In this study we fused high-spatial resolution (3.7m) hyperspectral imagery with 22pulse/m2 lidar data at the individual crown object scale to map 29 common tree species in Santa Barbara, California, ...USA. We first adapted and parallelized a watershed segmentation algorithm to delineate individual crowns from a gridded canopy maxima model. From each segment, we extracted all spectra exceeding a Normalized Difference Vegetation Index (NDVI) threshold and a suite of crown structural metrics computed directly from the three-dimensional lidar point cloud. The variables were fused and crowns were classified using canonical discriminant analysis. The full complement of spectral bands along with 7 lidar-derived structural metrics were reduced to 28 canonical variates and classified. Species-level and leaf-type level maps were produced with respective overall accuracies of 83.4% (kappa=82.6) and 93.5%. The addition of lidar data resulted in an increase in classification accuracy of 4.2 percentage points over spectral data alone. The value of the lidar structural metrics for urban species discrimination became particularly evident when mapping crowns that were either small or morphologically unique. For instance, the accuracy with which we mapped the tall palm species Washingtonia robusta increased from 29% using spectral bands to 71% with the fused dataset. Additionally, we evaluated the role that automated segmentation plays in classification error and the prospects for mapping urban forest species not included in a training sample. The ability to accurately map urban forest species is an important step towards spatially explicit urban forest ecosystem assessment.
•We map 29 urban tree species using hyperspectral and lidar data fusion.•Crown-objects are delineated using watershed segmentation on a canopy maxima model.•Species classified with 83.4% accuracy using canonical discriminant analysis•Lidar structural metrics critical for classifying species with small crowns•Segmentation errors only minimally impacted classification accuracy.
In June 2018, the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission was launched to measure plant temperatures and better understand how they respond to stress. ...While the ECOSTRESS mission delivers imagery with ~60 m spatial resolution, it is often useful to have spectra at the leaf level in order to explain variability seen at the pixel level. As it was originally titled, the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library version 2.0 has been expanded to support ECOSTRESS studies by including major additions of laboratory measured vegetation and non-photosynthetic vegetation (NPV) spectra. The library now contains 541 leaf visible shortwave infrared (VIS/SWIR) spectra, 472 leaf thermal infrared (TIR) spectra, and 51 NPV VIS/SWIR and TIR spectra. Previously, the library primarily contained VSWIR and TIR laboratory spectra of minerals, rocks, and man-made materials. This new library, containing over 3000 spectra, was renamed the ECOSTRESS spectral library version 1.0 and is publicly available (http://speclib.jpl.nasa.gov). It should be noted that as with the prior versions of the library, the VSWIR and TIR measurements were made with separate instruments with different calibration sources. Care should be taken when combining the data into a seamless spectrum to cover the entire spectral range. The ECOSTRESS spectral library provides a comprehensive collection of natural and man-made laboratory collected spectra covering the wavelength range of 0.35–15.4 μm.
•The ASTER spectral library 2.0 was updated to the ECOSTRESS spectral library 1.0.•The library includes 541 VIS/SWIR and 472 TIR green vegetation spectra.•The library also includes 51 VIS/SWIR and TIR spectra for non photosynthetic vegetation.•Library covers VIS/SWIR and TIR domain (0.35–15.4 μm).•Includes soil, manmade, meteorite, mineral, NPV, rock, vegetation, water/snow/ice
Automated, reliable cloud masks over snow‐covered terrain would improve the retrieval of snow properties from multispectral satellite sensors. The U.S. Geological Survey and NASA chose the currently ...operational cloud masks based on global performance across diverse land cover types. This study assesses errors in these cloud masks over snow‐covered, midlatitude mountains. We use 26 Landsat 8 scenes with manually delineated cloud, snow, and land cover to assess the performance of two cloud masks: CFMask for the Landsat 8 OLI and the cloud mask that ships with Moderate‐Resolution Imaging Spectroradiometer (MODIS) surface reflectance products MOD09GA and MYD09GA. The overall precision and recall of CFMask over snow‐covered terrain are 0.70 and 0.86; the MOD09GA cloud mask precision and recall are 0.17 and 0.72. A plausible reason for poorer performance of cloud masks over snow lies in the potential similarity between multispectral signatures of snow and cloud pixels in three situations: (1) Snow at high elevation is bright enough in the “cirrus” bands (Landsat band 9 or MODIS band 26) to be classified as cirrus. (2) Reflectances of “dark” clouds in shortwave infrared (SWIR) bands are bracketed by snow spectra in those wavelengths. (3) Snow as part of a fractional mixture in a pixel with soils sometimes produces “bright SWIR” pixels that look like clouds. Improvement in snow‐cloud discrimination in these cases will require more information than just the spectrum of the sensor's bands or will require deployment of a spaceborne imaging spectrometer, which could discriminate between snow and cloud under conditions where a multispectral sensor might not.
