COUPLING BIOPHYSICAL COMPLEXITY AND FOREST METABOLISM IN A FLOODPLAIN LANDSCAPE

Floodplains are biophysically complex systems that are considered among the most productive and biodiverse ecosystems on Earth. Until recently, quantitative assessment of the relationship between complexity and terrestrial production has been constrained by technological limitation. To address how floodplain biophysical complexity and ecosystem function are related, I employed remote sensing, GIS, and spatial analyses to quantify and couple metrics of complexity and terrestrial production, as well as explore the relationship among complexity, vegetation structural diversity, and terrestrial primary productivity. The study site is a 6.75-km by 1.75-km portion of the Bitterroot River floodplain near Carlton, MT upon which 551 sample plots were delimited via segmentation classification. Biophysical complexity, characterized by topographic heterogeneity, structural heterogeneity, and hydrologic connectivity was represented in each sample plot by mean standard deviation ground height, vegetative structural diversity index, mean flow length, mean flow accumulation, mean percent inundation, and gamma index metrics computed from Light Detection and Ranging (LiDAR) data, HEC-RAS inundation modeling, and ArcGIS Arc Hydro derived metrics. Potential primary production was represented by Normalized Difference Vegetation Index (NDVI) values generated from aerial 4-band multispectral imagery. Two questions were addressed in the analyses: 1) What is the causal relationship among floodplain physical complexity, vegetation structural diversity, and terrestrial productivity, and 2) How does floodplain biophysical complexity influence terrestrial primary production. Through these efforts, my goal was to explain how the dynamic nature of riverscapes translates to fundamental measures of ecological form and function. NDVI values ranged from -0.27 to 0.43, and were robustly related to biophysical complexity in which the explanatory variables together accounted for 58% of variation in NDVI (p \u3c 0.001). In investigating the relationship between biophysical complexity, vegetation structural diversity, and NDVI, biophysical complexity was positively correlated to NDVI (r2= 0.25, p \u3c 0.001), and structural diversity was positively related to NDVI (r2= 0.51, p \u3c 0.001). These results suggest a causal relationship and support the complexity diversity hypothesis, and the diversity- productivity hypothesis. Structural diversity and connectivity variables accounted for the most explanatory power in all analyses, and overall results indicate that areas of the floodplain with greater biophysical complexity exhibited greater productivity. Davis, Peter, M.S., Summer 2017 Systems Ecology Coupling Biophysical Complexity and Forest Metabolism in Floodplain Landscapes Chairperson: H. Maurice Valett Floodplains are biophysically complex systems that are considered among the most productive and biodiverse ecosystems on Earth. Until recently, quantitative assessment of the relationship between complexity and terrestrial production has been constrained by technological limitation. To address how floodplain biophysical complexity and ecosystem function are related, I employed remote sensing, GIS, and spatial analyses to quantify and couple metrics of complexity and terrestrial production, as well as explore the relationship among complexity, vegetation structural diversity, and terrestrial primary productivity. The study site is a 6.75-km by 1.75-km portion of the Bitterroot River floodplain near Carlton, MT upon which 551 sample plots were delimited via segmentation classification. Biophysical complexity, characterized by topographic heterogeneity, structural heterogeneity, and hydrologic connectivity was represented in each sample plot by mean standard deviation ground height, vegetative structural diversity index, mean flow length, mean flow accumulation, mean percent inundation, and gamma index metrics computed from Light Detection and Ranging (LiDAR) data, HEC-RAS inundation modeling, and ArcGIS Arc Hydro derived metrics. Potential primary production was represented by Normalized Difference Vegetation Index (NDVI) values generated from aerial 4-band multispectral imagery. Two questions were addressed in the analyses: 1) What is the causal relationship among floodplain physical complexity, vegetation structural diversity, and terrestrial productivity, and 2) How does floodplain biophysical complexity influence terrestrial primary production. Through these efforts, my goal was to explain how the dynamic nature of riverscapes translates to fundamental measures of ecological form and function. NDVI values ranged from -0.27 to 0.43, and were robustly related to biophysical complexity in which the explanatory variables together accounted for 58% of variation in NDVI (p \u3c 0.001). In investigating the relationship between biophysical complexity, vegetation structural diversity, and NDVI, biophysical complexity was positively correlated to NDVI (r2= 0.25, p \u3c 0.001), and structural diversity was positively related to NDVI (r2= 0.51, p \u3c 0.001). These results suggest a causal relationship and support the complexity diversity hypothesis, and the diversity- productivity hypothesis. Structural diversity and connectivity variables accounted for the most explanatory power in all analyses, and overall results indicate that areas of the floodplain with greater biophysical complexity exhibited greater productivity

