The biochemical traits of plant foliage provide important information to study photosynthetic capacity and biogeochemical cycling in ecosystems [1
]. Among widely-studied biochemical traits are leaf mass per area, water content, nitrogen content, and carbon content. Variations in leaf mass per area (LMA, ratio of leaf dry mass to leaf area) correspond to the fundamental tradeoffs in leaf construction costs vs. light-intercepting surface area and are driven by a range of environmental controls [6
]. While foliage gains carbon and builds structure, LMA corresponds to several biochemical and structural compounds in the plant including (positive correlation) cellulose and lignin, and (negative correlation) protein. These relate to physiological processes of photosynthesis, primary production and leaf decomposition [8
]. Water is one of the most important factors regulating plant growth and development in ecosystems [10
]. Leaf water content is an important parameter for assessing drought and predicting susceptibility of wildfire. Nitrogen content is a fundamental component of light-harvesting pigments and photosynthetic machinery, and it is closely related to the maximum photosynthetic rate [11
Remote sensing provides an opportunity to assess plant biochemical traits across large areas compared to field sampling methods. Multispectral satellite sensors like MODIS and Landsat have been used to monitor vegetation properties, and airborne imaging spectrometers have proven useful for mapping variation in the spatial distribution of plant biochemical traits mainly attributed to narrow spectral bands, fine spatial resolution and continuous reflectance measurement in solar spectra [13
]. Relationships between canopy biochemical traits and airborne imaging spectroscopy have been demonstrated in a variety of ecosystems, such as tropical forests [15
], northern temperate and boreal tree species [17
], Mediterranean species in California [19
], and northwest [21
] and northeast forests [22
] in the U.S. Although previous studies have demonstrated the potential for measuring plant biochemical traits from reflectance spectra, there are still shortages and challenges in the remote sensing of biochemical traits in dryland ecosystems.
First, spectral determination of biochemical traits of shrub species in dryland ecosystems has been difficult mainly due to the complex variation in vegetation structure from pixel to landscape levels [23
]. Dryland ecosystems usually have low-stature; sparse vegetation cover; an abundance of targets with high albedo [24
]; and complex spectral mixing functions between visible, near-infrared and shortwave infrared wavelengths [26
]. Although various statistical methods have been used to estimate traits with reflectance spectra [15
], there is a need for a more comprehensive examination of spectral correlation with traits in regression models.
Second, although there are previous studies examining imaging spectroscopy instruments for measuring certain traits in shrub species, limited studies have investigated the performance of new imaging spectroscopy instruments to assess a series of vegetation traits. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data in the USA have shown the potential to derive water content [30
] and nitrogen content [19
]. Mirik et al. [32
] investigated 1-m resolution PROBE-1 hyperspectral imagery system (Earth Search Sciences Inc. Kalispell, MT, USA) to estimate nitrogen, phosphorus and neutral detergent fiber in a range of shrub species in Yellowstone National Park. Mitchell et al. [33
] examined HyMap sensor data (HyVista, Inc., Sydney, Australia) to estimate foliar and canopy nitrogen content in sagebrush. Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) has been developed to replace the AVIRIS instrument that has been flying since 1986. Similarly, the National Ecological Observatory Network (NEON, http://www.neonscience.org/
, last accessed in August 2018) integrates an AVIRIS-NG sensor and field sites to measure numerous vegetation metrics across eco-climatic domains in the U.S. The increased availability and sensor characteristics of platforms such as AVIRIS-NG have the potential to more broadly examine shrub biochemical traits and monitor long-term changes in dryland ecosystems.
Third, dryland ecosystems account for roughly 41% of the Earth’s surface and support 38% of the world’s current population [34
]. The Great Basin, a semi-arid ecoregion, is representative of the dryland ecosystem in the western U.S. It has experienced multiyear drought accompanied with concerns that include long term aridity, biodiversity loss, and increased regional fire activity [35
]. The 2012–2015 drought has left severe canopy water loss in California and substantial future forest change [37
]. Xue et al. [38
] indicated that annual total precipitation and extreme precipitation days showed a significant positive trend in the eastern Great Basin during 1951–2013, while the western basin experienced significant negative trends. The western Great Basin might experience a drier and sparser shrub community if the trend continues. Great Basin sagebrush (Artemisia tridentata
) and bitterbrush (Purshia tridentata
), two widely distributed species in Great Basin ecoregion, play critical roles in the hydrologic cycle and in sustaining wildlife and livestock. Examining biochemical traits in these species will provide insights to study plant productivity and biogeochemical cycling in the Great Basin.
The primary objective of this research is to investigate imaging spectroscopy approaches for estimating key biochemical traits in shrub species in the Great Basin. We aim to examine three specific aspects: (i) The variation and correlation of biochemical traits within and between shrub species, (ii) the performance of airborne imaging spectrometer AVIRIS-NG for measuring biochemical traits, and (iii) the modeling complexity at canopy and plot levels. To answer these questions we collected shrub samples for biochemical traits in representative sagebrush and bitterbrush communities concurrent with AVIRIS-NG flights. Then we examined the correlation between traits and developed partial least square regression (PLSR) models to evaluate spectral correlation at canopy and plot levels.
