Quantifying Environmental Limiting Factors on Tree Cover Using Geospatial Data

Environmental limiting factors (ELFs) are the thresholds that determine the maximum or minimum biological response for a given suite of environmental conditions. We asked the following questions: 1) Can we detect ELFs on percent tree cover across the eastern slopes of the Lake Tahoe Basin, NV? 2) How are the ELFs distributed spatially? 3) To what extent are unmeasured environmental factors limiting tree cover? ELFs are difficult to quantify as they require significant sample sizes. We addressed this by using geospatial data over a relatively large spatial extent, where the wall-to-wall sampling ensures the inclusion of rare data points which define the minimum or maximum response to environmental factors. We tested mean temperature, minimum temperature, potential evapotranspiration (PET) and PET minus precipitation (PET-P) as potential limiting factors on percent tree cover. We found that the study area showed system-wide limitations on tree cover, and each of the factors showed evidence of being limiting on tree cover. However, only 1.2% of the total area appeared to be limited by the four (4) environmental factors, suggesting other unmeasured factors are limiting much of the tree cover in the study area. Where sites were near their theoretical maximum, non-forest sites (tree cover \u3c 25%) were primarily limited by cold mean temperatures, open-canopy forest sites (tree cover between 25% and 60%) were primarily limited by evaporative demand, and closed-canopy forests were not limited by any particular environmental factor. The detection of ELFs is necessary in order to fully understand the width of limitations that species experience within their geographic range

Analyzing the phenologic dynamics of kudzu (Pueraria montana) infestations using remote sensing and the normalized difference vegetation index.

Non-native invasive species are one of the major threats to worldwide ecosystems. Kudzu (Pueraria montana) is a fast-growing vine native to Asia that has invaded regions in the United States making management of this species an important issue. Estimated normalized difference vegetation index (NDVI) values for the years 2000 to 2015 were calculated using data collected by Landsat and MODIS platforms for three infestation sites in Kentucky. The STARFM image-fusing algorithm was used to combine Landsat- and MODIS-derived NDVI into time series with a 30 m spatial resolution and 16 day temporal resolution. The fused time series was decomposed using the Breaks for Additive Season and Trend (BFAST) algorithm. Results showed that fused NDVI could be estimated for the three sites but could not detect changes over time. Combining this method with field data collection and other types of analyses may be useful for kudzu monitoring and management

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Multi-scale assessment of land changes in Ethiopia : understanding the impact of human activities on ecosystem services

Remote sensing provides land-cover information on a variety of temporal and spatial scales. The increasing availability of remote sensing data is now a major factor in land-change analysis and in understanding its impact on ecosystem services and biodiversity. This wider accessibility is also leading to improvements in the methods used to integrate these data into land-cover modelling and change analysis. Despite these developments in current technology and data availability however, there are still questions to be addressed regarding the dynamics of land cover and its impact, particularly in areas such as Ethiopia where the human population is expanding and there is a need for improvement in the management of natural resources. Multi-scale approaches (from the national to the local) were used in this thesis to assess change in land cover and ecosystem services in Ethiopia, specifically in terms of provisioning (the production of food, i.e. cash crops) and regulating (climate control for vegetation cover). These assessments were based on multi-scale remote sensing (very high spatial resolution remote aerial sensing, high-resolution SPOT 5 satellite imaging and products of medium-resolution satellite remote sensing) and climate data (e.g., precipitation, temperature). The main focus in this thesis is on mapping and modelling the spatial distribution of vegetation. This includes: (i) forest mapping (indigenous and exotic forests), (ii) modelling the probabilistic presence of understory coffee, (iii) Coffea arabica species distribution modelling and mapping and (iv) simulating pre-agricultural-expansion vegetation cover in Ethiopia. The results of the applied predictive modelling were robust in terms of: (i) identifying and mapping past vegetation cover and (ii) mapping understory shrubs such as coffee plants that grow as understory. I present a reconstruction of earlier vegetation cover that mainly comprised broadleaved evergreen and deciduous forest but was replaced in the course of agricultural expansion. Given the spatial scale of the latter, the environmental modelling was complemented with high spatial resolution satellite (2.5m) and aerial images (0.5m). The results of the Object Based Image Analysis show that indigenous forests were separated from exotic forests. Current and future suitable locations that are environmentally favourable for the growth of understory coffee were identified and mapped in the coffee-growing areas of Ethiopia. In conclusion, the information presented in this thesis, based on the multi-scale assessment of land changes, should lead to the better-informed management of natural resources and conservation, and the restoration of major areas affected by human population growth.Kaukokartoituksen avulla kerätään informaatiota maanpeitteestä ja maankäytöstä eri temporaalisilla ja spatiaalisilla resoluutiolla. Kaukokartoituksella on tärkeä rooli analysoitaessa maankäytön muutosta ja sen vaikutusta ekosysteemipalveluihin ja luonnon monimuotoisuuteen. Huolimatta siitä että kaukokartoitusaineistojen saatavuus on parantunut ja menetelmät kehittyneet, maailmassa on kuitenkin vielä alueita joiden maanpeitteen muutoksen dynamiikkaa ei vielä hyvin tunneta. Tässä väitöskirjatutkimuksessa tutkittiin Etiopian maankäytön muutosta ja sen vaikutusta ekosysteemipalveluihin ja luonnon monimuotoisuuteen usealla eri spatiaalisilla mittakaavoilla (paikallistasolta alueelliselle tasolle). Pääpaino tässä työssä oli kartoittaa ja mallintaa kasvillisuuden alueellista levinneisyyttä hyödyntäen eri erotuskyvyn omaavia kaukokartoitusaineistoja ja erilaisia kaukokartoitusmenetelmiä. Työssä kehitettiin kaukokartoitusperusteisia luokitusmenetelmiä, joilla pystyttiin: (i) erottelemaan tutkimusalueella sijaitsevat alkuperäismetsät istutusmetsistä ja (ii) saadun tiedon avulla edelleen mallintaa alkuperäiskahvin Coffea arabican spatiaalista levinneisyyttä, sillä alkuperäiskahvi kasvaa alkuperäismetsien pensaskerroksessa; (iii) muodostaa bioklimaattisia ja geospatiaalisia muuttujia sisältävän todennäköisyysmallin alkuperäiskahvin levinneisyyden arvioimiseksi muuttuvissa ilmasto-olosuhteissa; sekä (iv) simuloida Etiopian kasvillisuuspeitettä ajalta ennen maanviljelyksen voimakasta leviämistä. Alueellisella tasolla tulokset osoittavat, että Etiopian pinta-alasta ikivihreät metsät ja lehtimetsät ovat peittäneet huomattavasti laajemman alueen kasvillisuuspeitteestä ennen maanviljelyksen voimakasta leviämistä. Paikallistason tutkimuksessa objektipohjaisella kaukokartoitusaineiston luokitusmenetelmällä pystyttiin erottelemaan alkuperäismetsät istutusmetsistä ja edelleen mallintaa alkuperäiskahvin Coffea arabican spatiaalista levinneisyyttä hyödyntäen alkuperäismetsien erottelua, bioklimaattisia ympäristömuuttujia sekä spatiaalis-tilastollisia todennäköisyysmalleja. Yhteenvetona voidaan todeta että tämä väitöskirjatutkimus antaa näkökulmia monimittakaavaisen maankäytönmuutoksen vaikutusten ymmärtämiseksi ekosysteemipalveluihin ja luonnon monimuotoisuuteen. Tämän tutkimuksen tuloksia voidaan hyödyntää esimerkiksi voimakaan väestönkasvun alueilla, joissa tarvitaan tehokkaita menetelmiä luonnonvarojen hallintaan ja ympäristön suojeluun

Challenges and opportunities of using ecological and remote sensing variables for crop pest and disease mapping

Crop pest and diseases are responsible for major economic losses in the agricultural systems in Africa resulting in food insecurity. Potential yield losses for major crops across Africa are mainly caused by pests and diseases. Total losses have been estimated at 70% with approximately 30% caused by inefficient crop protection practices. With newly emerging crop pests and disease, monitoring plant health and detecting pathogens early is essential to reduce disease spread and to facilitate effective management practices. While many pest and diseases can be acquired from another host or via the environment, the majority are transmitted by biological vectors. Thus, vector ecology can serve an indirect explanation of disease cycles, outbreaks, and prevalence. Hence, better understanding of the vector niche and the dependence of pest and disease processes on their specific spatial and ecological contexts is therefore required for better management and control. While research in disease ecology has revealed important life history of hosts with the surrounding environment, other aspects need to be explored to better understand vector transmission and control strategies. For instance, choosing appropriate farming practices have proved to be an alternative to the use of synthetic pesticides. For instance, intercropping can serve as a buffer against the spread of plant pests and pathogens by attracting pests away from their host plant and also increasing the distance between plants of the same species, making it more exigent for the pest to target the main crop. Many studies have explored the potential applications of geospatial technology in disease ecology. However, pest and disease mapping in crops is rather crudely done thus far, using Spatial Distribution Models (SDM) on a regional scale. Previous research has explored climatic data to model habitat suitability and the distribution of different crop pests and diseases. However, there are limitation to using climate data since it ignores the dispersal and competition from other factors which determines the distribution of vectors transmitting the disease, thus resulting in model over prediction. For instance, vegetation patterns and heterogeneity at the landscape level has been identified to play a key role in influencing the vector-host-pathogen transmission, including vector distribution, abundance and diversity at large. Such variables can be extracted from remote sensing dataset with high accuracy over a large extent. The use of remotely sensed variables in modeling crop pest and disease has proved to increase the accuracy and precision of the models by reducing over fitting as compared to when only climatic data which are interpolated over large areas thus disregarding landscape heterogeneity.When used, remotely sensed predictors may capture subtle variances in the vegetation characteristic or in the phenology linked with the niche of the vector transmitting the disease which cannot be explained by climatic variables. Subsequently, the full potential of remote sensing applications to detect changes in habitat condition of species remains uncharted. This study aims at exploring the potential behind developing a framework which integrates both ecological and remotely sensed dataset with a robust mapping/modelling approach with aim of developing an integrated pest management approach for pest and disease affecting both annual and perrennial crops and whom currently there is no cure or existing germplasm to control further spread across sub Saharan Africa.Herausforderungen und Möglichkeiten der Verwendung von ökologischen und Fernerkundungsvariablen für die Schädlings- und Krankheitskartierung Pflanzenschädlinge und Krankheiten in der Landwirtschaft sind für große wirtschaftliche Verluste in Afrika verantwortlich, die zu Ernährungsunsicherheit führen. Die Verluste werden auf 70% geschätzt, wobei etwa 30% auf ineffiziente Pflanzenschutzpraktiken zurückzuführen sind. Bei neu auftretenden Pflanzenschädlingen und Krankheiten ist die Überwachung des Pflanzenzustands und die frühzeitige Erkennung von Krankheitserregern unerlässlich, um die Ausbreitung von Krankheiten zu reduzieren und effektive Managementpraktiken zu erleichtern. Während viele Schädlinge und Krankheiten von einem anderen Wirt oder über die Umwelt erworben werden können, wird die Mehrheit durch biologische Vektoren übertragen. Daraus folgt, dass die Vektorökologie als indirekte Erklärung von Krankheitszyklen, Ausbrüchen und Prävalenz untersucht werden sollte. Um effektive Vektorkontrollmaßnahmen zu entwickeln ist ein besseres Verständnis der ökologischen Vektor-Nischen und der Abhängigkeit von Schädlings- und Krankheits-Prozessen von ihrem spezifischen räumlichen und ökologischen Kontext wichtig. Während die Forschung in der Krankheitsökologie wichtige Lebenszyklen von Wirten mit der Umgebung schon gut aufgezeigt hat, müssen weitere Aspekte noch besser untersucht werden, um Vektorübertragungs- und Kontroll-Strategien zu entwickeln. So hat sich beispielsweise die Wahl geeigneter Anbaumethoden als Alternative zum Einsatz synthetischer Pestizide erwiesen. In einigen Fällen wurde der Zwischenfruchtanbau als ‚Puffer' gegen die Ausbreitung von Pflanzenschädlingen und Krankheitserregern vorgeschlagen. Bei diesem Anbausystem werden Schädlinge von ihrer Wirtspflanze abgezogen und auch der Abstand zwischen Pflanzen derselben Art vergrößert (was eine Übertragung erschwert). Viele Studien haben bereits die Einsatzmöglichkeiten von Geodaten in der Krankheitsökologie untersucht. Die Kartierung von Schädlingen und Krankheiten in Nutzpflanzen ist jedoch bisher eher großskalig erfolgt, unter der Zunahme von sogenannten ‚Spatial Distribution Models (SDM)' auf regionaler Ebene. Etliche Studien haben diesbezüglich klimatische Daten verwendet, um die Eignung und Verteilung verschiedener Pflanzenschädlinge und Krankheiten zu modellieren. Es gibt jedoch Einschränkungen bei der Verwendung von Klimadaten, da dabei andere landschaftsbezogene Verbreitungs-Faktoren ignoriert werden, die die Verteilung der Vektoren und Krankheitserreger bestimmen, was zu einer Modell-Überprognose führt. Vegetationsmuster und Heterogenität auf Landschaftsebene beeinflussen maßgeblich die Diversität und Verteilung eines Vektors und spielen somit eine wichtige Rolle bei der Vektor-Wirt-Pathogen-Übertragung. Bei der Verwendung von Fernerkundungsdaten können subtile Abweichungen in der Vegetationscharakteristik oder in der Phänologie, die mit der Nische des Vektors verbunden sind, besser erfasst werden. Es besteht noch Forschungs-Bedarf hinsichtlich der Rolle von Fernerkundungsdaten bei der Verbesserung von Artenmodellen, die zum Ziel haben den Lebensraum von Krankheitsvektoren besser zu erfassen. Ziel dieser Studie ist es, das Potenzial für die Entwicklung eines Rahmens zu untersuchen, der sowohl ökologische als auch aus der Ferne erfasste Daten mit einem robusten Mapping- / Modellierungsansatz kombiniert, um einen integrierten Ansatz zur Schädlingsbekämpfung für Schädlinge und Krankheiten zu entwickeln, der sowohl einjährige als auch mehrjährige Kulturpflanzen betrifft Keine Heilung oder vorhandenes Keimplasma zur weiteren Verbreitung in Afrika südlich der Sahara

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Investigation of climate variability and climate change impacts on corn yield in the Eastern Corn Belt, USA

The increasing demand for both food and biofuels requires more corn production at global scale. However, current corn yield is not able to meet bio-ethanol demand without jeopardizing food security or intensifying and expanding corn cultivation. An alternative solution is to utilize cellulose and hemi-cellulose from perennial grasses to fulfill the increasing demand for biofuel energy. A watershed level scenario analysis is often applied to figure out a sustainable way to strike the balance between food and fuel demands, and maintain environment integrity. However, a solid modeling application requires a clear understanding of crop responses under various climate stresses. This is especially important for evaluating future climate impacts. Therefore, correct representation of corn growth and yield projection under various climate conditions (limited or oversupplied water) is essential for quantifying the relative benefits of alternative biofuel crops. The main objective of this study is to improve the evaluation of climate variability and climate change effects on corn growth based on plant-water interaction in the Midwestern US via a modeling approach. Traditional crop modeling methods with the Soil and Water Assessment Tool (SWAT) are improved from many points, including introducing stress parameters under limited or oversupplied water conditions, improving seasonal crop growth simulation from imagery-based LAI information, and integrating CO2 effects on crop growth and crop-water relations. The SWAT model’s ability to represent crop responses under various climate conditions are evaluated at both plot scale, where observed soil moisture data is available and watershed scale, where direct soil moisture evaluation is not feasible. My results indicate that soil moisture evaluation is important in constraining crop water availability and thus better simulates crop responses to climate variability. Over a long term period, drought stress (limited moisture) explains the majority of yield reduction across all return periods at regional scale. Aeration stress (oversupplied water) results in higher yield decline over smaller spatial areas. Future climate change introduces more variability in drought and aeration stress, resulting in yield reduction, which cannot be compensated by positive effects brought by CO2 enhancement on crop growth. Information conveyed from this study can also provide valuable suggestions to local stakeholders for developing better watershed management plans. It helps to accurately identify climate sensitive cropland inside a watershed, which could be potential places for more climate resilient plants, like biofuel crops. This is a sustainable strategy to maintain both food/fuel provision, and mitigate the negative impact of future climate change on cash crops

Using eddy covariance, remote sensing, and in situ observations to improve models of springtime phenology in temperate deciduous forests

Phenological events in temperate forests, such as bud burst and senescence, exert strong control over seasonal fluxes of water, energy and carbon. The timing of these transitions is influenced primarily by air temperature and photoperiod, although the exact nature and magnitude of these controls is poorly understood. In this dissertation, I use in situ and remotely sensed observations of phenology in combination with surface meteorological data and measurements of biosphere-atmosphere carbon exchanges to improve understanding and develop models of canopy phenology in temperate forest ecosystems. In the first element of this research I use surface air temperatures and eddy covariance measurements of carbon dioxide fluxes to evaluate and refine widely used approaches for predicting the onset of photosynthesis in spring that account for geographic variation in thermal and photoperiod constraints on phenology. Results from this analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than existing models. A key challenge in developing and testing these models, however, is lack of adequate data sets that characterize phenology over large areas at multi-decadal time scales. To address this need, I develop a new method for estimating long-term average and interannual dynamics in the phenology of temperate forests using time series of Landsat TM/ETM+ images. Results show that estimated spring and autumn transition dates agree closely with in-situ measurements and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. In the final element of this dissertation, I use meteorological data, species composition maps, satellite remote sensing, and ground observations to develop models of springtime leaf onset in temperate deciduous forests that account for geographic differences in how forest communities respond to springtime climate forcing. Results demonstrate important differences in cumulative heating requirements and photoperiod cues among forest types and that regional differences in species composition explain substantial geographic variation in springtime phenology of temperate forests. Together, the results from this dissertation provide an improved basis for observing and modeling springtime phenology in temperate forests