All models of satellite-derived phenology are wrong, but some are useful: a case study from northern Australia

Satellite-derived phenology (or apparent phenology) is frequently used to illustrate changes in plant phenology (i.e. true phenology) and the effects of climate forcing. However, each study uses a different method to detect phenology. Plant phenology refers to the relationship between the life cycle of plants and weather and climate events. Phenology is often studied in the field, but recently studies have transitioned towards using satellite images to monitor phenology at the plot, country, and continental scales. The problem with this approach is that there is an ever-increasing variety of earth observation satellites collecting data with different spatial, spectral, and temporal characteristics. In this paper we ask if studies that detect phenology using different sensors over the same site produce comparable results. Mangrove forests are one example where different methods have been used to examine their apparent phenology. In general, plant phenology, including mangroves, is described using few individual plants, but continental-scale descriptions of phenological events are scarce or inexistent. Few attempts have been made to describe the phenology of mangroves using satellite imagery, and each study presents a different method. We hypothesize that apparent phenology changes with: 1) areal extent; 2) site location; 3) frequency of observation; 4) spatial resolution; 5) temporal coverage; and 6) the number of cloud contaminated observations. Intuitively, one would assume that these hypotheses hold true, yet few studies have investigated this. For example, one would expect that clouds change the observed phenology of vegetation, that the number of species captured at spatial resolution will impact the apparent phenology, or that mangroves in different places display different phenologies, but how are these changes represented in the apparent phenology? We use the Enhanced Vegetation Index (EVI) to examine the changes in the start of season and peak growing season dates, as well as the shape and amplitude of the apparent phenology in each hypothesis. We use Landsat and Sentinel 2 imagery over the mangrove forests in Darwin Harbour (Northern Territory, Australia) as a case study, and found that apparent phenology does change with the sensor, site, and cloud contamination. Importantly, the apparent phenology is comparable between Landsat and Sentinel 2 sensors, but it is not comparable to phenology derived from MODIS. This is due to differences in the spatial resolution of the sensors. Cloud contamination also significantly changes the apparent phenology of vegetation. In this paper we expose the complexity of modelling phenology with remote sensing and help guide future phenology investigations

Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics

Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth’s land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results.Peer Reviewe

Leveraging big satellite image and animal tracking data for characterizing large mammal habitats

Die zunehmende Verfügbarkeit von Satellitenfernerkundungs- und Wildtier-Telemetriedaten eröffnet neue Möglichkeiten für eine verbesserte Überwachung von Wildtierhabitaten durch Habitatmodelle, doch fehlt es häufig an geeigneten Ansätzen, um dieses Potenzial voll auszuschöpfen. Das übergeordnete Ziel dieser Arbeit bestand in der Konzipierung und Weiterentwicklung von Ansätzen zur Nutzung des Potenzials großer Satellitenbild- und Telemetriedatensätze in Habitatmodellen. Am Beispiel von drei großen Säugetierarten in Europa (Eurasischer Luchs, Rothirsch und Reh) wurden Ansätze entwickelt, um (1) Habitatmodelle mit dem umfangreichsten global und frei verfügbaren Satellitenbildarchiv der Landsat-Satelliten zu verknüpfen und (2) Wildtier-Telemetriedaten über Wildtierpopulationen hinweg in großflächigen Analysen der Habitateignung und -nutzung zu integrieren. Die Ergebnisse dieser Arbeit belegen das enorme Potenzial von Landsat-basierten Variablen als Prädiktoren in Habitatmodellen, die es ermöglichen von statischen Habitatbeschreibungen zu einem kontinuierlichen Monitoring von Habitatdynamiken über Raum und Zeit überzugehen. Die Ergebnisse meiner Forschung zeigen darüber hinaus, wie wichtig es ist, die Kontextabhängigkeit der Lebensraumnutzung von Wildtieren in Habitatmodellen zu berücksichtigen, insbesondere auch bei der Integration von Telemetriedatensätzen über Wildtierpopulationen hinweg. Die Ergebnisse dieser Dissertation liefern neue ökologische Erkenntnisse, welche zum Management und Schutz großer Säugetiere beitragen können. Darüber hinaus zeigt meine Forschung, dass eine bessere Integration von Satellitenbild- und Telemetriedaten eine neue Generation von Habitatmodellen möglich macht, welche genauere Analysen und ein besseres Verständnis von Lebensraumdynamiken erlaubt und so Bemühungen zum Schutz von Wildtieren unterstützen kann.The growing availability of satellite remote sensing and animal tracking data opens new opportunities for an improved monitoring of wildlife habitats based on habitat models, yet suitable approaches for making full use of this potential are commonly lacking. The overarching goal of this thesis was to develop and advance approaches for harnessing the potential of big satellite image and animal tracking data in habitat models. Specifically, using three large mammal species in Europe as an example (Eurasian lynx, red deer, and roe deer), I developed approaches for (1) linking habitat models to the largest global and freely available satellite image record, the Landsat image archive, and (2) for integrating animal tracking datasets across wildlife populations in large-area assessments of habitat suitability and use. The results of this thesis demonstrate the enormous potential of Landsat-based variables as predictors in habitat models, allowing to move from static habitat descriptions to a continuous monitoring of habitat dynamics across space and time. In addition, my research underscores the importance of considering context-dependence in species’ habitat use in habitat models, particularly also when integrating tracking datasets across wildlife populations. The findings of this thesis provide novel ecological insights that help to inform the management and conservation of large mammals and more broadly, demonstrate that a better integration of satellite image and animal tracking data will allow for a new generation of habitat models improving our ability to monitor and understand habitat dynamics, thus supporting efforts to restore and protect wildlife across the globe

Examining spatiotemporal changes in the phenology of Australian mangroves using satellite imagery

Nicolás Younes investigated the phenology of Australian mangroves using satellite imagery, field data, and generalized additive models. He found that satellite-derived phenology changes with location, frequency of observation, and spatial resolution. Nicolás challenges the common methods for detecting phenology and proposes a data-driven approach

Near real-time monitoring of forest disturbance: a multi-sensor remote sensing approach and assessment framework

Fast and accurate monitoring of tropical forest disturbance is essential for understanding current patterns of deforestation as well as helping eliminate illegal logging. This dissertation explores the use of data from different satellites for near real-time monitoring of forest disturbance in tropical forests, including: development of new monitoring methods; development of new assessment methods; and assessment of the performance and operational readiness of existing methods. Current methods for accuracy assessment of remote sensing products do not address the priority of near real-time monitoring of detecting disturbance events as early as possible. I introduce a new assessment framework for near real-time products that focuses on the timing and the minimum detectable size of disturbance events. The new framework reveals the relationship between change detection accuracy and the time needed to identify events. In regions that are frequently cloudy, near real-time monitoring using data from a single sensor is difficult. This study extends the work by Xin et al. (2013) and develops a new time series method (Fusion2) based on fusion of Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data. Results of three test sites in the Amazon Basin show that Fusion2 can detect 44.4% of the forest disturbance within 13 clear observations (82 days) after the initial disturbance. The smallest event detected by Fusion2 is 6.5 ha. Also, Fusion2 detects disturbance faster and has less commission error than more conventional methods. In a comparison of coarse resolution sensors, MODIS Terra and Aqua combined provides faster and more accurate detection of disturbance events than VIIRS (Visible Infrared Imaging Radiometer Suite) and MODIS single sensor data. The performance of near real-time monitoring using VIIRS is slightly worse than MODIS Terra but significantly better than MODIS Aqua. New monitoring methods developed in this dissertation provide forest protection organizations the capacity to monitor illegal logging events promptly. In the future, combining two Landsat and two Sentinel-2 satellites will provide global coverage at 30 m resolution every 4 days, and routine monitoring may be possible at high resolution. The methods and assessment framework developed in this dissertation are adaptable to newly available datasets

Biomass Burning in the Conterminous United States: A Comparison and Fusion of Active Fire Observations from Polar-Orbiting and Geostationary Satellites for Emissions Estimation

Biomass burning is an important source of atmospheric greenhouse gases and aerosol emissions that significantly influence climate and air quality. Estimation of biomassburning emissions (BBE) has been limited to the conventional method in which parameters (i.e., burned area and fuel load) can be challenging to quantify accurately. Recent studies have demonstrated that the rate of biomass combustion is a linear function of fire radiative power (FRP), the instantaneous radiative energy released from actively burning fires, which provides a novel pathway to estimate BBE. To obtain accurate and timely BBE estimates for near real-time applications (i.e., air quality forecast), the satellite FRP-based method first requires a reliable biomass combustion coefficient that converts fire radiative energy (FRE), the temporal integration of FRP, to biomass consumption. The combustion coefficient is often derived in controlled small-scale fire experiments and is assumed a constant, whereas the coefficient based on satellite retrievals of FRP and atmospheric optical depth is suggested varying in a wide range. Undoubtedly, highly variable combustion coefficient results in large uncertainty of BBE estimates. Further, the FRP-based method also depends on high-spatiotemporalresolution FRP retrievals that, however, are not available in any active fire products from current polar-orbiting and geostationary satellites due to their sampling limitations. To address these challenges, this study first investigates the combustion coefficient for landscape-scale wildfires in the Conterminous United States (CONUS) by comparing FRE from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellite system (GOES) with the Landsat-based biomass consumption. The results confirms that biomass consumption is a linear function of FRE for wildfires. The derived combustion coefficient is 0.374 kg · MJ- 1 for GOES FRE, 0.266 kg · MJ-1 for MODIS FRE, and 0.320 kg · MJ-1 considering both GOES and MODIS FRE in the CONUS. Limited sensitivity analyses indicate that the combustion coefficient varies from 0.301 to 0.458 kg · MJ-1, which is similar to the reported values in small fire experiments. Then, this study reconstructs diurnal FRP cycle to derive high-spatiotemporal-resolution FRP by fusing MODIS and GOES FRP retrievals and estimates hourly BBE at a 0.25°×0.3125° grid across the CONUS. The results indicate that the reconstructed diurnal FRP cycle varies significantly in magnitude and shape among 45 CONUS ecosystems. In the CONUS, the biomass burning annually releases approximately 690 Gg particulate matter (smaller than 2.5 μm in diameter, PM2.5). The diurnal-FRP-cycle-based BBE estimates compare well with BBE derived from Landsat burned areas in the western CONUS and with the hourly carbon monoxide emissions simulated using a biogeochemical model over the Rim Fire in California. Moreover, the BBE estimates show a similar seasonal variation to six existing BBE inventories but with variable magnitude. Finally, this study examines potential improvements in fires characterization capability of the Visible Infrared Imaging Radiometer Suite (VIIRS), which is the follow-on sensor of the MODIS sensor, for integrating VIIRS FRP retrievals into the FRP-based method for BBE estimation in future work. The results indicate that the VIIRS fire characterization capability is similar across swath, whereas MODIS is strongly dependent on satellite view zenith angle. VIIRS FRP is generally comparable with contemporaneous MODIS FRP at continental scales and in most fire clusters. At 1-degree grid cells, the FRP difference between the two sensors is, on average, approximately 20% in fire-prone regions but varies significantly in fire-limited regions. In summary, this study attempts to enhance the capability of the FRP-based method by addressing challenges in its two parameters (combustion coefficient and FRP), which should help to improve estimation of BBE and advance our understanding of the effects of BBE on climate and air quality. This research has resulted in two published papers and one paper to be submitted to a peer-reviewed journal so far

A new approach for estimating northern peatland gross primary productivity using a satellite-sensor-derived chlorophyll index

Carbon flux models that are largely driven by remotely sensed data can be used to estimate gross primary productivity (GPP) over large areas, but despite the importance of peatland ecosystems in the global carbon cycle, relatively little attention has been given to determining their success in these ecosystems. This paper is the first to explore the potential of chlorophyll-based vegetation index models for estimating peatland GPP from satellite data. Using several years of carbon flux data from contrasting peatlands, we explored the relationships between the MERIS terrestrial chlorophyll index (MTCI) and GPP, and determined whether the inclusion of environmental variables such as PAR and temperature, thought to be important determinants of peatland carbon flux, improved upon direct relationships. To place our results in context, we compared the newly developed GPP models with the MODIS (Moderate Resolution Imaging Spectrometer) GPP product. Our results show that simple MTCI-based models can be used for estimates of interannual and intra-annual variability in peatland GPP. The MTCI is a good indicator of GPP and compares favorably with more complex products derived from the MODIS sensor on a site-specific basis. The incorporation of MTCI into a light use efficiency type model, by means of partitioning the fraction of photosynthetic material within a plant canopy, shows most promise for peatland GPP estimation, outperforming all other models. Our results demonstrate that satellite data specifically related to vegetation chlorophyll content may ultimately facilitate improved quantification of peatland carbon flux dynamics

Using MODIS Satellite Images to Confirm Distributed Snowmelt Model Results in a Small Arctic Watershed

Environmental analysts face the problem of obtaining distributed measurements to evaluate the performance of models with increasingly small spatiotemporal resolution. While U.S. government agencies readily provide both measurement products and data tools for the study of global change occurring over entire seasons and across continental areas, analysts need access to the low-level data that provides the basis for global products. Finally, analysts need to consider sensor errors inherent in low-level products that are accounted for in global, composite products. Hydrologists using tools for managing low-level snow swath measurements, in particular, must consider how measurements are affected by sensor errors like snow-cloud confusion and sensor errors due to low ground illumination at night. This thesis aims to explore the use of remotely sensed snow maps to confirm a time series of model maps. Specifically, snow covered area (SCA) measurements remotely sensed by the National Aeronautics and Space Administration (NASA) are used to confirm SCA predictions modeled by the United States Agriculture Department (USDA). The measurements come from the two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard near-polar, sun-synchronous satellites named Aqua and Terra. The USDA calls the model TOPMODEL-Based Land-Atmosphere Transfer Scheme (TOPLATS). The Upper Kuparuk River Watershed (UKRW) on the North Slope of Alaska acts as the case study location. To meet the map-comparison goal, the Kappa statistic, Kappa statistic variants, and probability density functions expressing measurement uncertainty in discrete scenes all evaluate the ability of MODIS measurements to confirm the accuracy of TOPLATS model maps. Data management objectives to make measured data accessible and comparable to the model output comprise a supporting goal. Results show that individual composite statistics, like the proportion of agreement between two maps, can easily obscure spatiotemporally distributed confirmation information without additional statistics and side-by-side images of measurement maps and model maps. These tools show some promise for using MODIS to confirm model predictions of snowmelt that occur across less than 150 km2 and less than a few days, however, clouds and malfunctioning sensors limit such use

Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal

Availability of remote sensed data provides powerful access to the spatial and temporal information of the earth surface Real-time earth observation data acquired during a cropping season can assist in assessing crop growth and development performance As remote sensed data is generally available at large scale rather than at field-plot level use of this information would help to improve crop management at broad-scale Utilizing the Landsat TM ETM ISODATA clustering algorithm and MODIS Terra the normalized difference vegetation index NDVI and enhanced vegetation index EVI datasets allowed the capturing of relevant rice cropping differences In this study we tried to analyze the MODIS Terra EVI NDVI February 2000 to February 2013 datasets for rice fractional yield estimation in Narowal Punjab province of Pakistan For large scale applications time integrated series of EVI NDVI 250-m spatial resolution offer a practical approach to measure crop production as they relate to the overall plant vigor and photosynthetic activity during the growing season The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS Sinusoidal to the national coordinate systems However its low spatial resolution has been an impediment to researchers pursuing more accurate classification results and will support environmental planning to develop sustainable land-use practices These results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data and assist for forthcoming rice fractional yield assessmen