A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape

Mallinis, Giorgos; Galidaki, Georgia; Gitas, Ioannis Z.

doi:10.3390/rs6021684

Cited by 43 publications

(22 citation statements)

References 82 publications

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“…The terrain of the study area is diverse and very rough at places as a result of high difference in altitude, ranging between 320 and 1200 m. The Mediterranean climate of the area is characterized by short periods of drought, hot summers and mild winters. Main characteristic of the climate is the large fluctuations of rainfall during summer as well as the double dry season (July and September) with limited duration and intensity [50].…”

Section: Study Areamentioning

confidence: 99%

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

et al. 2014

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This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral's spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

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Section: Study Areamentioning

confidence: 99%

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

et al. 2014

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“…The climate of the study area is characterized as Mediterranean with short periods of drought, hot summers and mild winters. The main characteristics of the climate are the large fluctuations of rainfall during winter (November-December) and summer (MayJune) and the two dry seasons in July and September [34]. The common forest species of the deciduous forest of the study area are Italian oak (Quercus frainetto), Calabrian pine (Pinus brutia), Black pine (Pinus nigra), Beech (Fagus sylvatica), and Norway spruce (Pice abies) [35].…”

Section: Study Areamentioning

confidence: 99%

“…There are patches within the forest which are covered with maquis, low vegetation and scattered oak trees. Besides, there are areas resulted from the abandonment of agricultural land with various regeneration patterns [34].The forested areas have an approximate average tree height of 16m. Figure 1.…”

Section: Study Areamentioning

confidence: 99%

Forest canopy height estimation using double-frequency repeat pass interferometry

Karamvasis

Karathanassi

2015

SPIE Proceedings

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In recent years, many efforts have been made in order to assess forest stand parameters from remote sensing data, as a mean to estimate the above-ground carbon stock of forests in the context of the Kyoto protocol. Synthetic aperture radar interferometry (InSAR) techniques have gained traction in last decade as a viable technology for vegetation parameter estimation. Many works have shown that forest canopy height, which is a critical parameter for quantifying the terrestrial carbon cycle, can be estimated with InSAR. However, research is still needed to understand further the interaction of SAR signals with forest canopy and to develop an operational method for forestry applications. This work discusses the use of repeat pass interferometry with ALOS PALSAR (L band) HH polarized and COSMO Skymed (X band) HH polarized acquisitions over the Taxiarchis forest (Chalkidiki, Greece), in order to produce accurate digital elevation models (DEMs) and estimate canopy height with interferometric processing. The effect of wavelength-dependent penetration depth into the canopy is known to be strong, and could potentially lead to forest canopy height mapping using dual-wavelength SAR interferometry at X-and L-band. The method is based on scattering phase center separation at different wavelengths. It involves the generation of a terrain elevation model underneath the forest canopy from repeat-pass L-band InSAR data as well as the generation of a canopy surface elevation model from repeat pass X-band InSAR data. The terrain model is then used to remove the terrain component from the repeat pass interferometric X-band elevation model, so as to enable the forest canopy height estimation. The canopy height results were compared to a field survey with 6.9 m root mean square error (RMSE). The effects of vegetation characteristics, SAR incidence angle and view geometry, and terrain slope on the accuracy of the results have also been studied in this work.

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“…Artificial neural network (ANN) models incorporating radar data performed better in estimating phytomass than ANN models, using only optical vegetation indices with r-squared values of 0.79 and 0.6, respectively [18]. Other studies centred on multispectral satellite imagery were carried out by [19], who used height and diameter data and a classification of cover fraction based on Quickbird and Landsat imagery for mapping fuel type. A comparison of eight spectral indices of vegetation based on MODIS imagery for fire prediction purposes was undertaken by [20].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

et al. 2019

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Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way.

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A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape

Cited by 43 publications

References 82 publications

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Forest canopy height estimation using double-frequency repeat pass interferometry

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

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