Plain Language Summary
Both snow and clouds are brighter in the visible spectrum and colder in the thermal infrared than other parts of the landscape, so discriminating between them in passive optical satellite images poses a long‐standing problem in remote sensing of Earth. The operational cloud masks now provided for satellite imagery were chosen because they identify clouds correctly over most land covers. Over snow‐covered terrain in the mountains, however, errors in identification persist. This study evaluates those errors and identifies reasons why some snow and clouds appear very similar in our workhorse satellite sensors, specifically Landsat and Moderate Resolution Imaging Spectroradiometer: (1) At high elevations, snow can look like cirrus clouds in the specific spectral regions employed to detect cirrus. (2) Snow is usually dark in spectral bands where most clouds are bright, but thin clouds can appear similarly dark in those same bands. (3) Image pixels containing both snow and soil can appear bright in the same spectral regions where clouds are bright. Fixing these cases of misidentification will require analyses that use more information than just the spectrum of the sensor's bands or will require deployment of an imaging spectrometer in space.
Key Points
Some snow and cloud pixels have similar multispectral signatures, so discrimination can be difficult
The best available cloud masks for Landsat and MODIS consistently identify snowpack boundaries as clouds
Better snow‐cloud discrimination with multispectral sensors requires information beyond just the pixel's spectral signature
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 30km2 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 7years, and vegetation cover affected by moderate severity recovered after 13years. Vegetation affected by high severity fire was estimated to recover after 20years. We conclude that VRI time series based on multi-temporal 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.
•Forest management needs a post-fire vegetation cover regrowth monitoring.•We introduced a Multiple Endmember Spectral Mixture Analysis (MESMA) based methodology.•A vegetation recovery index (VRI) was defined for each burn severity level.•VRI time series trend modeling leaded to an estimate of vegetation cover resilience.•Method applied in P. pinaster forests, but with potential for extrapolation
Forest fires are a major hazard in Mediterranean countries, with an average of 45,000 fires per year. Discrimination of different degrees of burn severity is critical to improve management of ...fire-affected areas. In this work, an unmixing-based methodology was evaluated in three Mediterranean study areas to estimate burn severity from medium spatial resolution optical satellite data. Post-fire Landsat 5 Thematic Mapper (TM) images were unmixed into four fraction images: non-photosynthetic vegetation and ash (NPV–Ash), green vegetation (GV), soil and shade using Multiple Endmember Spectral Mixture Analysis (MESMA). MESMA decomposes each pixel using different combinations of potential endmembers, overcoming the Linear Spectral Mixture Analysis limitation of using the same number of endmembers to model all image pixels. Next, a decision tree was used to classify the shade normalized fraction images into four classes: unburned and low, moderate, and high levels of burn severity. Finally, the burn severity estimates were validated using error matrix, producer and user accuracies per class, and κ statistic. For reference data, we used 50 plots per class defined from a 50cm post-fire orthophotography (proportion of dead tree<50%, low severity; proportion of dead tree between 50 and 90%, moderate severity; and proportion of dead tree>90%, high severity). MESMA-based burn severity estimates showed a high accuracy (0.80, 0.80, and 0.78) for the three test sites. We conclude that the proposed MESMA based methodology is valid to accurately map burn severity in Mediterranean countries from moderate resolution satellite data.
•Forest management needs an accurate burn severity levels mapping method.•We introduce a Multiple Endmember Spectral Mixture Analysis (MESMA) based methodology.•Landsat TM 5 shade normalized fraction images were classified by a decision tree.•We used three study areas located in Mediterranean countries.•MESMA-based burn severity estimates had a high accuracy (κ statistic>0.75).
