Bridging structure and function in semi-arid ecosystems by integrating remote sensing and ground based measurements

The Southwestern US is projected to continue the current significant warming trend, with increased variability in the timing and magnitude of rainfall events. The effects of these changes in climate are already evident in the form of multi-year droughts which have resulted in the widespread mortality of woody vegetation across the region. Therefore, the need to monitor and model forest mortality and carbon dynamics at the landscape and regional scale is an essential component of regional and global climate mitigation strategies, and critical if we are to understand how the imminent state transitions taking place in forests globally will affect climate forcing and feedbacks. Remote sensing offers the only solution to multitemporal regional observation, yet many challenges exist with employing modern remote sensing solutions in highly stressed vegetation characteristic of semi-arid biomes, making one of the most expansive biomes on the globe also one of the most difficult to accu- rately monitor and model. The goal of this research was to investigate how changes in the structure of semi-arid woodlands following forest mortality impacts ecosystem function, and to determine how this question can be addressed using remotely sensed data sets. I focused primarily on Pinus edulis and Juniperous monosperma (piñon-juniper) woodlands, and took advantage of an existing manipulation experiment where mortality was imposed on all of the large piñon (¡ 7 cm dbh) in a 4 ha PJ woodland in 2009 and the ecosystem functional responses have been quantified using eddy covariance. A nearby intact PJ woodland, also instrumented with eddy covariance, was used as a control for this experiment. I tested the ability of high resolution remote sensing data to mechanistically describe the patterns in overstory mortality and understory green-up in this manipulated woodland by comparing it to the intact woodland, and observed the heterogeneous response of the understory as a function of cover type. I also investigated the relationship between changes in soil water content and the greenness of the canopy, noting that in the disturbed woodland, I observed a decoupling between how the canopy was measured remotely (e.g., via vegetation indices, VI) and photosynthesis. This is significant in that it potentially represents a significant source of error in using existing light use efficiency models of carbon uptake in these disturbed woodlands. This research also suggested that leveraging remote sensing data which measures in the red-edge portion of reflected light can provide increased sensitivity to the low leaf area, ephemeral pulses of greenup that were identified in the disturbed woodland, post-canopy mortality. Given these findings, I developed a hierarchy of simple linear models to test how well vegetation indices acquired through different spatial resolution sensors (Land- sat and RapidEye) were able to predict carbon uptake in both intact and disturbed piñon-juniper woodlands. The vegetation indices used were a moisture sensitive VI, and a red-edge leveraging VI from these sensors, and I compared estimates of carbon uptake derived from these models to the Gross Primary Productivity estimated from tower-based eddy covariance at both the manipulated and intact piñon-juniper sites. I determined that the red-edge VI and the moisture sensitive VI both constrained uncertainty associated with carbon uptake, but that the variability in satellite view angle from scene to scene can impose a significant amount of noise in sparse canopy ecosystems. Finally, given the extent and prevalence of J. monosperma across the region, and its complex growth morphology, I tested the ability of aerial lidar to quantify the biomass of juniper. In this simplified case study, I developed a method- ology to relate the volume of canopy measured via lidar to the equivalent stem area at the root crown. By working in a single species ecosystem, I circumvented many challenges associated with driving allometries remotely, but also present a work-flow that I intend to adapt to more complex systems, namely piñon-juniper woodlands. Together, this work describes and addresses existing challenges with respect to us- ing remote sensing to understand both the structure and function of piñon-juniper woodlands, and how it changes in response to widespread piñon mortality. It provides several new techniques to mitigate the difficulties associated with monitoring mortality / recovery dynamics, predicting canopy function, and determining ecosystem state parameters in these complex, sensitive biomes

The global tree carrying capacity (keynote)

editorial reviewe

Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Using satellite remote sensing to quantify woody cover and biomass across Africa

The goal of quantifying the woody cover and biomass of tropical savannas, woodlands and forests using satellite data is becoming increasingly important, but limitations in current scientific understanding reduce the utility of the considerable quantity of satellite data currently being collected. The work contained in this thesis reduces this knowledgegap, using new field data and analysis methods to quantify changes using optical, radar and LiDAR data. The first paper shows that high-resolution optical data (Landsat & ASTER) can be used to track changes in woody vegetation in the Mbam Djerem National Park in Cameroon. The method correlates a satellite-derived vegetation index with field-measured canopy cover, and the paper concludes that forest encroached rapidly into savanna in the region from 1986-2006. Using the same study area, but with radar remote sensing data from 1996 and 2007 (ALOS PALSAR & JERS-1), the second paper shows that radar backscatter correlates well with field-measured aboveground biomass (AGB). This dataset confirms the woody encroachment within the park; however, in a larger area around the park, deforestation dominates. The AGB-radar relationships described above are expanded in the next paper to include field plots from Budongo Forest (Uganda), the Niassa Reserve (north Mozambique), and the Nhambita Community Project (central Mozambique). A consistent AGB-radar relationship is found in the combined dataset, with the RMSE for predicted AGB values for a site increasing by <30 %, compared with a site-specific equation, when using an AGB-radar equation derived from the three other sites. The study of the Nhambita site is extended in the following paper to assess the ability of radar to detect change over short time periods in this environment, as will be needed for REDD (Reducing Emissions from Deforestation and Degradation). Using radar mosaics from 2007 and 2009, areas known (from detailed ground data) to have been degraded decreased in AGB in the radar change detection, whereas areas of agroforestry and forest protection showed small increases

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Remote sensing environmental change in southern African savannahs : a case study of Namibia

Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s most iconic species and most of its livestock and rangeland, while sustaining the livelihoods of an important proportion of its human population. They provide essential ecosystem services and functions, ranging from forest, grazing and water resources, to global climate regulation and carbon sequestration. However, savannahs are highly sensitive to human activities and climate change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing anthropogenic disturbances in the form of agricultural intensification and urbanization, have resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened ecosystems are some of the least studied or protected, and hence should be given high conservation priority. Importantly, the scale of land degradation has not been fully explored, thereby comprising an important knowledge gap in our understanding of ecosystem services and processes, and effectively impeding conservation and management of these biodiversity hotspots. The primary drivers of land degradation include deforestation, triggered by the increasing need for urban and arable land, and concurrently, shrub encroachment, a process in which the herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These processes have significant repercussions on ecosystem service provision, both locally and globally, although the extents, drivers and impacts of either remain poorly quantified and understood. Additionally, regional aridification anticipated under climate change, will lead to important shifts in vegetation composition, amplified warming and reduced carbon sequestration. Together with a growing human population, these processes are expected to compound the risk of land degradation, thus further impacting key ecosystem services. Namibia is undergoing significant environmental and socio-economic changes. The most pervasive change processes affecting its savannahs are deforestation, degradation and shrub encroachment. Yet, the extent and drivers of such change processes are not comprehensively quantified, nor are the implications for rural livelihoods, sustainable land management, the carbon cycle, climate and conservation fully explored. This is partly due to the complexities of mapping vegetation changes with satellite data in savannahs. They are naturally spatially and temporally variable owing to erratic rainfall, divergent plant functional type phenologies and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to (i) quantify distinct vegetation change processes across Namibia, and (ii) develop methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite data spanning a range of spatial, temporal and spectral resolutions are integrated with field datasets to achieve these aims, which are addressed in four journal articles. Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in land cover classes over five decades throughout the Namibian Kalahari woodlands. The approach addresses issues implicit in change detection of savannahs by capturing the distinct phenological phases of woody vegetation and integrating multi-sensor, multi-source data. Vegetation extent was found to have decreased due to urbanization and small-scale arable farming. An assessment of the limitations leads to Chapter 4, which elaborates on the previous chapter by quantifying aboveground biomass changes associated with deforestation and shrub encroachment. The approach centres on fusing multiple satellite datasets, each acting as a proxy for distinct vegetation properties, with calibration/validation data consisting of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating the contribution of land management to ecosystem carbon changes. To identify whether biomass is declining across the country, Chapter 5 focuses on regional, moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS data are used to model observed fractional woody vegetation cover, a proxy for biomass. Trends in modelled fractional woody cover are then evaluated in relation to the predominant land-uses and precipitation. Negative trends slightly outweighed positive trends, with decreases arising largely in protected, urban and communal areas. Since precipitation is a fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as a function of precipitation, with residuals assumed to describe the fraction of NDVI not explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend, although extensive “greening” is unrelated to rainfall. NDVI amplitude, used as a proxy for vegetation density, indicates a widespread shift to a denser condition. In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and temporal limitations characteristic of the previous approaches. Results, together with those of the previous chapters, are synthesized and a synopsis of the main findings is presented. Vegetation loss is predominantly caused by demand for urban and arable land. Greening trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry and rangeland management, with precipitation presenting little influence. Despite prevalent greening, degradation processes associated with shrub encroachment, including soil erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment occurs regionally. This thesis successfully integrates multi-source data to map, measure and monitor distinct change processes across scales

VIRTUALIZATION OF FUELBEDS: BUILDING THE NEXT GENERATION OF FUELS DATA FOR MULTIPLE –SCALE FIRE MODELING AND ECOLOGICAL ANALYSIS

The primary goal of this research is to advance methods for deriving fine-grained, scalable, wildland fuels attributes in 3-dimensions using terrestrial and airborne laser scanning technology. It is fundamentally a remote sensing research endeavor applied to the problem of fuels characterization. Advancements in laser scanning are beginning to have significant impacts on a range of modeling frameworks in fire research, especially those utilizing 3-dimensional data and benefiting from efficient data scaling. The pairing of laser scanning and fire modeling is enabling advances in understanding how fuels variability modulates fire behavior and effects. This dissertation details the development of methods and techniques to characterize and quantify surface fuelbeds using both terrestrial and airborne laser scanning. The primary study site is Eglin Airforce Base, Florida, USA, which provides a range of fuel types and conditions in a fire-adapted landscape along with the multi-disciplinary expertise, logistical support, and prescribed fire necessary for detailed characterization of fire as a physical process. Chapter 1 provides a research overview and discusses the state of fuels science and the related needs for highly resolved fuels data in the southeastern United States. Chapter 2, describes the use of terrestrial laser scanning for sampling fuels at multiple scales and provides analysis of the spatial accuracy of fuelbed models in 3-D. Chapter 3 describes the development of a voxel-based occupied volume method for predicting fuel mass. Results are used to inform prediction of landscape-scale fuel load using airborne laser scanning metrics as well as to predict post-fire fuel consumption. Chapter 4 introduces a novel fuel simulation approach which produces spatially explicit, statistically-defensible estimates of fuel properties and demonstrates a pathway for resampling observed data. This method also can be directly compared to terrestrial laser scanning data to assess how energy interception of the laser pulse affects characterization of the fuelbed. Chapter 5 discusses the contribution of this work to fire science and describes ongoing and future research derived from this work. Chapters 2 and 4 have been published in International Journal of Wildland Fire and Canadian Journal of Remote Sensing, respectively, and Chapter 3 is in preparation for publication

Evaluation of low-cost Earth observations to scale-up national forest monitoring in Miombo Woodlands of Malawi

This study explored the extent that low-cost Earth Observations (EO) data could effectively be combined with in-situ tree-level measurements to support national estimates of Above Ground Biomass (AGB) and Carbon (C) in Malawi’s Miombo Woodlands. The specific objectives were to; (i) investigate the effectiveness of low-cost optical UAV orthomosaics in geo-locating individual trees and estimating AGB and C, (ii) scale-up the AGB estimates using the canopy height model derived from the UAV imagery, and crown diameter measurements; and (iii) compare results from (ii), ALOS-PALSAR-2, Sentinel1, ESA CCI Biomass Map datasets, and Sentinel 2 vis/NIR/SWIR band combination datasets in mapping biomass. Data were acquired in 2019 from 13 plots over Ntchisi Forest in 3-fold, vis-a-vis; (i) individual tree measurements from 0.1ha ground-based (gb) plots, (ii) 3-7cm pixel resolution optical airborne imagery from 50ha plots, and (iii) SAR backscatter and Vis/NIR/SWIR bands imagery. Results demonstrate a strong correlational relationship (R2 = 0.7, RMSE = 11tCha-1) between gb AGB and gb fractional cover percent (FC %), more importantly (R2 = 0.7) between gb AGB and UAV-based FC. Similarly, another set of high correlation (R2 = 0.9, RMSE = 7tCha-1; R2 = 0.8, RMSE = 8tCha-1; and R2 = 0.7) was observed between the gb AGB and EO-based AGB from; (i) ALOS-PALSAR-2, (ii) ESA-CCI-Biomass Map, and (iii) S1-C-band, respectively. Under the measurement conditions, these findings reveal that; (i) FC is more indicative of AGB and C pattern than CHM, (ii) the UAV can collect optical data of very high resolution (3-7cm resolution with ±13m horizontal geolocation error), and (iii) provides the cost-effective means of bridging the ground datasets to the wall-to-wall satellite EO data (£7 ha-1 compared to £30 ha-1, per person, provided by the gb system). The overall better performance of the SAR backscatter (R2 = 0.7 to 0.9) establishes the suitability of the SAR backscatter to infer the Miombo AGB and fractional cover with high accuracy. However, the following factors compromised the accuracy for both the SAR and optical measurements; leaf-off and seasonality (fire, aridness), topography (steep slopes of 18-74%), and sensing angle. Inversely, the weak to moderate correlation observed between the gb height and UAV FC % measurements (R2 = 0.4 to 0.7) are attributable to the underestimation systematic error that UAV height datasets are associated with. The visual lacunarity analysis on S2-Vis/NIR/SWIR composite band and SAR backscatter measurements demonstrated robust, consistent and homogenous spatial crown patterns exhibited particularly by the leaf-on tree canopies along riverine tree belts and cohorts. These results reveal the potential of vis/NIR/SWIR band combination in determining the effect of fire, rock outcrops and bare land/soil common in these woodlands. Coarsening the EO imagery to ≥50m pixel resolution compromised the accuracy of the estimations, hence <50m resolution is the ideal scale for these Miombo. Careful consideration of the aforementioned factors and incorporation of FC parameter in during estimation of AGB and C will go a long way in not only enhancing the accuracy of the measurements, but also in bolstering Malawi’s NFMS standards to yield carbon off-set payments under the global REDD+ mechanism

Remotely sensed and modelled pasture biomass, land condition and the potential to improve grazing-management decision tools across the Australian rangelands

This report assesses the potential for expanding on current capacity to monitor land condition using remotely sensed fractional cover products to improve biomass estimation, animal productivity, pasture growth models and grazing decision tools (e.g. safe carrying capacity) across the Australian rangelands. We focus on northern Australia and include relevant research and implementation from southern Australia where appropriate