Vegetation impact and recovery from oil-induced stress on three ecologically Distinct Wetland Sites in the Gulf of Mexico

April 20, 2010 marked the start of the British Petroleum Deepwater Horizon oil spill, the largest marine oil spill in US history, which contaminated coastal wetland ecosystems across the northern Gulf of Mexico. We used hyperspectral data from 2010 and 2011 to compare the impact of oil contamination and recovery of coastal wetland vegetation across three ecologically diverse sites: Barataria Bay (saltmarsh), East Bird's Foot (intermediate/freshwater marsh), and Chandeleur Islands (mangrove-cordgrass barrier islands). Oil impact was measured by comparing wetland pixels along oiled and oil-free shorelines using various spectral indices. We show that the Chandeleur Islands were the most vulnerable to oiling, Barataria Bay had a small but widespread and significant impact, and East Bird's Foot had negligible impact. A year later, the Chandeleur Islands showed the strongest signs of recovery, Barataria Bay had a moderate recovery, and East Bird's Foot had only a slight increase in vegetation. Our results indicate that the recovery was at least partially related to the magnitude of the impact such that greater recovery occurred at sites that had greater impact

Testing remotely-sensed predictors of meso-carnivore habitat use in Mediterranean ecosystems

Context: The legacy of human use of Mediterranean ecosystems results in spatial and temporal heterogeneity of resources for wildlife. Understanding wildlife use of these ecosystems may be improved by including information on ecosystem type, structure, and function extracted from remote sensing data. Objectives: To assess whether we can improve our understanding of wildlife-habitat use by including information on ecosystem type, structure and function. Methods: We tested whether remote sensing derived descriptors of ecosystem type, structure (tree cover and patch size) and function (productivity and stress) determine the habitat of stone martens (Martes foina), common genets (Genetta genetta), and European badgers (Meles meles) in southern Portugal. We linked radio-tracking data from five stone martens, five genets and eight badgers with aerial photography, and some spectra-selectivity to classify vegetation, its structure, productivity and drought stress. Results: Statistically-derived generalized linear mixed regression models using combinations of remotely sensed descriptors of ecosystem type, structure and function, performed better than single ecosystem type descriptors. Conclusion: Inclusion of information on ecosystem functioning in predictive models of habitat use is more informative than ecosystem type alone, suggesting functional relationships between wildlife and their habitat. However, inclusion of both ecosystem type and function maybe limited to finer spatial resolutions. Our results illustrate the untapped potential of remote sensing to provide detailed descriptors of habitat at adequate spatial scales, now that they are freely available and are systematically collected over space and time. This information adds useful insights on wildlife-habitat relationships under changing patterns of land use and climate

Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data

Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

Vegetation impact and recovery from oil-induced stress on three ecologically Distinct Wetland Sites in the Gulf of Mexico

April 20, 2010 marked the start of the British Petroleum Deepwater Horizon oil spill, the largest marine oil spill in US history, which contaminated coastal wetland ecosystems across the northern Gulf of Mexico. We used hyperspectral data from 2010 and 2011 to compare the impact of oil contamination and recovery of coastal wetland vegetation across three ecologically diverse sites: Barataria Bay (saltmarsh), East Bird's Foot (intermediate/freshwater marsh), and Chandeleur Islands (mangrove-cordgrass barrier islands). Oil impact was measured by comparing wetland pixels along oiled and oil-free shorelines using various spectral indices. We show that the Chandeleur Islands were the most vulnerable to oiling, Barataria Bay had a small but widespread and significant impact, and East Bird's Foot had negligible impact. A year later, the Chandeleur Islands showed the strongest signs of recovery, Barataria Bay had a moderate recovery, and East Bird's Foot had only a slight increase in vegetation. Our results indicate that the recovery was at least partially related to the magnitude of the impact such that greater recovery occurred at sites that had greater impact

Imaging spectroscopic analysis of biochemical traits for shrub species in Great Basin, USA

The biochemical traits of plant canopies are important predictors of photosynthetic capacity and nutrient cycling. However, remote sensing of biochemical traits in shrub species in dryland ecosystems has been limited mainly due to the sparse vegetation cover, manifold shrub structures, and complex light interaction between the land surface and canopy. In order to examine the performance of airborne imaging spectroscopy for retrieving biochemical traits in shrub species, we collected Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) images and surveyed four foliar biochemical traits (leaf mass per area, water content, nitrogen content and carbon) of sagebrush (Artemesia tridentata) and bitterbrush (Purshia tridentata) in the Great Basin semi-desert ecoregion, USA, in October 2014 and May 2015. We examined the correlations between biochemical traits and developed partial least square regression (PLSR) models to compare spectral correlations with biochemical traits at canopy and plot levels. PLSR models for sagebrush showed comparable performance between calibration (R2: LMA = 0.66, water = 0.7, nitrogen = 0.42, carbon = 0.6) and validation (R2: LMA = 0.52, water = 0.41, nitrogen = 0.23, carbon = 0.57), while prediction for bitterbrush remained a challenge. Our results demonstrate the potential for airborne imaging spectroscopy to measure shrub biochemical traits over large shrubland regions. We also highlight challenges when estimating biochemical traits with airborne imaging spectroscopy data

Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others