scholarly journals Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV

2019 ◽  
Vol 11 (9) ◽  
pp. 993 ◽  
Author(s):  
Fernando Carvajal-Ramírez ◽  
José Rafael Marques da Silva ◽  
Francisco Agüera-Vega ◽  
Patricio Martínez-Carricondo ◽  
João Serrano ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Fire Severity ◽  
Matrix Analysis ◽  
Surface Model ◽  
Chi Square ◽  
Error Matrix ◽  
Red Edge ◽  
The Impact

Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications.

scholarly journals Urban Fire Severity and Vegetation Dynamics in Southern California

2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Lauren E. H. Mathews ◽  
Alicia M. Kinoshita
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Current Knowledge ◽  
Fire Severity ◽  
Satellite Image ◽  
Burn Severity ◽  
Native Vegetation ◽  
Riparian Areas ◽  
The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

scholarly journals Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 25
Author(s):  
Antoine Mury ◽  
Antoine Collin ◽  
Thomas Houet ◽  
Emilien Alvarez-Vanhard ◽  
Dorothée James
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
Crop Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Wave Attenuation ◽  
Coefficient Of Determination ◽  
Coastal Protection ◽  
Surface Model ◽  
Red Edge

Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm0) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R2): 0.85, root mean square error (RMSE): 0.20%/m).

scholarly journals A STUDY OF THE IMPACT OF INSOLATION ON REMOTE SENSING-BASED LANDCOVER AND LANDUSE DATA EXTRACTION

2016 ◽  
Vol XLI-B7 ◽  
pp. 65-69 ◽  
Author(s):  
K. Becek ◽  
A. Borkowski ◽  
Ç. Mekik
Keyword(s):  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hyperspectral Image ◽  
Data Extraction ◽  
Technical Committee ◽  
Surface Model ◽  
Imaging Spectrometer ◽  
The Impact ◽  
Spectrometer Data

We examined the dependency of the pixel reflectance of hyperspectral imaging spectrometer data (HISD) on a normalized total insolation index (NTII). The NTII was estimated using a light detection and ranging (LiDAR)-derived digital surface model (DSM). The NTII and the pixel reflectance were dependent, to various degrees, on the band considered, and on the properties of the objects. The findings could be used to improve land cover (LC)/land use (LU) classification, using indices constructed from the spectral bands of imaging spectrometer data (ISD). To study this possibility, we investigated the normalized difference vegetation index (NDVI) at various NTII levels. The results also suggest that the dependency of the pixel reflectance and NTII could be used to mitigate the shadows in ISD. This project was carried out using data provided by the Hyperspectral Image Analysis Group and the NSF-funded Centre for Airborne Laser Mapping (NCALM), University of Houston, for the purpose of organizing the 2013 Data Fusion Contest (IEEE 2014). This contest was organized by the IEEE GRSS Data Fusion Technical Committee.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

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

2019 ◽  
Vol 11 (21) ◽  
pp. 2579 ◽  
Author(s):  
Fernando Carvajal-Ramírez ◽  
João Manuel Pereira Ramalho Serrano ◽  
Francisco Agüera-Vega ◽  
Patricio Martínez-Carricondo
Keyword(s):  
Forest Fires ◽  
Point Cloud ◽  
Vegetation Index ◽  
Matrix Analysis ◽  
Mediterranean Forest ◽  
Estimation Methods ◽  
Surface Model ◽  
Error Matrix ◽  
Vegetation Height ◽  
Dense Point

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.

scholarly journals Unpiloted Aerial System (UAS)-Supported Biogeomorphic Analysis of Restored Sierra Nevada Montane Meadows

2020 ◽  
Vol 12 (11) ◽  
pp. 1828
Author(s):  
Jerry Davis ◽  
Leonhard Blesius ◽  
Michelle Slocombe ◽  
Suzanne Maher ◽  
Michael Vasey ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Cross Sections ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Survey Methods ◽  
Vegetation Indices ◽  
Field Studies ◽  
Red Edge ◽  
Montane Meadows

The benefits of meadow restoration can be assessed by understanding the connections among geomorphology, hydrology, and vegetation; and multispectral imagery captured from unpiloted aerial systems (UASs) can provide the best method in terms of cost, resolution, and support for vegetation indices. Our field studies were conducted on northern Sierra montane meadows (with ≤70 km2 watershed area). The meadows exist in various stages of ecological restoration. Field survey methods included GPS + laser-leveling channel survey, cross-sections, LiDAR, vegetation sampling, soil measurements, and UAS imaging. A sensor captured calibrated blue (465–485 nm), green (550–570 nm), red (663–673 nm), near infrared (NIR) (820–860 nm), and red-edge (712–722 nm) bands at 5.5 cm resolution (as well as thermal at 81 cm resolution) and provided multispectral images and derivative vegetation indices such as the normalized difference vegetation index (NDVI) and red-edge chlorophyll index (Clre). This fine-scale imagery extended our morphometric assessment of post-restoration channel bedform patterns and sinuosity related to Carex-influenced soil properties and Salix influence, and also documented groundwater-related effects via Carex patterns evident from spring snowmelt images, as well as NDVI and Clre (derived from spring and summer images) in growing to senescent phenological stages. Carex was significantly associated with low bulk density and high soil moisture, NDVI, and Clre in low-lying areas, and channel sinuosity was significantly associated with willow influence. Our methods can be applied by restoration managers to assess where projects are threatened by renewed incision and to document levels of carbon sequestration significant to addressing climate change.

