Estimating and monitoring land surface phenology in rangelands: A review of progress and challenges

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment

A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

Scaling Near-Surface Remote Sensing To Calibrate And Validate Satellite Monitoring Of Grassland Phenology

Phenology across the U.S. Great Plains has been modeled at a variety of field sites and spatial scales. However, combining these spatial scales has never been accomplished before, and has never been done across multiple field locations. We modeled phenocam Vegetation Indices (VIs) across the Great Plains Region. We used coupled satellite imagery that has been aligned spectrally, for each imagery band to align with one another across the phenocam locations. With this we predicted the phenocam VIs for each year over the six locations.Using our method of coupling the phenocam VIs and the meteorological data we predicted 38 years of phenocam VIs. This resulted in a coupled dataset for each phenocam site across the four VIs. Using the coupled datasets, we were able to predict the phenocam VIs, and examine how they would change over the 38 years of data. While imagery was not available for modeling the 38 years of weather data, we found weather data could act as an acceptable proxy. This means we were able to predict 38 years of VIs using weather data. A main assumption with this method, it that no major changes in the vegetation community took place in the 33 years before the imagery. If a large change did take place, it would be missed because of the data lacking to represent it. Using the phenocam and satellite imagery we were able to predict phenocam GCC, VCI, NDVI, and EVI2 and model them over a five-year period. This modeled six years of phenocam imagery across the Great Plains region and attempted to predict the phenocam VIs for each pixel of the satellite imagery. The primary challenge of this method is aggregating grassland predicted VIs with cropland. This region is dominated by cropland and managed grasslands. In many cases the phenology signal is likely driven by land management decisions, and not purely by vegetation growth characteristics. Future models that take this into account may provide a more accurate model for the region

Changing landscapes: Compositional and phenological shifts in New Zealand's natural grassland

Vegetation in a wide range of ecosystems across the globe is responding to recent anthropogenic climate change. There are two key ecological responses in plants associated with recent anthropogenic climate change: shifts in species’ geographic distributions (range shifts) and shifts in the timing of key life cycle events (phenological shifts). These shifts can lead to temporal and spatial changes in vegetation composition and growth activity and hence ecosystem function. Understanding the patterns and processes of these shifts is crucial for the successful management of natural ecosystems under ongoing anthropogenic environmental change. This thesis investigates recent spatiotemporal compositional and phenological shifts in New Zealand’s natural grassland ecosystems and identifies potential topographical and climatic drivers of these shifts. Three grassland types in New Zealand are investigated (Alpine, Tall Tussock and Low Producing grasslands). They are characterised by high levels of indigenous endemic plant biodiversity and cover a wide elevation range. This thesis primarily utilises remote sensing information for quantifying growth dynamics and vegetation patterns in these grasslands over the last 16 years and across large spatial scales, i.e., the catchment of the river Clutha/Mata-Au River in South Island, New Zealand. Shrub encroachment in grassland ecosystems is a globally observed example of compositional shifts in ecosystems associated with recent anthropogenic climate change. In New Zealand, where extensive area of current grassland habitats exist because of anthropogenic deforestation, shrub encroachment into grasslands has two distinct facets: firstly the invasion of non-native shrub species into native grasslands (i.e., exotic shrub invasion) and secondly the dispersal of native woody and shrub species into native grasslands (i.e., native shrub recovery). Propagule pressure is a measurement of species’ seed source size in neighbourhood of a focal area, and it is a key determinant of the degree to which a location gets colonised by individuals from species present in the neighbourhood. The spatial patterns of potential native and exotic shrub propagule pressure on three grassland types in New Zealand were quantified with the assumption that proximity of higher shrub coverage indicates higher shrub propagule availability. Results show that Alpine grasslands are mostly surrounded by native shrublands, while Low producing grassland are most at risk from exotic shrub invasion from neighbouring areas. High native and exotic shrub propagule pressure does not generally coincide spatially, however, it occurs in very similar climates for Low Producing grassland but not for Alpine and Tall Tussock grassland. The analysis of recent shrub encroachment over the last five years in a tussock grassland area in the central South Island showed a 0.35% year-1 increase in shrub cover in grassland area located in immediate neighbourhood of shrub. Shrub encroachment speed was strongly correlated with shrub cover in the neighbourhood. Recent shrub encroachment into grasslands was most pronounced in areas with neighbouring shrub cover of greater than 40%. A wide range of species and ecosystems worldwide have shown changes in the timing of life cycle events and growing seasons in a direction congruent with recent anthropogenic climate changes. In this study, temporal trends over the last 16 years in the start, peak and end dates of the growing season were analysed using remotely sensed data on the Normalised Difference Vegetation Index (NDVI) in New Zealand’s three main grassland types. Overall, 90% of Alpine, 86% of Tall Tussock and 89% of Low Producing grassland areas showed an advancing start of the growing season over the last 16 years. In these areas start of the growing advanced by 7.2, 6.0 and 8.8 days per decade in Alpine, Tall Tussock and Low Producing grassland, respectively. Only small changes in timing of the end of the growing season were observed in the three grassland types. The length of growing season extended by 3.2, 5.2 and 7.1 days per decade in three grassland types. Landscape topography (elevation and aspect) played an important role in particular in alpine grasslands: the start of the growing season was strongly correlated with elevation (later start with increasing elevation), while the end of the growing season was strongly correlated with aspect (later end of season on more south-facing slopes). The start of season was delayed by 7.5, 5.1 and 3.7 days/100 m elevation increase in Alpine, Tall Tussock and Low producing grassland, separately. The end of season was advanced by 1.7 (Alpine), 1.3 (Tall Tussock) and delayed by 0.3 (Low Producing) days/10-degree-south on the slopes in these three grassland types. The results from this thesis show that recent shrub invasion into New Zealand grasslands is highest near shrub areas once a threshold of shrub cover in the neighbourhood is reached. Shrub encroachment was highest at lower elevations and on north-facing slopes. It also highlighted a measurable shift to an earlier start and extended length of the growing season in New Zealand’s main grassland types over the last 16 years, but the magnitude of these shifts showed considerable geographic variation. Importantly, this study has shown a high degree of topographical control on the timing of the growing in New Zealand’s grasslands with elevation and aspect acting differentially on start and end of the growing season. This highlights the importance of landscape heterogeneity and microclimates for ecosystem responses to climate change. This study shows that remotely sensed data can be successfully used to elucidate ecosystem-level shifts in temporal dynamics and spatial patterns of vegetation growth in grassland ecosystems