta la gestión de desastres naturales. Los incendios forestales forman parte del tipo de emergencias con mayores daños a la biodiversidad implicando enormes pérdidas económicas y deben ser registrados debidamente. La información satelital ha evolucionado hacia el uso combinado de sistemas de observación que permiten acortar los tiempos de respuesta. Se presenta una metodología multitemporal basada en diferencias relativas, que combina imágenes multiespectrales Landsat-8 OLI y Sentinel-2 MSI para la delimitación de polígonos de área quemada por el incendio ocurrido en la Reserva La Calera, el 18 de Agosto de 2017, en Córdoba. La constelación Sentinel 2 realiza un aporte importante de imágenes al total disponible para una determinada región geográfica y período de tiempo. Las características similares de los sensores OLI y MSI permite un uso combinado y presenta significativos beneficios comparado al uso individual.Sociedad Argentina de Informática e Investigación Operativ

Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems

P. 137-144The development of improved spatial and spectral resolution sensors provides new opportunities to assess burn severity more accurately. This study evaluates the ability of remote sensing indices derived from three remote sensing sensors (i.e., Landsat 8 OLI/TIRS, Sentinel-2 MSI and Deimos-1 SLIM-6-22) to assess burn severity (site, vegetation and soil burn severity). As a case study, we used a megafire (9,939 ha) that occurred in a Mediterranean ecosystem in northwestern Spain. Remote sensing indices included seven reflective, two thermal and four mixed indices, which were derived from each satellite and were validated with field burn severity metrics obtained from CBI index. Correlation patterns of field burn severity and remote sensing indices were relatively consistent across the different sensors. Additionally, regardless of the sensor, indices that incorporated SWIR bands (i.e., NBR-based indices), exceed those using red and NIR bands, and thermal and mixed indices. High resolution Sentinel-2 imagery only slightly improved the performance of indices based on NBR compared to Landsat 8. The dNDVI index from Landsat 8 and Sentinel-2 images showed relatively similar correlation values to NBR-based indices for site and soil burn severity, but showed limitations using Deimos-1. In general, mono-temporal and relativized indices better correlated with vegetation burn severity in heterogeneous systems than differenced indices. This study showed good potential for Landsat 8 OLI/TIRS and Sentinel-2 MSI for burn severity assessment in fire-prone heterogeneous ecosystems, although we highlight the need for further evaluation of Deimos-1 SLIM-6-22 in different fire scenarios, especially using bi-temporal indices.S

The new hyperspectral satellite prisma: Imagery for forest types discrimination

Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups

Assessment of vegetation regrowth and spatial patterns and severity factors of wildfires in wildland-urban interface - the case of the large wildfire in Baião (2019)

Portugal is one of the countries most affected by forest fires in southern Europe, with recurrent events and frequent impacts. The demographic and social changes that have occurred in rural areas have driven land neglect in recent years, which, in turn, influences forest management and wildland-urban interface (WUI) areas that are related to fires. Therefore, it is the aim of this study to develop a case study in the municipality of Baião, based on the large wildfire (LWF) of 2019, defining and mapping the WUI areas, as well as evaluate, the recurrence, the GIF severity and in a period of 2 years, the regeneration of vegetation, in areas with different land uses and affected by different severities. The study was organized into 4 stages, being that in the first proceeded to the mapping of fire occurrences, the second of the wildland-urban interfaces, the third the characterization of the recurrence of large fires, the fourth corresponded to the evaluation of the severity of the large wildfire of 2019 and the evaluation of the vegetation regeneration, as a function of land use. The WUI represent 26.7% of the territory of the municipality of Baião, during the years 2001 to 2021 the municipality registered 3 770 fire occurrences. The LWF of Baião burned an area corresponding to 853 ha, the burned area in 2019 presented a maximum number of 12 fires between the years of 1975 to 2019, resulting in a maximum degree of 11 recurrences for the same area. We can verify that 2 years after the LWF, the area occupied by forest and scrub classes, which were hit by high severity, already showed significant levels of vegetation regeneration. With this, the main conclusions consider studies in this line contribute to the understanding of the patterns created by the wildfire in different landscapes, being information valuable for forest managers to understand the consequences (beneficial or not) and plan actions of prevention, restoration, and environmental education

Effect of Tides and Currents on UAV-Based Detection of Giant Kelp Canopy

Satellite and aerial imagery have been used extensively for mapping the abundance and distribution of giant kelp (Macrocystis pyrifera) in southern California. While tides and currents have been shown to affect the amount of floating kelp canopy on the water surface, there have been no quantifications of how these processes can bias remotely sensed kelp estimates in this region. We used unmanned aerial vehicles (UAVs) to map fine-scale changes in canopy area due to tidal height and current speed at both Palos Verdes, CA and Santa Barbara, CA. Additionally, we collected a biweekly time series of kelp canopy area in Palos Verdes over the course of a year to monitor fine-scale, intra-seasonal changes in canopy coverage. Our automated method for detecting kelp canopy in color and multispectral UAV imagery was highly accurate (over 84% and 98%, respectively) in classifying exemplary kelp and water pixels across a range of weather, ocean, and illumination conditions. Increases in tidal height of 1 m reduced the amount of floating kelp canopy by 15% to 32%. Current speeds are generally low in southern California and had no statistically significant effect on apparent bed size. However, increases in current speeds of 0.1 m/s reduced the amount of floating kelp canopy by over 31%. Tidal height and current speed can introduce significant variability to estimates of kelp abundance, but the magnitude of this variability is region specific. The biweekly time series displayed intra-annual variability typical of giant kelp, with a relatively gradual decline in kelp canopy in late summer corresponding with high sea surface temperatures, a rapid decline in the winter associated with wave disturbance, and a gradual recovery in the spring of the following year. The time series was also able to capture intra-seasonal changes in kelp canopy area that would have gone undetected in an annual or quarterly dataset, namely an increase in area to about half of the maximum in the late fall before wave events began

FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

[EN] The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts and ecosystem recovery processes, particularly in the context of extreme wildfire events that challenge land management initiatives. Wildfire data collection and analysis through remote sensing earth observations is of utmost importance for this purpose. However, the needs of land managers are not always met because the exploitation of the full potential of remote sensing techniques requires a high level of technical expertise. In addition, data acquisition and storage, database management, networking, and computing requirements may present technical difficulties. Here, we present FIREMAP software, which leverages the potential of Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), and the European Forest Fire Information System (EFFIS) wildfire database for wildfire analyses through remote sensing techniques and data collections. FIREMAP software allows automatic computing of (i) machine learning-based burned area (BA) detection algorithms to facilitate the mapping of (historical) fire perimeters, (ii) fire severity spectral indices, and (iii) post-fire recovery trajectories through the inversion of physically-based radiative transfer models. We introduce (i) the FIREMAP platform architecture and the GUI, (ii) the implementation of well-established algorithms for wildfire science and management in GEE, (iii) the validation of the algorithm implementation in fifteen case-study wildfires across the western Mediterranean Basin, and (iv) the near-future and long-term planned expansion of FIREMAP featuresSIThis study was financially supported by the Spanish Ministry of Science and Innovation in the framework of LANDSUSFIRE project (PID2022-139156OB-C21) within the National Program for the Promotion of Scientific-Technical Research (2021-2023), and with Next-Generation Funds of the European Union (EU) in the framework of the FIREMAP project (TED2021-130925B-I00); and by the Regional Government of Castile and León in the framework of the IA-FIREXTCyL project (LE081P23). Víctor Fernández-García was supported by a Margarita Salas post-doctoral fellowship from the Ministry of Universities of Spain, financed with European Union-NextGenerationEU and Ministerio de Universidades Fund