Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response

Casas, A.; Riaño, David; Ustin, Susan L.; Dennison, Philip E.; Salas, Julián

doi:10.1016/j.rse.2014.03.011

Cited by 106 publications

(75 citation statements)

References 111 publications

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“…7 and 8). This confirms the results in Casas et al (2014) where the CWC algorithm based on RTM inversion developed by Trombetti et al (2008) also failed to improve results from empirical estimates. Regarding the RTMbased FMC estimates, considering that the FMC inversion models were not calibrated with any data from the field campaign and that the results were similar to those obtained using empirical approach (Fig.…”

Section: Discussionsupporting

confidence: 83%

“…Similar to this study, Casas et al (2014) reliably predicted water content variables in California (USA) from GEMI, NDII and EVI using simulated MODIS spectral response from airborne hyperspectral AVIRIS instrument. In their case, VARI was actually the most accurate for grasslands (FMC and CWC), chaparral (EWT, FMC and CWC) and a Mediterranean oak forest (EWT).…”

Section: Discussionsupporting

confidence: 72%

“…Summer should be the best time of the year to invert RTM and predict Dm, since leaves are drier and therefore EWT does mask the Dm spectral absorption signal (Riaño et al, 2005). Casas et al (2014) applied a constant annual Dm value from the literature to successfully predict seasonal variations in EWT and CWC. However, our study suggests that, due to the high seasonal variation in Dm, a constant annual value would not be recommended in grassland ecosystems as the one analyzed in this work.…”

Section: Discussionmentioning

confidence: 99%

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Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones.Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R 2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

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Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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“…Moisture Stress Index (MSI) was developed and tested for remote sensing of leaf RWC [11,12]. VIs based on reflectance of NIR and SWIR regions, such as Water Index (WI) [13], Normalized Difference Water Index (NDWI) [4], and Normalized Difference Infrared Index (NDII) [14], have been tested to estimate water content for different vegetation types at both leaf level [15][16][17] and canopy level [9,15,[18][19][20]. Since GWC is determined on dry mass, the ratios of water indices and dry-matter indices were developed in order to estimate GWC [17].…”

Section: Introductionmentioning

confidence: 99%

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Liu

et al. 2015

Remote Sensing

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This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best retrieve canopy water-related properties of winter wheat. Results demonstrated that the vertical distribution of these properties followed a near-bell-shaped curve with the highest values at the intermediate leaf position. GWC of the top three or four leaves during the heading stage and the top two or three leaves during the early ripening stage can represent the GWC of the whole canopy, but the RWC and EWT of the whole canopy should be calculated based on the top four leaves. At leaf level, the analysis demonstrated strong relationships between EWT and VIs for the top leaf layer, but for GWCD, GWCF, and RWC, the strongest relationships with VIs were found in the intermediate leaf layers. At canopy level, VIs provided the most accurate estimation of GWCfor the top three or four leaves. Water absorption-based VIs could estimate canopy EWT of winter wheat for the top four leaves, but the suitable bands sensitive to water absorptions should be carefully selected for the studied species.

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“…Casas et al [44] concluded that each index is only applicable for a specific purpose and a selection and combination of indices for different biochemical parameters is important for a comprehensive analysis.…”

Section: Index Analysismentioning

confidence: 99%

Early Detection of Vitality Changes of Multi-Temporal Norway Spruce Laboratory Needle Measurements—The Ring-Barking Experiment

Reichmuth

Henning²,

Pinnel

et al. 2018

Remote Sensing

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Abstract:The focus of this analysis is on the early detection of forest health changes, specifically that of Norway spruce (Picea abies L. Karst.). In this analysis, we planned to examine the time (degree of early detection), spectral wavelengths and appropriate method for detecting vitality changes. To accomplish this, a ring-barking experiment with seven subsequent laboratory needle measurements was carried out in 2013 and 2014 in an area in southeastern Germany near Altötting. The experiment was also accompanied by visual crown condition assessment. In total, 140 spruce trees in groups of five were ring-barked with the same number of control trees in groups of five that were selected as reference trees in order to compare their development. The laboratory measurements were analysed regarding the separability of ring-barked and control samples using spectral reflectance, vegetation indices and derivative analysis. Subsequently, a random forest classifier for determining important spectral wavelength regions was applied. Results from the methods are consistent and showed a high importance of the visible (VIS) spectral region, very low importance of the near-infrared (NIR) and minor importance of the shortwave infrared (SWIR) spectral region. Using spectral reflectance data as well as indices, the earliest separation time was found to be 292 days after ring-barking. The derivative analysis showed that a significant separation was observed 152 days after ring-barking for six spectral features spread through VIS and SWIR. A significant separation was detected using a random forest classifier 292 days after ring-barking with 58% separability. The visual crown condition assessment was analysed regarding obvious changes of vitality and the first indication was observed 302 days after ring-barking as bark beetle infestation and yellowing of foliage in the ring-barked trees only. This experiment shows that an early detection, compared with visual crown assessment, is possible using the proposed methods for this specific data set. This study will contribute to ongoing research for early detection of vitality changes that will support foresters and decision makers.

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Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response

Cited by 106 publications

References 111 publications

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Early Detection of Vitality Changes of Multi-Temporal Norway Spruce Laboratory Needle Measurements—The Ring-Barking Experiment

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