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)

Augmenting Land Cover/Land Use Classification by Incorporating Information from Land Surface Phenology: An Application to Quantify Recent Cropland Expansion in South Dakota

Understanding rapid land change in the U.S. NGP region is not only critical for management and conservation of prairie habitats and ecosystem services, but also for projecting production of crops and biofuels and the impacts of land conversion on water quality and rural transportation infrastructure. Hence, it raises the need for an LCLU dataset with good spatiotemporal coverage as well as consistent accuracy through time to enable change analysis. This dissertation aims (1) to develop a novel classification method, which utilizes time series images from comparable sensors, from the perspective of land surface phenology, and (2) to apply the land cover/land use dataset generated from the phenometrically-based classification approach to quantify crop expansion in South Dakota. A novel classification approach from the perspective of land surface phenology (LSP) uses rich time series datasets. First, surface reflectance products at 30 m spatial resolution from Landsat Collection-1, its newer structure—Landsat Analysis Ready Data, and the Harmonized Landsat Sentinel-2 (HLS) data are used to construct vegetation index time series, including the Enhanced Vegetation Index (EVI), and the 2-band EVI (EVI2), and various spectral variables (spectral band and normalized ratio composites). MODIS Level-3 Land Surface Temperature & Emissivity 8-day composite products at 1 km spatial resolution from both the Aqua and Terra satellites are used to compute accumulated growing degree-days (AGDD) time series. The EVI/EVI2 and AGDD time series are then fitted by two different land surface phenology models: the Convex Quadratic model and the Hybrid Piecewise Logistic Model. Suites of phenometrics are derived from the two LSP models and spectral variables and input to Random Forest Classifiers (RFC) to map land cover of sample areas in South Dakota. The results indicate that classifications using only phenometrics can accurately map major crops in the study area but show limited accuracy for non-vegetated land covers. RFC models using the combined spectralphenological variables can achieve higher accuracies than those using either spectral variables or phenometrics alone, especially for the barren/developed class. Among all sampling designs, the “same distribution” models—proportional distribution of the sample is like proportional distribution of the population—tends to yield best land cover prediction. A “same distribution” random sample dataset covering approximately 0.25% or more of the study area appears to achieve an accurate land cover map. To characterize crop expansion in South Dakota, a trajectory-based analysis, which considers the entire land cover dataset generated from the LSP-based classifications, is proposed to improve change detection. An estimated cropland expansion of 5,447 km2 (equivalent to 14% of the existing cropland area) occurred between 2007 and 2015, which matches more closely the reports from the National Agriculture Statistics Service—NASS (5,921 km2) and the National Resources Inventory—NRI (5,034 km2) than an estimation from a bi-temporal change approach (8,018 km2). Cropland gains were mostly concentrated in 10 counties in northern and central South Dakota. An evaluation of land suitability for crops using the Soil Survey Geographic Database—SSURGO indicates a scarcity in high-quality arable land available for cropland expansion

Characterizing an Annual Grass Invasion and Its Link to Environmental and Disturbance Factors Using Remote Sensing: New Tools and Applications

The spread of nonnative species across the globe has contributed to biodiversity loss and changes in ecosystem structure and function. Monitoring the introduction, naturalization, and spread of introduced species is critical in abating negative impacts wrought by species invasions. However, providing basic information concerning the presence or spread of many introduced species is often only considered once the invasion is already at an advanced stage, resulting in economic or ecological impacts. To better assess the present and future effects of and risk from introduced species, a clear understanding of invasive species populations' spatial and temporal patterns is needed. In some cases, remote sensing can serve as a useful information source that may be leveraged to characterize and monitor the invasion of nonnative species. This dissertation utilizes remote sensing and other geospatial data sources to better understand a nonnative annual grass (Ventenata dubia) invasion in the northwestern United States. Each research chapter builds a different facet of our understanding of this invasion by connecting land-surface processes, environmental conditions, and landscape disturbances. These three different topics help to describe the current state of the invasion, how it progressed to this state, and what this may mean for the future. The first research-chapter adapts image fusion methods to a cloud-computing environment in an effort to improve the spatial and temporal resolution of estimates of land surface phenology. The research focused on whether these methods would enable the estimation of phenology in heterogeneous landscapes that have historically been difficult to characterize. This chapter showed that high-quality image fusion results are possible with less processing time when image fusion is conducted in a cloud-computing environment. Further, this chapter showed that phenology estimated from these data can capture patterns occurring in grassland, shrubland, and open forest land cover types. The second research-chapter leverages the improved land surface phenology estimates from the first research-chapter to model the present distribution of the invasive annual grass species Ventenata dubia in the Blue Mountains Ecoregion of the interior Pacific Northwest. The results from this chapter suggest that both phenological and environmental information are needed to best detect populations of ventenata. The model based on phenological and environmental information predicted that ventenata was present in 7.8% of the Blue Mountains Ecoregion in 2017. The third research-chapter uses the information gained from the proceeding chapters to examine the change occurring over a decade of invasion by applying the model developed in the second research-chapter to the image archive and examining the invasion progression. Spatial and temporal patterns of the invasion were characterized by their association with the biophysical environment and the effect of wildfire on ventenata occurence was investigated. This analysis revealed that ventenata may have been introduced to lower shrubland ecosystems but has since transitioned to higher elevation dry conifer forests and areas with abundant ecotone. Furthermore, this chapter shows that wildfire occurrence and severity was associated with an increased probability of invasion in some parts of the interior Pacific Northwest. Although this research is focused on a specific annual grass species (Ventenata dubia), insights gained from this investigation are applicable to other invasive annual grasses. This research contributes to the scientific advancement in the study of exotic plant invasion and provides useful baseline ecological information that can be employed to inform both policy and management. Additionally, the methods developed for cloud-computing-based image fusion offer a useful tool to the remote sensing community that has the flexibility to be utilized for many applications

Time Series Analysis of Long-Term Vegetation Trends, Phenology, and Ecosystem Service Valuation for Grasslands in the U.S. Great Plains

Doctor of PhilosophyDepartment of GeographyJ. M. Shawn HutchinsonGrasslands are one of the largest, most biodiverse, and productive terrestrial biomes but they receive very low levels of protection. The temperate grasslands in the United States are one of the most threatened grassland ecosystems. Every year, a significant portion of grasslands in the Great Plains are converted to agricultural use, with almost 96% of the historical extent lost. Other factors that affect existing grassland health include significant climatic changes, invasion of woody, non-native species, fragmentation, lack or inadequate burning, and excessive grazing. The impact of the combination of these factors on grasslands in the US Great Plains is still unknown. The goal of this research is to investigate the long-term grassland vegetation conditions using a well-known indicator (greenness) and assesses its impact on the provision of select grassland ecosystem services within the US Great Plains from 2001 to 2017. The above goal was achieved with three objectives addressed in three chapters. In Chapter 3, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 6) was performed to assess long-term trends in vegetation greenness across the Great Plains ecoregion of the United States. The Breaks for Additive Season and Trend (BFAST) decomposition method was applied to a time series of images from 2001 to 2017 to derive spatially explicit estimates of gradual interannual change. Results show more 'greening' trends than 'browning' and 'no change' trends during the study period. Comparing the trend results from both vegetation indices suggests that EVI is more suitable for this analysis in the study area, especially in areas with high biomass. In Chapter 4, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 5) is used to explore spatial patterns of vegetation phenology and to assess long-term phenology trends across the region. The program TIMESAT was used to extract key measures of vegetation phenological development from 2001 to 2017, including the phenometrics (1) season length, (2) start of growing season, (3) end of growing season, (4) middle of the growing season, (5) maximum NDVI value, (6) small integral, (7) left derivative, and (8) right derivative. Results show important variation in phenological patterns across the region such as a shift to a later start, earlier end, and shorter the growing season length, especially in the southern parts of the region. As shown in the small integral and maximum EVI, vegetation productivity appears to have increased over many areas in the Great Plains ecoregion. Finally, Chapter 5 focuses on developing a methodological improvement to the widely used Invest ecosystem services model that uses remotely sensed inputs to capture the interannual spatio-temporal dynamics of grassland vegetation on the provision of grassland ecosystem services across the US Great Plains. A selected set of grassland ecosystem services was quantified (economic and biophysical values) for the period between 2001 and 2017. This exploratory study will be a basis for highlighting the role grasslands play in providing essential ecosystem services and how improved long-term vegetation monitoring can benefit land-use decisions locally and regionally

Time series analysis of phenometrics and long-term vegetation trends for the Flint Hills ecoregion using moderate resolution satellite imagery

Master of ArtsDepartment of GeographyJ. M. Shawn HutchinsonGrasslands of the Flint Hills are often burned as a land management practice. Remote sensing can be used to help better manage prairie landscapes by providing useful information about the long-term trends in grassland vegetation greenness and helping to quantify regional differences in vegetation development. Using MODIS 16-day NDVI composite imagery between the years 2001-10 for the entire Flint Hills ecoregion, BFAST was used to determine trend, seasonal, and noise components of the image time series. To explain the trend, 4 factors were considered including hydrologic soil group, burn frequency, and precipitation deviation from the 30 year normal. In addition, the time series data was processed using TIMESAT to extract eight different phenometrics: Growing season length, start of season, end of season, middle of season, maximum value, small integral, left derivative, and right derivative. Phenometrics were produced for each year of the study and an ANOVA was performed on the means of all eight phenometrics to assess if significant differences existed across the study area. A K-means cluster analysis was also performed by aggregating pixel-level phenometrics at the county level to identify administrative divisions exhibiting similar vegetation development. For the study period, the area of negatively and positively trending grassland were similar (41-43%). Logistic regression showed that the log odds of a pixel experiencing a negative trend were higher in sites with clay soils and higher burning frequencies and lower for pixels having higher than normal precipitation and loam soils. Significant differences existed for all phenometrics when considering the ecoregion as a whole. On a phenometric-by-phenometric basis, unexpected groupings of counties often showed statistically similar values. Similarly, when considering all phenometrics at the same time, counties clustered in surprising patterns. Results suggest that long-term trends in grassland conditions warrant further attention and may rival other sources of grassland change (e.g., conversion, transition to savannah) in importance. Analyses of phenometrics indicates that factors other than natural gradients in temperature and precipitation play a significant role in the annual cycle of grassland vegetation development. Unanticipated, and sometimes geographically disparate, groups of counties were shown to be similar in the context of specific phenology metrics and this may prove useful in future implementations of smoke management plans within the Flint Hills

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.E.A. was supported by the predoctoral scholarship, grant number ACIF/2019/187, funded by the Generalitat Valenciana and co-funded by the European Social Fund. J.V. and S.B. were supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project, grant number 755617. J.V. was additionally supported by a Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities). S.B. was additionally supported by the Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union—NextGenerationEU (ZAMBRANO 21-04)

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global Drylands: A UN system-wide response, 2011). In this research, a combination of remote sensing, field data collection, and ecosystem modeling were used to establish an integrated framework for semi-arid ecosystems dynamics monitoring. Foliar nitrogen (N) plays an important role in vegetation processes such as photosynthesis and there is wide interest in retrieving this variable from hyperspectral remote sensing data. In this study, I used the theory of canopy spectral invariants (AKA p-theory) to understand the role of canopy structure and soil in the retrieval of foliar N from hyperspectral data and machine learning techniques. The results of this study showed the inconsistencies among different machine learning techniques used for estimating N. Using p-theory, I demonstrated that soil can contribute up to 95% to the total radiation budget of the canopy. I suggested an alternative approach to study photosynthesis is the use of dynamic global vegetation models (DGVMs). Gross primary production (GPP) is the apparent ecosystem scale photosynthesis that can be estimated using DGVMs. In this study, I performed a thorough sensitivity analysis and calibrated the Ecosystem Demography (EDv2.2) model along an elevation gradient in a dryland study area. I investigated the GPP capacity and activity by comparing the EDv2.2 GPP with flux towers and remote sensing products. The overall results showed that EDv2.2 performed well in capturing GPP capacity and its long term trend at lower elevation sites within the study area; whereas the model performed worse at higher elevations likely due to the change in vegetation community. I discussed that adding more heterogeneity and modifying ecosystem processes such as phenology and plant hydraulics in ED.v2.2 will improve its application to higher elevation ecosystems where there is more vegetation production. And finally, I developed an integrated hyperspectral-lidar framework for regional mapping of xeric and mesic vegetation in the study area. I showed that by considering spectral shape and magnitude, canopy structure and landscape features (riparian zone), we can develop a straightforward algorithm for vegetation mapping in drylands. This framework is simple, easy to interpret and consistent with our ecological understanding of vegetation distribution in drylands over large areas. Collectively, the results I present in this dissertation demonstrate the potential for advanced remote sensing and modeling to help us better understand ecosystem processes in drylands

Assessing the Impact of Gold Mining on Forest Cover in the Surinamese Amazon Rainforest from 1997 - 2019: A Semi-Automated Satellite-Based Approach

The Amazon rainforest, as a biodiversity hotspot and regulator of the earths climate, is one of the most important ecosystems on earth, but has been facing extensive deforestation for decades due to urban growth, agricultural expansion, logging and mining. Mining (and the use of remote sensing methods to detect it) has been relatively understudied in the Amazon compared to the other drivers up until a decade ago, highlighting the importance of current research. The objectives of this study are: To quantify the increase in industrial and artisanal mining and its impact on forest cover in the northern Amazonian country of Suriname between 1997 and 2019; Evaluate the impact of this expansion on the structure (fragmentation) and health (phenology) of the forest; and improve existing remote sensing techniques for mining detection through the development of a pioneer method based on cloud processing and semi-automated mining reclassification. The cloud processing software known as Google Earth Engine (GEE) was used for the initial land use land cover classification of the study area. Landsat 5 and 8 images and the classification and regression trees (C.A.R.T) algorithm were used in this step. The resulting classified maps were fed into the semi-automated re-classification model developed for this study, producing final re-classified output maps, which were used to analyse the expansion of mining and its associated impacts on forest fragmentation and phenology. The proposed method is the first documented method which combines cloud processing with a semi-automated re-classification model, providing a technologically advanced approach capable of rapid and efficient detection of mines. This approach resulted in an 89.5% accuracy of mining detection, and the combination of speed, efficiency, and highly accurate detection outperformed many of the other currently documented methods for mining detection in the Amazon. The results highlighted that mining increased from 69.4km² in 1997 to 431.6km² in 2019, an increase of 522% over 22 years. This growth led directly to 351.9km² of forest loss, 83% of which was due to artisanal mining. This loss of forest led to a 122.8km² reduction in the effective mesh size for the artisanal mine sub-area, compared to a decrease of 83km² for the Industrial mine sub-area. Mining also caused a decrease in the health of the surrounding forest, with the decrease in peak greenness being more pronounced for artisanal mining compared to industrial mining. Recommendations for future research include exploring the use of higher resolution imagery such as Sentinel for better results, as well as the use of microwave data in the classification to combat the issue of extensive cloud cover in the Amazon. The issue of overclassification present in the proposed method can potentially be combated by exploring combinations of different classification algorithms with the reclassification model