Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS)

Remote sensing of opium poppy cultivation in Afghanistan

This work investigates differences in the survey methodologies of the monitoring programmes of the United Nations Office on Drugs and Crime (UNODC) and the US Government that lead to discrepancies in quantitative information about poppy cultivation. The aim of the research is to improve annual estimates of opium production. Scientific trials conducted for the UK Government (2006–2009) revealed differences between the two surveys that could account for the inconsistency in results. These related to the image interpretation of poppy from very high resolution satellite imagery, the mapping of the total area of agriculture and stratification using full coverage medium resolution imagery. MODIS time-series profiles of Normalised Difference Vegetation Index (NDVI), used to monitor Afghanistan’s agricultural system, revealed significant variation in the agriculture area between years caused by land management practices and expansion into new areas. Image interpretation of crops was investigated as a source of bias within the sample using increasing levels of generalisation in sample interpretations. Automatic segmentation and object-based classification were tested as methods to improve consistency. Generalisation was found to bias final estimates of poppy up to 14%. Segments were consistent with manual field delineations but object-based classification caused a systematic labelling error. The findings show differences in survey estimates based on interpretation keys and the resolution of imagery, which is compounded in areas of marginal agriculture or years with poor crop establishment. Stratified and unstratified poppy cultivation estimates were made using buffered and unbuffered agricultural masks at resolutions of 20, 30 and 60 m, resampled from SPOT-5 10 m data. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Decreasing the resolution of the imagery and buffering increased unstratified estimates. Stratified estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area explained differences in cultivation figures of the opium monitoring programmes in Afghanistan. Supporting methods for yield estimation for opium poppy were investigated at field sites in the UK in 2004, 2005 and 2010. Good empirical relationships were found between NDVI and the yield indicators of mature capsule volume and dry capsule yield. The results suggested a generalised relationship across all sampled fields and years (R2 >0.70) during the 3–4 week period including poppy flowering. The application of this approach in Afghanistan was investigated using VHR satellite imagery and yield data from the UNODC’s annual survey. Initial results indicated the potential of improved yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling. The recommendations for poppy cultivation surveys are: the use of image-based stratification for improved precision and reducing differences in the agricultural mask, and use of automatic segmentation for improved consistency in field delineation of poppy crops. The findings have wider implications for improved confidence in statistical estimates from remote sensing methodologies

Sensitivity of GNSS-R spaceborne observations to soil moisture and vegetation

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant.Postprint (author's final draft

Spatio-temporal study of wood formation of Quercus robur L. and Q. pyrenaica Willd. in the Northwest of the Iberian Peninsula

This study on ecological wood anatomy contributes to the knowledge on the process of xylem formation and its associated climatic patterns, which are fundamental to establish a correct interpretation of the cause-effect relationships observed in dendrochronological retrospective analyses of ring-porous species. For this purpose, the process of wood formation (xylogenesis) and leaf phenology were monitored during two years at 14 sites of natural forests (nive for Quercus robur, and five for Q. pyrenaica). A retrospective (dendrochronological) study of tree rings and anatomical variables (earlywood vessels) was also carried out at four out of the 14 sites. Climate-growth relationships at the 14 sites were analyzed, the synchronicity between wood growth and leaf development was described, and the water content of crown organs (branches, buds, leaves) was quantified to minimize observer’s subjectivity for leaf phenology

Using multi-resolution remote sensing to monitor disturbance and climate change impacts on Northern forests

Global forests are experiencing a variety of stresses in response to climate change and human activities. The broad objective of this dissertation is to improve understanding of how temperate and boreal forests are changing by using remote sensing to develop new techniques for detecting change in forest ecosystems and to use these techniques to investigate patterns of change in North American forests. First, I developed and applied a temporal segmentation algorithm to an 11-year time series of MODIS data for a region in the Pacific Northwest of the USA. Through comparison with an existing forest disturbance map, I characterized how the severity and spatial scale of disturbances affect the ability of MODIS to detect these events. Results from these analyses showed that most disturbances occupying more than one-third of a MODIS pixel can be detected but that prior disturbance history and gridding artifacts complicate the signature of forest disturbance events in MODIS data. Second, I focused on boreal forests of Canada, where recent studies have used remote sensing to infer decreases in forest productivity. To investigate these trends, I collected 28 years of Landsat TM and ETM+ data for 11 sites spanning Canada's boreal forests. Using these data, I analyzed how sensor geometry and intra- and inter-sensor calibration influence detection of trends from Landsat time series. Results showed systematic patterns in Landsat time series that reflect sensor geometry and subtle issues related to inter-sensor calibration, including consistently higher red band reflectance values from TM data relative to ETM+ data. In the final chapter, I extended the analyses from my second chapter to explore patterns of change in Landsat time series at an expanded set of 46 sites. Trends in peak-summer values of vegetation indices from Landsat were summarized at the scale of MODIS pixels. Results showed that the magnitude and slope of observed trends reflect patterns in disturbance and land cover and that undisturbed forests in eastern sites showed subtle, but detectable, differences from patterns observed in western sites. Drier forests in western Canada show declining trends, while mostly increasing trends are observed for wetter eastern forests

Doctor of Philosophy

dissertationWildfire is a multifaceted, global phenomenon with ecological, environmental, climatic and socioeconomic impacts. Live fuel moisture content (LFMC) is a critical fuel property for determining fire danger. Previous research has used meteorological data and remote sensing to estimate LFMC with the goal of extending direct ground measurement. A fundemental understanding of plant physiology and spectral response toLFMC variation is needed to advance use of LFMC for fire risk management and remote sensing applications. This study integrates field samples of three species, lab measurements, remote sensing dataand statistical analysis to construct a more complete knowledge of the physical foundations of LFMC seasonalityfrom three perspectives: 1)relationships between soil moisture and LFMC; 2) spectroscopic analysis of seasonal changes in LFMC and leaf dry mass; 3) relationships between LFMC and leaf net heat content, and between leaf net heat content and remotely sensed indices. This study is the first to demonstrate a relationship between in situ soil moisture and LFMC. It also challengesthe current asumption of changing water content and stable dry matter content over time in remote sensing esimation of LFMC, showing the dominant contribution of dry matter in LFMC variation in some conifer species. The resultsdemonstrate the combination of spectroscopic data and partial least squares regression can improve modeling accuray for LFMC temporal variation, but the spectral response to changing LFMC and dry mass is difficult to seperate from broader spectral trends due to temporal change in chlorophyll, leaf structure, water and covaried biochemical components. Lastly it introducesa new vegetation variable, leaf net heat content, and demostrates its relationship with LFMC and potential for remote sensing estimation.This study will improve present capabilities of remote sensing for monitoring vegetation water stress and physiological properties. It will also advance understanding of seasonal changes in LFMC to better estimate fire danger and potential impacts of fire on ecosystems and the carbon cycle

Optical and radar remotely sensed data for large-area wildlife habitat mapping

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models