Uncertainty in parameterizing floodplain forest friction for natural flood management, using remote sensing

One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM

Forage supply of West African rangelands : Towards a better understanding of ecosystem services by application of hyperspectral remote sensing

Grazing is the predominant type of land use in savanna regions all over the world. Although large savanna areas in Africa are still grazed by wild herbivores, the West African Sudanian savanna region mainly comprises rangeland ecosystems, providing the important ecosystem service of forage supply for domestic livestock. However, these dryland rangelands are threatened by global change, including a predicted in-crease in climatic aridity and variability as well as land degradation caused by overgrazing. In this context, the international research project WASCAL (West African Science Service Centre on Climate Change and Adapted Land Use) was initiated to investigate the effects of climatic change in this region and to develop effective adaptation and mitigation measures. This cumulative dissertation aims at providing a methodology for a regular knowledge-driven monitoring of forage resources in West Africa. Due to the vast and remote nature of Sudanian savannas, remote sensing technologies are required to achieve this goal. Hence, as a first step, it was necessary to test whether hyperspectral near-surface remote sensing offers the means to model and estimate the two most important aspects of forage supply, i.e. forage quantity (green biomass) and quality (metabolisable energy) (Chapter 2.1). Evidence was provided that partial least squares regression was able to generate robust and transferable forage models. In a second step, direct and indirect drivers of forage supply on the plot and site level were identified by using path modelling within the well-defined concept of social-ecological systems (Chapter 2.2). Results indicate that the provisioning ecosystem service of forage supply is mainly driven by land use, while climatic aridity exerts foremost indirect control by determining the way people use their environment. Building on these findings, upscaling of models was tested to generate maps of forage quality and quantity from satellite images (Chapter 2.3). Here, two different available data sources, i.e. multi- and hyperspectral satellites, were compared to serve the overall objective to install a regular forage monitoring system. In conclusion, preliminary forage maps could be created from both systems. An independent validation would be a research desiderate for future studies. Moreover, both systems feature certain shortcomings that might only be overcome by future satellite missions

Laser vision : lidar as a transformative tool to advance critical zone science

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hydrology and Earth System Sciences 19 (2015): 2881-2897, doi:10.5194/hess-19-2881-2015.Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demonstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.The workshop forming the impetus for this paper was funded by the National Science Foundation (EAR 1406031). Additional funding for the workshop and planning was provided to S. W. Lyon by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT grant no. 2013-5261). A. A. Harpold was supported by an NSF fellowship (EAR 1144894)

Ecohydrology: processes and implications for rangelands

This chapter is organized around the concept of ecohydrological processes that are explicitly tied to ecosystem services. Ecosystem services are benefits that people receive from ecosystems. We focus on (1) the regulating services of water distribution, water purification, and climate regulation; (2) the supporting services of water and nutrient cycling and soil protection and restoration; and (3) the provisioning services of water supply and biomass production. Regulating services are determined at the first critical juncture of the water cycle—on the soil surface, where water either infiltrates or becomes overland flow. Soil infiltrability is influenced by vegetation, grazing intensity, brush management, fire patterns, condition of biological soil crusts, and activity by fauna. At larger scales, water-regulating services are influenced by other factors, such as the nature and structure of riparian zones and the presence of shallow groundwater aquifers. Provisioning services are those goods or products that are directly produced from ecosystems, such as water, food, and fiber. Work over the last several decades has largely overturned the notion that water supply can be substantially increased by removal of shrubs. In riparian areas, surprisingly, removal of invasive, non-native woody plants appears to hold little potential for increasing water supply. Here, the primary factor appears to be that non-native plants use no more water than the native vegetation they displace. Clearly there is a close coupling between biota (both fauna and flora) and water on rangelands—which is why water-related ecosystem services are so strongly dependent on land management strategies.Fil: Wilcox, Bradford P.. Texas A&M University; Estados UnidosFil: Le Maitre, David. Council for Scientific and Industrial Research; SudáfricaFil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Wang, Lixin. Indiana University; Estados UnidosFil: Breshears, David D.. University of Arizona; Estados Unido

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Remote sensing of geomorphodiversity linked to biodiversity — part III: traits, processes and remote sensing characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed