Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images

The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas

Land Surface Temperature (LST) estimated from Landsat images: applications in burnt areas and tree-grass woodlands (dehesas)

A lo largo de los últimos 40 años, las diferentes misiones del proyecto Landsat han proporcionado una gran cantidad de información espectral sobre la superficie terrestre. Las imágenes obtenidas por estos satélites se caracterizan por una resolución espacial de tipo medio, bandas espectrales situadas en diferentes regiones del espectro electromagnético (ópticas y térmicas) y una amplia cobertura terrestre. Si bien las bandas del óptico han sido utilizadas con éxito en numerosas aplicaciones, el uso del térmico ha sido mucho más limitado, a pesar de la gran importancia que representa el parámetro de la temperatura de superficie para numerosas aplicaciones ambientales, especialmente para aquellas relacionadas con la modelización de los flujos de energía en el sistema suelo-vegetación-atmósfera y con el cambio global. En este contexto, el objetivo principal de la presente investigación es explorar el potencial de la temperatura de superficie terrestre (siglas en inglés - LST), derivada de imágenes Landsat, en el estudio de ecosistemas heterogéneos, concretamente (i) áreas afectadas por los incendios forestales y (ii) ecosistemas de dehesa,formaciones constituidas por los árboles dispersos y pastizal/cultivos. En primer lugar, en el marco del proyecto BIOSPEC “Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of Global Change” (http://www.lineas.cchs.csic.es/biospec) se comparan las diferentes metodologías disponibles para la estimación de la LST a partir de la banda térmica de Landsat. Los mejores resultados, en condiciones atmosféricas caracterizadas por niveles medios de contenido de vapor, se obtuvieron usando el método mono-banda (en inglés - SingleChannel) (Jiménez-Muñoz et al., 2009), con un error de estimación <1º K. En el siguiente paso de la investigación la información sobre la distribución de LST derivada del sensor Thematic Mapper se utilizó en el análisis de la severidad del fuego en una zona forestal de Las Hurdes(Extremadura, España), y en el estudio de los efectos ocasionados por los diferentes tratamientos post-incendio en una zona quemada, esta vez localizada en los Montes de Zuera (Zaragoza, España). En relación con la severidad del fuego analizada en diferentes fechas post-incendio, se han detectado diferencias estadísticamente significativas entre los valores de LST correspondientes a las categorías de severidad establecidas a partir del índice espectral ΔNBR (Key y Benson, 2006).Los niveles de LST más elevados se observaron en las zonas donde la severidad del fuego fue mayor, debido a la menor emisividad de los productos de combustión y los cambios en el balance de energía relacionados con la ausencia de vegetación. En cuanto a las consecuencias de los tratamientos de madera quemada en la regeneración vegetal, se han observado diferencias estadísticamente significativas entre las áreas intervenidas y no intervenidas. En este sentido, en las áreas no intervenidas se registraron valores de LST ~1 K más bajos y niveles de recubrimiento vegetal ~10% más altos que en las intervenidas. En otro ámbito de aplicación, los datos de LST obtenidos mediante imágenes de Landsat-5 TM (período 2009-2011), se utilizaron en el análisis de los patrones espacio-temporales de la LST y su relación con el grado de ocupación de la fracción arbórea en ecosistemas de dehesa. Se ha detectado una relación negativa entre la LST y la cobertura arbórea, con diferencias a nivel estacional debido al dinamismo del ciclo fenológico del pastizal

Thermally enhanced spectral indices to discriminate burn severity in Mediterranean forest ecosystems

P. 1-8Fires are a problematic and recurrent issue in Mediterranean forest ecosystems. Accurate discrimination of burn severity level is fundamental for the rehabilitation planning of affected areas. Though fieldwork is still necessary for measuring post-fire burn severity, remote sensing based techniques are being widely used to predict it because of their computational simplicity and straightforward application. Among them, spectral indices classification (especially difference Normalized Burn Ratio–dNBR- based ones) may be considered the standard remote sensing based method to distinguish burn severity level. In this work we show how this methodology may be improved by using land surface temperature (LST) to enhance the standard spectral indices. We considered a large wildfire in August 2012 in North Western Spain. The Composite Burn Index (CBI) was measured in 111 field plots and grouped into three burn severity levels. Relationship between Landsat 7 Enhanced Thematic Mapper (ETM+) LST-enhanced spectral indices and CBI was evaluated by using the normalized distance between two burn severity levels and spectral dispersion graphs. Inclusion of LST in the spectral index equation resulted in higher discrimination between burn severity levels than standard spectral indices (0.90, 8.50, and 17.52 NIR-SWIR Temperature version 1 vs 0.60, 2.83, and 6.46 NBR). Our results demonstrate the potential of LST for improving burn severity discrimination and mapping. Future research, however, is needed to evaluate the performance of the proposed LST-enhanced spectral indices in other fire regimes, and forest ecosystems.S

HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%

Geographical Information System (GIS) and Remote Sensing (RS) Applications in Disaster Risk Reduction (DRR) in Malaysia

In a world today that is highly dependent on information technologies, Geographical Information System (GIS) and Remote Sensing (RS) has become one of advancement in spatial technologies that had been used to tackle the issue of an uncertain world. Primarily functioned with specialized capabilities in manipulating, analyzing, and visualizing the massive data from multiagencies, has opened new avenues for these technologies to be adopted in disaster management. Taking this into consideration, it leads to achieving disaster management objectives in Disaster Risk Reduction (DRR) which to reduce or minimize the exposure to hazards, lessened vulnerability of people and property, wise emergency preparation, and enhanced preparedness for an unfavourable situation. This paper aims to make a systematic review of literature in highlighting the significant potential of the GIS and remote sensing, integrating the aspect relevant to disasters socially and physically that helps in forming a comprehensive disaster management operations to reduce the vulnerability and strengthen the resilience to disaster. Accordingly, the paper has presented the outcomes and review of several researchers concerning the implementation of GIS and remote sensing in disaster management, specifically on disaster risk reduction. &nbsp

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

Geographical Information System (GIS) and Remote Sensing (RS) Applications in Disaster Risk Reduction (DRR) in Malaysia

In a world today that is highly dependent on information technologies, Geographical Information System (GIS) and Remote Sensing (RS) has become one of advancement in spatial technologies that had been used to tackle the issue of an uncertain world. Primarily functioned with specialized capabilities in manipulating, analyzing, and visualizing the massive data from multiagencies, has opened new avenues for these technologies to be adopted in disaster management. Taking this into consideration, it leads to achieving disaster management objectives in Disaster Risk Reduction (DRR) which to reduce or minimize the exposure to hazards, lessened vulnerability of people and property, wise emergency preparation, and enhanced preparedness for an unfavourable situation. This paper aims to make a systematic review of literature in highlighting the significant potential of the GIS and remote sensing, integrating the aspect relevant to disasters socially and physically that helps in forming a comprehensive disaster management operations to reduce the vulnerability and strengthen the resilience to disaster. Accordingly, the paper has presented the outcomes and review of several researchers concerning the implementation of GIS and remote sensing in disaster management, specifically on disaster risk reduction. &nbsp

Variabilidad espacio-temporal de la temperatura de superficie en ecosistemas de dehesa estimada mediante imágenes Landsat TM: el papel del arbolado

Las dehesas son sistemas agro-forestales en los que se producen complejos mecanismos de intercambio de carbono y agua debido a la presencia de estratos de vegetación con comportamiento eco-fisiológico contrastado: arbolado/herbáceo. Una de las variables clave en la parametrización del balance energético en estos ecosistemas es la temperatura de superficie (Ts). Este trabajo analiza su variación espacio-temporal en función de la cobertura arbórea en una dehesa al norte de Cáceres. La Ts se obtiene a partir de una serie de 14 imágenes Landsat-5 TM (2009-2011) que se agrupan en 3 compuestos estacionales (primavera, verano y otoño). La cobertura arbórea se estima a partir de ortofotografía e información del SIOSE. La distribución espacial de la Ts se relaciona con los niveles de cobertura en los compuestos de otoño y, especialmente, en verano momento en el que las diferencias medias entre las categorías extremas de arbolado (60%) alcanzan los 2,5°C

Assessing fire severity in semi-arid environments: application in Donceles 2012 wildfire (SE Spain)

Revista oficial de la Asociación Española de Teledetección[EN] Post-fire management should be based on a proper evaluation of fire damage (burn severity), mainly for Large Fires (>500 ha). Several methodologies have been developed based on remote sensing information validated with fieldwork. The most widespread techniques was the assessment of fire severity indices obtained from remote sensing. It allow a quick assessment of large areas at affordable costs, although the analysis of soil burn severity and the degree of agreement with the ground truth is not fully reliable. Our study case was the Donceles fire (summer 2012, Hellín, Albacete). The post-fire restoration planning, emergency actions, was based on cartographic information of burn severity. To optimize results in a short time and low budget, we applied methodologies in a similar way other similar fires in the Mediterranean peninsular area. We assessed burn severity by using spectral indices (NDVI, dNBR, RdNBR and RBR) and images from Landsat-7 (including banded) and Deimos-1. For each index, we developed both supervised and unsupervised classifications, using field data as training areas. The highest overall reliability values were found for dNBR (79%) and NBR (71%), obtaining low values with RdNBR. In all cases, the reliability was higher using the supervised classification, so using real-ground data to identify the categories of severity to be discriminated. We conclude the need to extend fire studies in our area to improve the reliability of the fire severity assessment obtained from spectral indexes, thus establishing a protocol of data collection and standard methodology of calculation adapted to the characteristics of the region.[ES] Para una correcta gestión de zonas afectadas por incendio forestal es necesaria una correcta evaluación del daño ocasionado, especialmente en Grandes Incendios Forestales (>500 ha), donde es necesario priorizar la utilización de recursos disponibles en la restauración. Una de las técnicas más difundidas es la aplicación de índices de severidad obtenidos de imágenes de satélite, que permite realizar rápidas evaluaciones sobre extensas superficies a bajo coste, aunque su fiabilidad es todavía cuestionada en el análisis de la severidad de quemado de suelo y el grado de acuerdo con la verdad terreno. Para el caso del incendio de Donceles (verano 2012, Hellín, Albacete), en la planificación de medidas urgentes de restauración post-incendio se usó información cartográfica de severidad a nivel cuenca y microcuenca. Se usaron imágenes procedentes de satélites de acceso gratuito e inmediato y, para optimizar resultados, se aplicaron metodologías de cálculo validadas en otros incendios similares en el ámbito mediterráneo peninsular. Se calcularon índices espectrales de uso extendido en el cálculo de la severidad del fuego (NDVI, dNBR, RdNBR y RBR) a partir de imágenes Landsat-7 y Deimos-1. Para cada índice, se generaron clasificaciones supervisadas y no supervisadas de los valores obtenidos, integrando datos de campo en la fase de entrenamiento. Los valores de fiabilidad global más altos se obtuvieron con el índice dNBR (79%) y RBR (71%), destacando los bajos resultados del RdNBR, siendo siempre mayores con clasificación supervisada y empleo de datos verdad-terreno para identificar las categorías de severidad a discriminar. Se concluye la necesidad de ampliar los estudios en incendios de la zona para mejorar la fiabilidad de la cartografía de la severidad obtenida a partir de índices espectrales, estableciendo con ello un protocolo de toma de datos y de metodología estándar de cálculo adaptado a las características de la región.Agradecemos el apoyo y la ayuda realizada por el Servicio Forestal del Gobierno Regional de la Junta de Comunidades de Castilla-La Mancha y de la Comunidad de Murcia. 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Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE