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

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground

Grazing and Climate Effects on High Elevation Meadow Resources

Semi-arid rangelands cover roughly 41% of the Earth’s land surface, and house more than 38% of the human global population. The Greater sage-grouse (Centrocercus urophasianus) has commonly been used as an umbrella species for restoration of sagebrush ecosystems in rangelands, due to its status as an indicator of overall rangeland health. Scarce mesic resources may lead to an energetic bottleneck for juvenile sage grouse, limiting fitness and survival rates. Mesic and ground-water dependent ecosystems found in the Great Basin of North America are heavily utilized by livestock and wildlife throughout the year. It is important for land managers to understand how intensity and timing of grazing affect the temporal availability of mesic commodities utilized by species like sage-grouse. This dissertation quantifies changes in the timing of availability of mesic sage-grouse resources across grazing and climatic gradients in high-elevation meadows. The methods include both on the ground and remotely sensed tools, and the correlations between them are assessed. The results suggest that field determined phenology, phenocam Green Chromatic Coordinate (GCC), Phenocam Normalized Difference Vegetation Index (NDVI), and Landsat NDVI are all highly correlated, with slight de-coupling occurring at the end of the growing season. Timing of growth varied in these ecosystems depending on yearly precipitation and vegetative type. Arthropod taxa abundance responded differently to grazing management and environmental variables in these meadow communities. Coleoptera abundance during peak sage-grouse usage periods had an increase of roughly 40% in some meadows with increased grazing intensity, while Formicidae abundance saw a 22% decrease. Near-surface cameras had varied success with predicting peak insect abundance levels. Sage grouse usage of the meadows was highly linked to growth seasons of vegetation, with slight decoupling occurring with growth seasons derived from phenocam GCC in drier years. Little correlation was seen between peak sage grouse usage of the meadows and peak capture rates of arthropods, this was true for all insect groups (Coleoptera, Formicidae, and Lepidoptera)

Grazing and Climate Effects on High Elevation Meadow Resources

Semi-arid rangelands cover roughly 41% of the Earth’s land surface, and house more than 38% of the human global population. The Greater sage-grouse (Centrocercus urophasianus) has commonly been used as an umbrella species for restoration of sagebrush ecosystems in rangelands, due to its status as an indicator of overall rangeland health. Scarce mesic resources may lead to an energetic bottleneck for juvenile sage grouse, limiting fitness and survival rates. Mesic and ground-water dependent ecosystems found in the Great Basin of North America are heavily utilized by livestock and wildlife throughout the year. It is important for land managers to understand how intensity and timing of grazing affect the temporal availability of mesic commodities utilized by species like sage-grouse. This dissertation quantifies changes in the timing of availability of mesic sage-grouse resources across grazing and climatic gradients in high-elevation meadows. The methods include both on the ground and remotely sensed tools, and the correlations between them are assessed. The results suggest that field determined phenology, phenocam Green Chromatic Coordinate (GCC), Phenocam Normalized Difference Vegetation Index (NDVI), and Landsat NDVI are all highly correlated, with slight de-coupling occurring at the end of the growing season. Timing of growth varied in these ecosystems depending on yearly precipitation and vegetative type. Arthropod taxa abundance responded differently to grazing management and environmental variables in these meadow communities. Coleoptera abundance during peak sage-grouse usage periods had an increase of roughly 40% in some meadows with increased grazing intensity, while Formicidae abundance saw a 22% decrease. Near-surface cameras had varied success with predicting peak insect abundance levels. Sage grouse usage of the meadows was highly linked to growth seasons of vegetation, with slight decoupling occurring with growth seasons derived from phenocam GCC in drier years. Little correlation was seen between peak sage grouse usage of the meadows and peak capture rates of arthropods, this was true for all insect groups (Coleoptera, Formicidae, and Lepidoptera)

Automated Fractional Snow Cover Monitoring From Near-Surface Remote Sensing In Grassland

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. I developed a mostly semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research, which make use of RGB images only, the use of the monochrome RGB + NIR (near-infrared) channel reduced pixel misclassification and increased accuracy. The results have an average RMSE of 7.67 compared to visual estimates. This is a promising outcome, although not every PhenoCam system has NIR capability

Comparing time-Lapse PhenoCams with satellite observations across the Boreal forest of Quebec, Canada