scholarly journals Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

2019 ◽  
Vol 11 (10) ◽  
pp. 1192 ◽  
Author(s):  
Nianxu Xu ◽  
Jia Tian ◽  
Qingjiu Tian ◽  
Kaijian Xu ◽  
Shaofei Tang
Keyword(s):  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Bare Soil ◽  
Vegetation Canopy ◽  
Vegetation Monitoring ◽  
Red Edge ◽  
Positive Correlations

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

scholarly journals An Empirical Assessment of Angular Dependency for RedEdge-M in Sloped Terrain Viticulture

2019 ◽  
Vol 11 (21) ◽  
pp. 2561
Author(s):  
Chizhang Gong ◽  
Henning Buddenbaum ◽  
Rebecca Retzlaff ◽  
Thomas Udelhoven
Keyword(s):  
Vegetation Index ◽  
3D Model ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Surface Model ◽  
Resolution Limit ◽  
Angular Correction ◽  
Aerial Vehicle ◽  
Angular Dependency

For grape canopy pixels captured by an unmanned aerial vehicle (UAV) tilt-mounted RedEdge-M multispectral sensor in a sloped vineyard, an in situ Walthall model can be established with purely image-based methods. This was derived from RedEdge-M directional reflectance and a vineyard 3D surface model generated from the same imagery. The model was used to correct the angular effects in the reflectance images to form normalized difference vegetation index (NDVI) orthomosaics of different view angles. The results showed that the effect could be corrected to a certain scope, but not completely. There are three drawbacks that might restrict a successful angular model construction and correction: (1) the observable micro shadow variation on the canopy enabled by the high resolution; (2) the complexity of vine canopies that causes an inconsistency between reflectance and canopy geometry, including effects such as micro shadows and near-infrared (NIR) additive effects; and (3) the resolution limit of a 3D model to represent the accurate real-world optical geometry. The conclusion is that grape canopies might be too inhomogeneous for the tested method to perform the angular correction in high quality.

scholarly journals A STUDY OF THE IMPACT OF INSOLATION ON REMOTE SENSING-BASED LANDCOVER AND LANDUSE DATA EXTRACTION

2016 ◽  
Vol XLI-B7 ◽  
pp. 65-69
Author(s):  
K. Becek ◽  
A. Borkowski ◽  
Ç. Mekik
Keyword(s):  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hyperspectral Image ◽  
Data Extraction ◽  
Technical Committee ◽  
Surface Model ◽  
Imaging Spectrometer ◽  
The Impact ◽  
Spectrometer Data

We examined the dependency of the pixel reflectance of hyperspectral imaging spectrometer data (HISD) on a normalized total insolation index (NTII). The NTII was estimated using a light detection and ranging (LiDAR)-derived digital surface model (DSM). The NTII and the pixel reflectance were dependent, to various degrees, on the band considered, and on the properties of the objects. The findings could be used to improve land cover (LC)/land use (LU) classification, using indices constructed from the spectral bands of imaging spectrometer data (ISD). To study this possibility, we investigated the normalized difference vegetation index (NDVI) at various NTII levels. The results also suggest that the dependency of the pixel reflectance and NTII could be used to mitigate the shadows in ISD. This project was carried out using data provided by the Hyperspectral Image Analysis Group and the NSF-funded Centre for Airborne Laser Mapping (NCALM), University of Houston, for the purpose of organizing the 2013 Data Fusion Contest (IEEE 2014). This contest was organized by the IEEE GRSS Data Fusion Technical Committee.

Evaluating the near and mid infrared bi-spectral space for assessing fire severity and comparison with the differenced normalized burn ratio

2021 ◽  
Author(s):  
Max van Gerrevink ◽  
Sander Veraverbeke
Keyword(s):  
Spectral Index ◽  
Field Data ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Fire Regime ◽  
Fire Severity ◽  
Spectral Space ◽  
Mid Infrared ◽  
Fire Emissions

&lt;p&gt;Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burned ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index (dNDVI&lt;sub&gt;MID&lt;/sub&gt;), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER (MASTER) instrument. The field data consists of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR-SWIR combination and three with a NIR-MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. Results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVI&lt;sub&gt;MID&lt;/sub&gt; approaches. The results yielded an R&lt;sup&gt;2&lt;/sup&gt; of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVI&lt;sub&gt;MID &lt;/sub&gt;indices was clearly lower with an R&lt;sup&gt;2&lt;/sup&gt; = 0.61 for the best performing dNDVI&lt;sub&gt;MID &lt;/sub&gt;index. The dNBR also outperformed the dNDVI&lt;sub&gt;MID&lt;/sub&gt; in terms of spectral optimality across both fires. The best performing dNBR index yielded the optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVI&lt;sub&gt;MID, &lt;/sub&gt;index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVI&lt;sub&gt;MID&lt;/sub&gt; approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVI&lt;sub&gt;MID&lt;/sub&gt;&amp;#160;approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity.&amp;#160;&lt;/p&gt;

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