Vegetation growth and physiological functioning, including carbon uptake and transpiration, can be strongly affected by drought. During water-limited conditions, urban vegetation may be limited in ...its ability to provide ecosystem services, such as shading and evaporative cooling. While there has been extensive research on drought response in natural vegetation and agricultural systems using remote sensing, drought impacts on urban vegetation have remained understudied. With >50% of people worldwide living in cities and the increased potential for future droughts, it is important to understand how urban vegetation will respond and to evaluate potential impacts. Here, we used repeat flights of NASA airborne imaging spectroscopy (AVIRIS and AVIRIS-NG) to quantify the responses of common urban tree species and turfgrass in downtown Santa Barbara, California, USA to the 2012–2016 California drought. The effects of the drought on vegetation were quantified using several spectral indicators: fractional cover from multiple endmember spectral mixture analysis (MESMA), vegetation indices, and continuum removed absorption features. We used existing tree crown object polygons that were classified to species and had associated estimates of pre-drought leaf area index and biomass. We also included polygons of turfgrass parks and recreational fields. All tree species and turfgrass had four or more spectral indicators with significantly lower mean values (p < 0.05) during the drought in 2014, and evidence of recovery was observed for some species in 2017, which was after the drought had ended. In general, needleleaf and native trees were more affected than broadleaf and non-native trees, respectively, but with considerable interspecific variability. Based on the spectral indicators, turfgrass cover senesced in the middle of the drought but recovered soon after the drought ended, whereas the majority of tree species had not fully recovered. Mean green vegetation cover declined in most tree canopies during the drought, primarily exposing paved surfaces and/or non-photosynthetic vegetation, depending on the site type and tree patch size. Drought impacts as expressed in spectral indicators were generally more severe in tree patches with high pre-drought leaf area index, suggesting that denser, leafier canopies were more difficult for trees to maintain during the drought. Most spectral indicators were correlated with one another (R > 0.4), suggesting co-occuring adjustments of greenness, water content, and plant pigments.
•Quantified turfgrass and 19 urban tree species' spectral response to drought•Spectral indicators showed changes in greenness, water content, and pigments•All tree species had significant response, with large species differences•Trees with higher pre-drought leaf area index had greater drought response overall•Turfgrass recovered after drought, but the majority of tree species had not
Orbital imaging spectrometers, such as the proposed Hyperspectral Infrared Imager (HyspIRI) mission, will provide global, multi-year Visible Shortwave Infrared (VSWIR) reflectance maps. Monitoring ...the Earth's surface at high spectral resolution will advance our understanding of changing ecosystems and land use. These applications depend on reliable correction of atmospheric scattering and absorption. The HyspIRI Preparatory Campaign is an airborne precursor mission comprised of multiple flights by the “classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) over a wide geographic area. This article describes the atmospheric correction that we have implemented for the campaign. We first present the theoretical basis of our approach, which is grounded in the ATmospheric REMoval (ATREM) algorithm. We then describe new enhancements including retrieval of pressure altitude, which improves accuracy over widely varying topography, and joint retrieval of optical absorption for three phases of water (vapor, liquid, and ice), which improves accuracy over vegetated areas. Reflectance is validated using ground spectra acquired across a wide range of targets and elevations. Finally, we use the algorithm to map vapor, liquid, and ice phases of water over 6months across a 14,000km2 region of California.
•We enhance ATREM reflectance retrievals for global VSWIR imaging spectroscopy.•Our approach estimates pressure altitude and water vapor, liquid and ice absorption.•The method is evaluated on data spanning a year of the HyspIRI Preparatory Campaign.•We compare reflectances to ground truth spectra from diverse targets and elevations.•Simultaneous retrieval of three water phases improves biases in water vapor products.
Remote sensing has considerable potential for providing accurate, up-to-date information in urban areas. Urban remote sensing is complicated, however, by very high spectral and spatial complexity. In ...this paper, Multiple Endmember Spectral Mixture Analysis (MESMA) was applied to map urban land cover using HyMap data acquired over the city of Bonn, Germany. MESMA is well suited for urban environments because it allows the number and types of endmembers to vary on a per-pixel basis, which allows controlling the large spectral variability in these environments. We employed a hierarchical approach, in which MESMA was applied to map four levels of complexity ranging from the simplest level consisting of only two classes, impervious and pervious, to 20 classes that differentiated material composition and plant species. Lower levels of complexity, mapped at the highest accuracies, were used to constrain spatially models at higher levels of complexity, reducing spectral confusion between materials. A spectral library containing 1521 endmembers was created from the HyMap data. Three endmember selection procedures, Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA) and Count Based Endmember Selection (COB), were used to identify the most representative endmembers for each level of complexity. Combined two-, three- or four-endmember models – depending on the hierarchical level – were applied, and the highest endmember fractions were used to assign a land cover class. Classification accuracies of 97.2% were achieved for the two lowest complexity levels, consisting of impervious and pervious classes, and a four class map consisting of vegetation, bare soil, water and built-up. At the next level of complexity, consisting of seven classes including trees, grass, bare soil, river, lakes/basins, road and roof/building, classification accuracies remained high at 81.7% with most classes mapped above 85% accuracy. At the highest level, consisting of 20 land cover classes, a 75.9% classification accuracy was achieved. The ability of MESMA to incorporate within-class spectral variability, combined with a hierarchical approach that uses spatial information from one level to constrain model selection at a higher level of complexity was shown to be particularly well suited for urban environments.
Successful post-fire management depends on accurate burn severity maps that are increasingly derived from satellite data, replacing field-based estimates. Post-fire vegetation and soil changes, ...besides modifying the reflected and emitted radiation recorded by sensors onboard satellites, strongly alters water balance in the fire affected area. While fire-induced spectral changes can be well represented by fraction images from Multiple Endmember Spectral Mixture Analysis (MESMA), changes in water balance are mainly registered by evapotranspiration (ET). As both types of variables have a clear physical meaning, they can be easily understood in terms of burn severity, providing a clear advantage compared to widely-used spectral indices. In this research work, we evaluate the potential of Landsat-derived ET to estimate burn severity, together with MESMA derived Sentinel-2 fraction images and important environment variables (pre-fire vegetation, climate, topography). In this study, we use the random forest (RF) classifier, which provides information on variable importance allowing us to identify the combination of input variables that provided the most accurate estimate. Our study area is located in Central Portugal, where a mega-fire burned >450 km2 from 17 to 24 June 2017. We used the official burn severity map as ground reference. The RF algorithm identified ET as the most important variable in the burn severity model, followed by MESMA char fractions. When both ET and MESMA char fraction image were used as RF inputs, burn severity estimates reached higher accuracy than if only one of them was used, which suggests their potential synergetic interaction. In particular, when environmental variables were used in addition to ET and char fraction, the highest accuracy for burn severity was reached (κ = 0.79). Our main conclusion is that post-fire fine resolution ET is a useful and easily understandable indicator of burn severity in Mediterranean ecosystems, in particular when used in combination with a MESMA char fraction image. This novel approach to estimate burn severity may help to develop successful post-fire management strategies not only in Mediterranean ecosystems but also in other ecosystems, due to ease of generalization.
•Post-fire fine-resolution evapotranspiration is useful as indicator of burn severity.•Post-fire ET reflected fire-induced changes in water balance.•MESMA fractions showed post-fire spectral changes.•Burn severity could be accurately estimated from post-fire ET and MESMA fractions.•Method applied to Central Portugal, 2017, with potential for extrapolation.
Remote sensing can inform agricultural knowledge of crop water use through observation of land surface temperature, which can act as an indicator of plant function and health. This study uses ...remotely sensed data to quantify thermal variability within fruit and nut orchards during an intense drought period in California's Central Valley (2013–2015). First, fractions of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil were derived for a variety of crop species using visible-shortwave infrared (VSWIR) spectra imaged by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Fractional estimates were then used to select thermal endmembers for each class using simultaneously collected MODIS/ASTER Airborne Simulator (MASTER) thermal imagery and a crop species map. Expected pixel temperatures of non-stressed crop fields were then modeled, and the per-pixel difference between measured and expected temperatures was calculated as a temperature residual. Crop residuals serve to capture variability in temperature that may be attributable to differences in crop health and/or management practices. We found multiple distinct thermal classes to exist across the study site. Furthermore, crop temperatures correlated to expected crop ET rates, and temperature residuals showed correlations to changes in crop yields during the study period. Further assessment of findings revealed an increase in temperature residuals during the study period that is consistent with increasing stress, likely linked to the progression of drought. The method presented here shows utility for regional agricultural analysis of crop water use and is particularly relevant for ECOSTRESS and the upcoming Surface Biology & Geology mission.
•We use spatially coincident hyperspectral and thermal imagery to assess crop stress.•Expected pixel temperatures of non-stressed crop fields are modeled.•Hyperspectral imagery controls for thermal variability not attributable to stress.•Crop stress is quantified as the difference between the expected and measured LST.•High temperature residuals correspond to decreases in crop yields.
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