Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission

Roberts, Dar A.; Quattrochi, Dale A.; Hulley, Glynn; Hook, Simon J.; Green, Robert O.

doi:10.1016/j.rse.2011.07.021

Cited by 188 publications

(102 citation statements)

References 49 publications

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“…We found that biweekly 534 sampling of leaf traits and spectra would provide a robust PLSR model to estimate the seasonal 535 variations of leaf traits. This work demonstrated the capability of leaf spectra to track seasonally-536 variable leaf traits, and thus supports the use of automated field spectrometers, airborne and 537 satellite hyperspectral sensors to track leaf traits repeatedly throughout the season and across 538 broad regions (Roberts et al 2012;Singh et al, 2015;Yang et al 2015). 539…”

Section: The Implications For Field Sampling Strategy 489mentioning

confidence: 54%

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Yang

Tang

Mustard

et al. 2016

Remote Sensing of Environment

137

109

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31Understanding the temporal patterns of leaf traits is critical in determining the seasonality 32 and magnitude of terrestrial carbon and water fluxes. However, robust and efficient ways 33 to monitor the temporal dynamics of leaf traits are lacking. Here we assessed the 34 potential of using leaf spectroscopy to predict leaf traits across their entire life cycle, 35 forest sites, and light environments (sunlit vs. shaded) using a weekly sampled dataset 36 across the entire growing season at two temperate deciduous forests. The dataset includes 37 field measured leaf-level directional-hemispherical reflectance/transmittance together 38 with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, 39 mass-based nitrogen concentration (N mass ), mass-based carbon concentration (C mass ), and 40 leaf mass per area (LMA)]. All leaf properties, including leaf traits and spectra, varied 41 significantly throughout the growing season, and displayed trait-specific temporal 42 patterns. We used a Partial Least Square Regression (PLSR) analysis to estimate leaf 43 traits from spectra, and found a significant capability of PLSR to capture the variability 44 across time, sites, and light environment of all leaf traits investigated (R 2 =0.6~0.8 for 45 temporal variability; R 2 =0.3~0.7 for cross-site variability; R 2 =0.4~0.8 for variability from 46 light environments). We also tested alternative field sampling designs and found that for 47 most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate 48 characterization of the leaf trait seasonal patterns. Increasing the sampling frequency 49 improved in the estimation of N mass , C mass and LMA comparing with foliar pigments. Our 50 results, based on the comprehensive analysis of spectra-trait relationships across time, 51 sites and light environments, highlight the capacity and potential limitations to use leaf 52 3 spectra to estimate leaf traits with strong seasonal variability, as an alternative to time-53 consuming traditional wet lab approaches. 54 4

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Section: The Implications For Field Sampling Strategy 489mentioning

confidence: 54%

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Yang

Tang

Mustard

et al. 2016

Remote Sensing of Environment

137

109

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“…The image was acquired on 26 August 2009, 3 months after the May 2009 Jesusita fire. Modtran radiative transfer code and a ground reference target allowed to correct the image atmospheri cally and converted it to reflectance (see Herold et al, 2004;Roberts et al, 2012).…”

Section: Ancillary Datamentioning

confidence: 99%

“…For each pixel, the performance of all models was assessed by considering fraction images, the shade fraction image and RMSE. Similar to Powell et al (2007); Quintano et al (2013); Roberts et al (2003), and Roberts et al (2012), we made use of the following selection criteria: − 0.05 and 1.05, as the minimum and maximum permissible fraction values; 0.8 as maximum permissi ble shade fraction value, and 0.025 as maximum permissible RMSE. If several models satisfied these conditions, the model that generated the lowest RMSE was chosen.…”

Section: Mesma Proceduresmentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999-2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km 2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forests.

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“…In this study, the water boundaries were extracted from high-precision Google Earth TM images and applied for the reference water fraction map generation, using a 10ˆ10 grids for each pixel for the test images (see Figure 5). As for reducing the impact of the geometric error between the Landsat OLI images and the reference images, a 90ˆ90 m 2 sampling size (3ˆ3 pixels size for the OLI image) was adopted for both the reference water maps and the estimated water maps [34][35][36][37].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…Accounting for spectral variability when selecting endmembers has been pointed out to be an important factor that can severely affect the accuracy of subpixel land-cover fractions [22,35,36,48]. Traditional SMA uses an invariant set of endmembers and it is quite limited when mapping heterogeneous urban surfaces [49][50][51].…”

Section: Endmember Selectionmentioning

confidence: 99%

Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery

Xie

Luo

et al. 2016

Remote Sensing

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Abstract:Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; however, when applied to urban areas, this spatially-explicit approach is a challenging task due to the fact that the water bodies are often of a small size and spectral confusion is common between water and the complex features in the urban environment. Water indexes are the most common method of water extraction at the pixel level. More recently, spectral mixture analysis (SMA) has been widely employed in analyzing the urban environment at the subpixel level. The objective of this study is to develop an automatic subpixel water mapping method (ASWM) which can achieve a high accuracy in urban areas. Specifically, we first apply a water index for the automatic extraction of mixed land-water pixels, and the pure water pixels that are generated in this process are exported as the final result. Secondly, the SMA technique is applied to the mixed land-water pixels for water abundance estimation. As for obtaining the most representative endmembers, we propose an adaptive iterative endmember selection method based on the spatial similarity of adjacent ground surfaces. One classical water index method (the modified normalized difference water index (MNDWI)), a pixel-level target detection method (constrained energy minimization (CEM)), and two widely used SMA methods (fully constrained least squares (FCLS) and multiple endmember spectral mixture analysis (MESMA)) were chosen for the water mapping comparison in the experiments. The results indicate that the proposed ASWM was able to detect water pixels more efficiency than other unsupervised water extraction methods, and the water fractions estimated by the proposed ASWM method correspond closely to the reference fractions with the slopes of 0.97, 1.02, 1.04, and 0.98 and the R-squared values of 0.9454, 0.9486, 0.9665, and 0.9607 in regression analysis corresponding to different test regions. In the quantitative accuracy assessment, the ASWM method shows the best performance in water mapping with the mean kappa coefficient of 0.862, mean producer's accuracy of 82.8%, and mean user's accuracy of 91.8% for test regions.

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Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission

Cited by 188 publications

References 49 publications

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery

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