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather

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

Fernerkundung der Vegetationsphänologie über MODIS NDVI Daten - Herausforderungen bei der Datenverarbeitung und -validierung mittels Bodenbeobachtungen zahlreicher Arten und LiDAR

Phenology, the cyclic events in living organisms is triggered by climatic conditions and indicators of climate change. They are important factors influencing species interactions and ecosystem functioning. This thesis deals with the estimation of phenological metrics (Land Surface Phenology or LSP) from MODIS based time series NDVI data. Results of data analysis emphasises the role of ground observations, topography and LiDAR characteristics of forest stand in describing the variability in LSP.Phänologie, die zyklischen Stadien von lebenden Organismen werden über klimatische Verhältnisse gesteuert und dienen als Indikatoren des Klimawandels. Diese Faktoren beeinflussen maßgeblich die Interaktionen zwischen Arten und sind für das Funktionieren von Ökosystemen ausschlaggebend. Diese Arbeit behandelt die Bestimmung von phänologischen Metriken (Phänologie der Landoberfläche oder LSP) unter Verwendung von MODIS basierten NDVI Zeitreihen. Die Ergebnisse der Datenanalyse hebt die Wichtigkeit von Bodenbeobachtungen, Topographie und LiDAR Merkmalen von Waldbeständen bei der Beschreibung der LSP Variabilität hervor

Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecosphere 9 (2018): e02337, doi:10.1002/ecs2.2337.Camera‐based observation of forest canopies allows for low‐cost, continuous, high temporal‐spatial resolutions of plant phenology and seasonality of functional traits. In this study, we extracted canopy color index (green chromatic coordinate, Gcc) from the time‐series canopy images provided by a digital camera in a deciduous forest in Massachusetts, USA. We also measured leaf‐level photosynthetic activities and leaf area index (LAI) development in the field during the growing season, and corresponding leaf chlorophyll concentrations in the laboratory. We used the Bayesian change point (BCP) approach to analyze Gcc. Our results showed that (1) the date of starting decline of LAI (DOY 263), defined as the start of senescence, could be mathematically identified from the autumn Gcc pattern by analyzing change points of the Gcc curve, and Gcc is highly correlated with LAI after the first change point when LAI was decreasing (R2 = 0.88, LAI < 2.5 m2/m2); (2) the second change point of Gcc (DOY 289) started a more rapid decline of Gcc when chlorophyll concentration and photosynthesis rates were relatively low (13.4 ± 10.0% and 23.7 ± 13.4% of their maximum values, respectively) and continuously reducing; and (3) the third change point of Gcc (DOY 295) marked the end of growing season, defined by the termination of photosynthetic activities, two weeks earlier than the end of Gcc curve decline. Our results suggested that with the change point analysis, camera‐based phenology observation can effectively quantify the dynamic pattern of the start of senescence (with declining LAI) and the end of senescence (when photosynthetic activities terminated) in the deciduous forest.Priority Academic Program Development of Jiangsu Higher Education Institutions in Discipline of Environmental Science and Engineer in Nanjing Forest University; China Scholarship Council Grant Number: 201506190095; Brown University Seed Funds for International Research Projects on the Environmen

Constraints on shrub cover and shrub-shrub competition in a U.S. southwest desert

The cover of woody perennial plants (trees and shrubs) in arid ecosystems is at least partially constrained by water availability. However, the extent to which maximum canopy cover is limited by rainfall and the degree to which soil water holding capacity and topography impacts maximum shrub cover are not well understood. Similar to other deserts in the U.S. southwest, plant communities at the Jomada Basin Long-Term Ecological Research site in the northern Chihuahuan Desert have experienced a long-term state change from perennial grassland to shrubland dominated by woody plants. To better understand this transformation, and the environmental controls and constraints on shrub cover, we created a shrub cover map using high spatial resolution images and explored how maximum shrub cover varies with landform, water availability, and soil characteristics. Our results indicate that when clay content is below similar to 18%, the upper limit of shrub cover is positively correlated with plant available water as mediated by surface soil clay influence on water retention. At surface soil day contents >18%, maximum shrub cover decreases, presumably because the amount of water percolating to depths preferentially used by deep-rooted shrubs is diminished. In addition, the relationship between shrub cover and density suggests that self-thinning occurs in denser stands in most landforms of the Jomada Basin, indicating that shrub-shrub competition interacts with soil properties to constrain maximum shrub cover in the northern Chihuahuan Desert.National Science Foundation [1235828]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]