Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions

Multiple Endmember Spectral Mixture Analysis (MESMA) Applied to the Study of Habitat Diversity in the Fine-Grained Landscapes of the Cantabrian Mountains

P. 1-19 ArtículoHeterogeneous and patchy landscapes where vegetation and abiotic factors vary at small spatial scale (fine-grained landscapes) represent a challenge for habitat diversity mapping using remote sensing imagery. In this context, techniques of spectral mixture analysis may have an advantage over traditional methods of land cover classification because they allow to decompose the spectral signature of a mixed pixel into several endmembers and their respective abundances. In this work, we present the application of Multiple Endmember Spectral Mixture Analysis (MESMA) to quantify habitat diversity and assess the compositional turnover at different spatial scales in the fine-grained landscapes of the Cantabrian Mountains (northwestern Iberian Peninsula). A Landsat-8 OLI scene and high-resolution orthophotographs (25 cm) were used to build a region-specific spectral library of the main types of habitats in this region (arboreal vegetation; shrubby vegetation; herbaceous vegetation; rocks–soil and water bodies). We optimized the spectral library with the Iterative Endmember Selection (IES) method and we applied MESMA to unmix the Landsat scene into five fraction images representing the five defined habitats (root mean square error, RMSE 0.025 in 99.45% of the pixels). The fraction images were validated by linear regressions using 250 reference plots from the orthophotographs and then used to calculate habitat diversity at the pixel ( -diversity: 30 30 m), landscape (-diversity: 1 1 km) and regional ("-diversity: 110 33 km) scales and thecompositional turnover ( - and -diversity) according to Simpson’s diversity index. Richness and evenness were also computed. Results showed that fraction images were highly related to reference data (R2 0.73 and RMSE 0.18). In general, our findings indicated that habitat diversity was highly dependent on the spatial scale, with values for the Simpson index ranging from 0.20 0.22 for -diversity to 0.60 0.09 for -diversity and 0.72 0.11 for "-diversity. Accordingly, we found -diversity to be higher than -diversity. This work contributes to advance in the estimation of ecological diversity in complex landscapes, showing the potential of MESMA to quantify habitat diversity in a comprehensive way using Landsat imageryS

Using Remote Sensing to Characterize Disturbance during a Severe Drought in the Sierra Nevada

Between 2012 and 2016, California experienced an extreme period of drought and high temperatures. During this period there were two particularly notable disturbances in the southern Sierra Nevada. In 2013, the first major disturbance occurred in the form of the Rim Fire. At 1,041 km2, the Rim Fire is the largest fire ever recorded in the Sierra Nevada. Throughout the drought, but particularly in the latter years, there was also epidemic levels of tree mortality, particularly mortality tied to native bark beetles (Dendroctonus spp.) killing pines (Pinus spp.).Using the southern Sierra Nevada as a case study, I investigated the novel insights a next-generation imaging spectroscopy satellite would be able to give in understanding fire and tree mortality globally. Specifically, I showed the potential for imaging spectroscopy based spectral mixture analysis (SMA) as an assessment of fire severity. SMA cover fractions allow for a remotely sensed fire severity metric that would be more readily compared at the global level than those currently in use, such as difference normalized burn ratio (dNBR). I also demonstrated that using the random forest machine learning algorithm, a simulated spaceborne imaging spectrometer would be able to more accurately identify the location of red stage tree mortality compared to existing multispectral satellites such as Landsat. In addition, remote sensing was used to gain an understanding of the impact and drivers of tree mortality. For a 2,240 ha watershed, a model of tree crown locations, with species and height identified, was created based on a combination of high spatial resolution airborne imaging spectroscopy and lidar. Then, high-resolution multispectral imagery was interpreted to determine a tree’s 2016 status. In the area investigated, the net effect of the drought was to reduce the number of live conifer stems taller than 15 m at the crown level by 75%, primarily due to the death of ponderosa pine. Finally, the factors that distinguished conifers that were alive in 2016 from conifers that died between 2015 and 2016, both within the 2,240 ha watershed and across the southern Sierra Nevada were examined. Trees that survived were typically associated with being located in stands with tree species and height class heterogeneity. Stands in wetter, cooler parts of the Sierra Nevada during the drought were also more likely to survive

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global Drylands: A UN system-wide response, 2011). In this research, a combination of remote sensing, field data collection, and ecosystem modeling were used to establish an integrated framework for semi-arid ecosystems dynamics monitoring. Foliar nitrogen (N) plays an important role in vegetation processes such as photosynthesis and there is wide interest in retrieving this variable from hyperspectral remote sensing data. In this study, I used the theory of canopy spectral invariants (AKA p-theory) to understand the role of canopy structure and soil in the retrieval of foliar N from hyperspectral data and machine learning techniques. The results of this study showed the inconsistencies among different machine learning techniques used for estimating N. Using p-theory, I demonstrated that soil can contribute up to 95% to the total radiation budget of the canopy. I suggested an alternative approach to study photosynthesis is the use of dynamic global vegetation models (DGVMs). Gross primary production (GPP) is the apparent ecosystem scale photosynthesis that can be estimated using DGVMs. In this study, I performed a thorough sensitivity analysis and calibrated the Ecosystem Demography (EDv2.2) model along an elevation gradient in a dryland study area. I investigated the GPP capacity and activity by comparing the EDv2.2 GPP with flux towers and remote sensing products. The overall results showed that EDv2.2 performed well in capturing GPP capacity and its long term trend at lower elevation sites within the study area; whereas the model performed worse at higher elevations likely due to the change in vegetation community. I discussed that adding more heterogeneity and modifying ecosystem processes such as phenology and plant hydraulics in ED.v2.2 will improve its application to higher elevation ecosystems where there is more vegetation production. And finally, I developed an integrated hyperspectral-lidar framework for regional mapping of xeric and mesic vegetation in the study area. I showed that by considering spectral shape and magnitude, canopy structure and landscape features (riparian zone), we can develop a straightforward algorithm for vegetation mapping in drylands. This framework is simple, easy to interpret and consistent with our ecological understanding of vegetation distribution in drylands over large areas. Collectively, the results I present in this dissertation demonstrate the potential for advanced remote sensing and modeling to help us better understand ecosystem processes in drylands

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems