Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Phenological responses of Icelandic subarctic grasslands to short-term and long-term natural soil warming

The phenology of vegetation, particularly the length of the growing season (LOS; i.e., the period from greenup to senescence), is highly sensitive to climate change, which could imply potent feedbacks to the climate system, for example, by altering the ecosystem carbon (C) balance. In recent decades, the largest extensions of LOS have been reported at high northern latitudes, but further warming-induced LOS extensions may be constrained by too short photoperiod or unfulfilled chilling requirements. Here, we studied subarctic grasslands, which cover a vast area and contain large C stocks, but for which LOS changes under further warming are highly uncertain. We measured LOS extensions of Icelandic subarctic grasslands along natural geothermal soil warming gradients of different age (short term, where the measurements started after 5 years of warming and long term, i.e., warmed since ≥50 years) using ground-level measurements of normalized difference vegetation index. We found that LOS linearly extended with on average 2.1 days per °C soil warming up to the highest soil warming levels (ca. +10°C) and that LOS had the potential to extend at least 1 month. This indicates that the warming impact on LOS in these subarctic grasslands will likely not saturate in the near future. A similar response to short- and long-term warming indicated a strong physiological control of the phenological response of the subarctic grasslands to warming and suggested that genetic adaptations and community changes were likely of minor importance. We conclude that the warming-driven extension of the LOSs of these subarctic grasslands did not saturate up to +10°C warming, and hence that growing seasons of high-latitude grasslands are likely to continue lengthening with future warming (unless genetic adaptations or species shifts do occur). This persistence of the warming-induced extension of LOS has important implications for the C-sink potential of subarctic grasslands under climate change

Precipitation impacts on vegetation spring phenology on the Tibetan Plateau

The ongoing changes in vegetation spring phenology in temperate/cold regions are widely attributed to temperature. However, in arid/semiarid ecosystems, the correlation between spring temperature and phenology is much less clear. We test the hypothesis that pre cipitation plays an important role in the temperature dependency of phenology in arid/semiarid regions. We therefore investigated the influence of preseason precipitation on satellite-derived estimates of starting date of vegetation growing season (SOS) across the Tibetan Plateau (TP). We observed two clear patterns linking precipitation to SOS. First, SOS is more sensitive to interannual variations in preseason precipitation in more arid than in wetter areas. Spatially, an increase in long-term averaged preseason precipitation of 10 mm corresponds to a decrease in the precipitation sensitivity of SOS by about 0.01 day mm−1. Second, SOS is more sensitive to variations in preseason temperature in wetter than in dryer areas of the plateau. A spatial increase in precipitation of 10 mm corresponds to an increase in temperature sensitivity of SOS of 0.25 day °C−1 (0.25 day SOS advance per 1 °C temperature increase). Those two patterns indicate both direct and indirect impacts of precipitation on SOS on TP. This study suggests a balance between maximizing benefit from the limiting climatic resource and minimizing the risk imposed by other factors. In wetter areas, the lower risk of drought allows greater temperature sensitivity of SOS to maximize the thermal benefit, which is further supported by the weaker interannual partial correlation between growing degree days and preseason precipitation. In more arid areas, maximizing the benefit of water requires greater sensitivity of SOS to precipitation, with reduced sensitivity to temperature. This study highlights the impacts of precipitation on SOS in a large cold and arid/semiarid region and suggests that influences of water should be included in SOS module of terrestrial ecosystem models for drylands

Movement ecology of gemsbok in the central Kalahari in response to vegetation greenness as assessed by satellite imagery

Centre of African Ecology Animal, Plants and the Environmental Sciences University of the Witwatersrand, Johannesburg.Arid African savannas experience seasonal, variable rainfall, resulting in unpredictable patterns in vegetation distribution. Understanding the spatio-temporal variability in primary productivity and the resulting behavioural responses of native herbivores is essential for the analysis of the vulnerability of savanna ecosystems to climatic and human-induced threats. The Central Kalahari Game Reserve (CKGR), Botswana, is open to free-ranging wildlife to its south and west. The mostly homogeneous dune landscape is interspersed with valley and pan systems, which deviate considerably from dune regions in their soil and vegetation structures. I assessed the phenology of green vegetation across the pan-valley and dune habitats of the northern CKGR, using Normalized Difference Vegetation Index (NDVI) imagery, and related variations in greenness to the ecology of gemsbok (Oryx gazella), a herbivore species that is highly adapted to arid conditions. Eight female gemsbok were collared in the northern CKGR, and their patterns of habitat selection and responses to three greenness measures (NDVI, ΔNDVI and Relative Greenness) were assessed using logistic regression models. Gemsbok 12-hour displacement distances for each herd were compared seasonally to assess whether gemsbok in the northern CKGR differ in their movement strategies depending on the prevailing environmental conditions at that point in time and space. The northern CKGR experiences high inter-annual variability in NDVI greenness and phenology. Pan-valley and dune habitats did not have significantly different rates of green-up or green season durations, but dune habitats had higher NDVI levels. Patches with the highest greenness levels showed little spatial persistence from year to year. Gemsbok did not select for higher NDVI or ΔNDVI, but they selected for categories of relative greenness that were higher than the lowest relative greenness level. Gemsbok selected pan-valleys over dunes during the green season, but were not selective during the brown season, probably as a result of the loss of green grasses in pan-valley habitats during this period. Finally, gemsbok had no specific general trend in seasonal displacement distances. Gemsbok in the CKGR are likely to be opportunistic feeders, and herds probably made varying behavioural decisions based on their immediate environmental conditions

Responses of Land Surface Phenology to Wildfire Disturbances in the Western United States Forests