Our study sites represent a typical sagebrush and bitterbrush dominant community in the Great Basin, albeit sites are at the extreme southwest boundary of this vegetation type, into the ecotone transition with the warmer and drier Mojave Desert ecosystem. Our sampling dates were long after the rainy season ended in both 2014 and 2015, and the severe drought in California had significantly stressed forests and rangelands throughout southern Sierra Nevada, to produce extensive mortality [37
]. Leaf water content changes seasonally between 75% and 45% of fresh weight in shrub species [42
], but water in our samples averaged about 46% by fresh weight in sagebrush and 42% in bitterbrush. These low values of leaf water content are consistent with the drought conditions during both years of this project, indicating a high degree of water stress on the plants [46
]. The variation in foliar traits across years represented a seasonal variation in the plant physiological process and environment. As shrubs grew from spring to fall, foliar LMA and carbon content increased, but water and nitrogen content decreased. Shrubs utilized nitrogen to construct the foliar structure, likely resulting in higher LMA, and water resource scarcity during the summer led to the lower water content.
Given the complexity of the environmental conditions, the spectroscopic analysis was still able to demonstrate the potential to predict biochemical traits. Our model accuracy of four traits in sagebrush was comparable to or higher than previous studies of nitrogen content [33
]. At canopy level, only validation models of LMA and carbon content showed R
-square values above 0.5 and comparable rRMSE values to calibration models. In comparison, canopy-level PLSR models of bitterbrush showed much lower prediction ability than sagebrush. Bitterbrush leaves are about 1cm long (Jepson eFlora, http://ucjeps.berkeley.edu/eflora/
, last accessed in August 2018), but our samples showed significantly smaller leaf sizes and fragmented shape. The complex canopy structure and especially small leaves in bitterbrush makes it more difficult to spectrally estimate traits. Various methods have been used to minimize noise and resolve overlapping absorption features such as difference and logarithm transformation in reflectance [47
]. We tested the difference and logarithm transformation and found no significant improvement for prediction (results not reported).
Our PLSR models identified high VIP values in spectral regions significantly correlated with biochemical trait variation. High VIP values in LMA models were generally located in near-infrared and short-wave infrared [13
], which was associated to leaf structure and dry matter such as cellulose and lignin. Significant spectral regions in short-wave infrared were also shown in the models of carbon which was the major component in dry matter. VIP values of water models showed some high values near 1450 and 2300 nm as identified in previous studies [52
], and smaller values in spectral regions associated with dry matter. Nitrogen is a relatively small component of leaf dry matter content, with an average 1.5% in bitterbrush and 2.3% in sagebrush from our leaf samples. Kokaly [53
] identified two absorption features at 2055 and 2172 nm on the shoulders of 2100 nm corresponding to absorption features of proteins. Our nitrogen models showed high VIP values in this spectral region. VIP values of four vegetation traits covered common dry matter-related wavelengths in short-wave infrared, which demonstrated that the main spectral signatures of water-stressed shrubs in this ecoregion were attributed to dry matter.
Plot-level LMA models in sagebrush showed higher R
-square values and lower rRMSE values than canopy-level models. The nitrogen content model showed higher R
-square value but rRMSE value increased as well. Results of water and carbon content were poorer than canopy-level models. Compared to study sites in a wetter section of the Great Basin [33
], our sagebrush had a much lower nitrogen content, and shrub vegetation cover in our plots was also much lower. Many of our surveyed shrub canopies had canopy diameters that were smaller than the AVIRIS-NG data pixel size of 2.6 m. This study showed significant effects of canopy structure for remote sensing of traits. Knyazikhin et al. [54
] argued the canopy structure’s important role in canopy radiative transfer and spectral determination of nitrogen content in closed canopy environments. Several papers have investigated the practical limits on sparse vegetation cover discrimination in dryland environments [25
], and several methods have been proposed to estimate vegetation structure and cover such as spectral unmixing and data fusion of passive and active remote sensing data [55
]. We suggest future research should examine whether the combining airborne imaging spectrometer with a high-resolution multispectral camera or lidar sensor can better predict shrub biochemical traits over large landscapes.
The characterization of plant biochemical traits is important for understanding dryland ecosystems and their response to environmental change. To summarize our findings, this study demonstrated the performance of airborne imaging spectrometer AVIRIS-NG to estimate biochemical traits of sagebrush and bitterbrush in the Great Basin ecoregion, including leaf mass per area, water content, nitrogen content and carbon content. Sparse vegetation cover, complex canopy structure and small foliage size made the spectral estimation more challenging. The spectral regions identified by VIP values in PLSR models displayed significant distinctions in specific wavelengths corresponding to known biochemical absorption features, including those related to foliar lignin, cellulose, nitrogen, and water. A regional remote sensing estimation of vegetation canopy structure will facilitate a more robust prediction of vegetation traits. A future step will be to combine similar data sets from other shrub species to refine and standardize both data and methods as a basis to operationally estimating foliar traits in dryland ecosystems with the NEON airborne observation platform, NASA’s proposed space-borne Hyperspectral Infrared Imager (HyspIRI), and German EnMAP mission.