Land surface phenology (LSP) characterizes the seasonal dynamics in the vegetation communities observed for a satellite pixel and it has been widely associated with global climate change. However, LSP and its long-term trend can be influenced by land disturbance events, which could greatly interrupt the LSP responses to climate change. Wildfire is one of the main disturbance agents in the western United States (US) forests, but its impacts on LSP have not been investigated yet. To gain a comprehensive understanding of the LSP responses to wildfires in the western US forests, this dissertation focused on three research objectives: (1) to perform a case study of wildfire impacts on LSP and its trend by comparing the burned and a reference area, (2) to investigate the distribution of wildfire impacts on LSP and identify control factors by analyzing all the wildfires across the western US forests, and (3) to quantify the contributions of land cover composition and other environmental factors to the spatial and interannual variations of LSP in a recently burned landscape. The results reveal that wildfires play a significant role in influencing spatial and interannual variations in LSP across the western US forests. First, the case study showed that the Hayman Fire significantly advanced the start of growing season (SOS) and caused an advancing SOS trend comparing with a delaying trend in the reference area. Second, summarizing \u3e800 wildfires found that the shifts in LSP timing were divergent depending on individual wildfire events and burn severity. Moreover, wildfires showed a stronger impact on the end of growing season (EOS) than SOS. Last, LSP trends were interrupted by wildfires with the degree of impact largely dependent on the wildfire occurrence year. Third, LSP modeling showed that land cover composition, climate, and topography co-determine the LSP variations. Specifically, land cover composition and climate dominate the LSP spatial and interannual variations, respectively. Overall, this research improves the understanding of wildfire impacts on LSP and the underlying mechanism of various factors driving LSP. This research also provides a prototype that can be extended to investigate the impacts on LSP from other disturbances

WILD BIRDS AND EMERGING DISEASES: MODELING AVIAN INFLUENZA TRANSMISSION RISK BETWEEN DOMESTIC AND WILD BIRDS IN CHINA

Emerging infectious diseases in wildlife have become a growing concern to human health and biological systems with more than 75 percent of known emerging pathogens being transmissible from animals to humans. Highly pathogenic avian influenza (HPAI) H5N1 has caused major global concern over a potential pandemic and since its emergence in 1996 has become the longest persisting HPAI virus in history. HPAI viruses are generally restricted to domestic poultry populations, however, their origins are found in wild bird reservoirs (Anatidae waterfowl) in a low-pathogenic or non-lethal form. Understanding the spatial and temporal interface between wild and domestic populations is fundamental to taking action against the virus, yet this information is lacking. My dissertation takes two approaches to increase our understanding of wild bird and H5N1 transmission. The first includes a field component to track the migratory patterns of bar-headed geese (Anser indicus) and ruddy shelduck (Tadorna ferruginea) from the large H5N1 outbreak at Qinghai Lake, China. The satellite telemetry study revealed a new migratory connection between Qinghai Lake and outbreak regions in Mongolia, and provided ecological data that supplements phylogenetic analyses of virus movement. The second component of my dissertation research took a modeling approach to identify areas of high transmission risk between domestic poultry and wild waterfowl in China, the epicenter of H5N1. This effort required the development of spatial models for both the poultry and wild waterfowl species of China. Using multivariate regression and AIC to determine statistical relationships between poultry census data and remotely-sensed environmental predictors, I generated spatially explicit distribution models for China's three main poultry species: chickens, ducks, and geese. I then developed spatially explicit breeding and wintering season models of presence-absence, abundance, and H5N1 prevalence for each of China's 42 Anatidae waterfowl species. The poultry and waterfowl datasets were used as the main inputs for the transmission risk models. Distinct patterns in both the spatial and temporal distributions of H5N1 risk was observed in the model predictions. All models included estimates of uncertainty, and sensitivity analyses were performed for the risk models

Understanding the spatiotemporal heterogeneity in grassland dynamics in Kenya’s semi-arid pastures

Grassland biomes are one of the largest terrestrial ecosystems on the planet, providing critical ecological, social, and economic benefits. However, they are subjected to natural and anthropogenic stresses such as precipitation, temperature variability, and widespread land degradation. Invasive plant species, for example, pose enormous challenges regarding biodiversity loss and degradation. Therefore, we need to keep up with and improve our knowledge of how they change, especially over space and time, to make good decisions about their productivity, management, and conservation. However, questions remain in (i) mapping and invasion science regarding a methodological framework for mapping invasive plant species (Opuntia stricta) using satellite remote sensing, (ii) understanding the spatiotemporal relationship between grassland greenness, communities, precipitation, temperature, and grazing factors, and (iii) our understanding of the spatial variation in grassland community types and their palatability probability. As a result, this study aimed to better understand the spatiotemporal dynamics in Kenya’s heterogeneous semi-arid grasslands by characterising the grassland into grassland communities and palatable and non-palatable plants. Additionally, it evaluates the intra-seasonal drivers of grassland changes at a site-specific level in 2019. The results show that combining Sentinel-2 spectral data, vegetation, and topographic indices is sufficient to map Opuntia stricta in a complex, heterogeneous semi-arid landscape. Additionally, precipitation, temperature and grazing, though at different times, are the major drivers of intra-seasonal grassland dynamics in semi-arid areas. Furthermore, the study found Sentinel-2 imagery to be adequate in achieving fine-scale spatial variations in grassland communities and inferring palatability probability in heterogeneous semi-arid grasslands. Finally, these findings and recommendations can help us better understand grassland dynamics and uncertainty modelling, as well as improve our understanding of plant-animal interactions, which can lead to management implications for rangeland management in terms of productivity, conservation, and rehabilitation

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)

Earth resources: A continuing bibliography with indexes (issue 59)

This bibliography lists 518 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1988. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors