From Pixels to Plants: Remote Sensing of California Invasive Plants

Invasive plants cause significant impacts to ecosystems, the economy, and human health. California has experienced significant plant invasions and is well suited to future invasion because of its Mediterranean climate and human disturbance. Eradication or control of invasive plant species requires a detailed understanding of their spatial distribution, which typically involves on the ground surveys that can be expensive or inconsistent. Remote sensing offers a potential alternative or supplement to in-person invasive plant mapping. This study performed a comparative analysis of 41 remote sensing studies that mapped the distribution of California invasive plants. I found that while high spectral resolution hyperspectral imagery was most often and successfully used to map California invasive plant species, recent studies suggest that employing low cost, color or color-infrared imagery are capable of overcoming lower spectral resolution with higher spatial or temporal resolution. Imagery obtained by UAVs are becoming increasingly more accessible for the use of mapping invasive plants at the site-scale. From this study, I examine two case studies that illustrate the use of remote sensing for large scale invasive plant management. One case study examines the use of remote sensing to monitor widespread infestations of salt cedar (Tamarix spp.) across the Western U.S.. A second case study examines the use of remote sensing to monitor invasive plants in a complex and regulatorily challenging landscape: The Sacramento-San Joaquin Delta. I recommend that land managers can incorporate remote sensing to monitor invasive plants by using low cost, color or color-infrared imagery obtained by drone or UAVs, developing partnerships with other relevant agencies, and collecting in-person data using methods that facilitate remote sensing analysis

Assessment and Development of Remotely Sensed Evapotranspiration Modeling Approaches

Remote sensing has been a promising approach to extracting distributed evapotranspiration (ET) information at varying spatial and temporal scales. Performances of several vegetation index (VI) based and remotely sensed surface energy balance (RSEB) models were evaluated to identify simple and accurate models and apply them to study ET variations from field to regional scales. A simple VI model using a single Landsat image to estimate annual ET was evaluated and successfully captured inter-annual riparian ET variations along a section of the Colorado River, U.S. The study showed the applicability of a simple and accurate approach for annual ET estimation with fewer data and resources. A modeling framework was developed to derive daily time series of ET maps using a RSEB model, satellite imagery, and ground-based weather data. The daily and annual ET maps obtained from the modeling framework successfully captured spatial and temporal ET variations across Oklahoma, U.S. The model also identified the regions that are more susceptible to droughts. Finally, five RSEB models were evaluated for their performance in estimating daily ET of winter wheat under variable grazing and tillage practices in central Oklahoma. The surface energy balance algorithm for land (SEBAL) had the best agreement whit eddy covariance estimates. The daily ET estimates from SEBAL captured the field-scale ET variations within grazing/tillage managements. All studies conducted based on VI and RSEB models over different land covers and spatial/temporal scales identified advantages and limitations of models and developed a framework to construct time series of ET maps, which has a wide range of applications

Spatial-temporal mapping of Parthenium (P. HysterophoruL) in the Mtubatuba municipality, KwaZulu-Natal, South Africa.

Master of Science in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Detecting and mapping the occurrence, spread, and abundance of Alien Invasive Plants (AIPs) have recently gained substantial attention, globally. Therefore, the present study aims to assess remote sensing application for mapping the spatial and temporal spread of Parthenium (P. HysterophoruL) in the Mtubatuba municipality of KwaZulu-Natal, South Africa. Parthenium is an aggressive herbaceous plant from the South and Central America that has colonized many regions of the world including Asia, Australia, and Africa. The adverse social, economic and ecological impacts of the plant have emphasized the need for a robust control programme to combat its spread. However, data for the management of the weed has been gathered by means of manual methods such as field surveys which are time and labour intensive. Alternatively, remote sensing techniques provides cost effective approach to large-scale mapping of AIPs. The first objective of the study provides an overview of advancements in satellite remote sensing for mapping AIPs spread and the associated challenges and opportunities. Satellite remote sensing techniques have been successful in detecting and mapping of AIPs, exploring their spatial and temporal distribution in rangeland ecosystems. Although they provide fine spatial information, the excessive image acquisition costs associated with the use of high spatial and hyperspectral datasets are a limitation to continuous and large-scale mapping of AIPs. The signing of the license agreement between the South African Space Agency (SANSA) and Airbus Defense and Space (ADS) has ensured a continued provision of SPOT data with improved spatial properties for South Africa. Similarly, the signing of the single licence government multi-user agreement between the South African government and SANSA has ensured free provision of SPOT data for public use in South Africa to support land change monitoring. The second objective was to determine the spatial and temporal distribution of Parthenium from 2006 to 2016 using SPOT series data in concert with Random Forest and Land Change Modeler (LCM). Findings have shown a steady decrease in Parthenium distribution over the 10-year period of the study because of the low annual rainfall experienced in the area over the recent past. Furthermore, disturbances in the soil opens vacant spaces which are susceptible to Parthenium invasion. This study has demonstrated the value of readily available multispectral SPOT series data in concert with robust and advanced non-parametric Random Forest algorithm in detecting trends and patterns in the spatial and temporal spread of AIPs

Advancements in satellite remote sensing for mapping and monitoring ofalien invasive plant species (AIPs)

Detecting and mapping the occurrence, spatial distribution and abundance of Alien Invasive Plants (AIPs) have recently gained substantial attention, globally. This work, therefore, provides an overview of advancements in satellite remote sensing for mapping and monitoring of AIPs and associated challenges and opportunities. Satellite remote sensing techniques have been successful in detecting and mapping the spatial and temporal distribution of AIPs in rangeland ecosystems. Also, the launch of high spatial resolution and hyperspectral remote sensing sensors marked a major breakthrough to precise characterization of earth surface feature as well as optimal resource monitoring. Although essential, the improvements in spatial and spectral properties of remote sensing sensors presented a number of challenges including the excessive acquisition and limited temporal resolution. Therefore, the use of high spatial and hyperspectral datasets is not a plausible alternative to continued and operational scale earth observation, especially in financially constrained countries. On the other hand, literature shows that image classification algorithms have been instrumental in compensating the poor spatial and spectral resolution of remote sensing sensors. Furthermore, the emergence of robust and advanced non-parametric image classification algorithms have been a major development in image classification algorithms

Assessing the potential replacement of laurel forest by a novel ecosystem in the steep terrain of an Oceanic Island

Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12\u201317% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images

Hydrological Characterization of a Riparian Vegetation Zone Using High Resolution Multi-Spectral Airborne Imagery

The Middle Rio Grande River (MRGR) is the main source of fresh water for the state of New Mexico. Located in an arid area with scarce local water resources, this has led to extensive diversions of river water to supply the high demand from municipalities and irrigated agricultural activities. The extensive water diversions over the last few decades have affected the composition of the native riparian vegetation by decreasing the area of cottonwood and coyote willow and increasing the spread of invasive species such as Tamarisk and Russian Olives, harmful to the river system, due to their high transpiration rates, which affect the river aquatic system. The need to study the river hydrological processes and their relation with its health is important to preserve the river ecosystem. To be able to do that a detailed vegetation map was produced using a Utah State University airborne remote sensing system for 286 km of river reach. Also a groundwater model was built in ArcGIS environment which has the ability to estimate soil water potential in the root zone and above the modeled water table. The Modified Penman- Monteith empirical equation was used in the ArcGIS environment to estimate riparian vegetation ET, taking advantage of the detailed vegetation map and spatial soil water potential layers. Vegetation water use per linear river reach was estimated to help decision makers to better manage and release the amount of water that keeps a sound river ecosystem and to support agricultural activities

Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.This study has been funded by the Spanish Ministry of Science and Innovation through the research projects HYDRA (No. CGL2006-02247/BOS) and HYDRA2 (CGL2009-09801/BOS), by the National Parks Authority (Organismo Autonomo de Parques Nacionales) of the Spanish Ministry of Environment to project OAPN 042/2007, and by funding from the European Union (EU) Horizon 2020 research and innovation program under grant agreement No. 641762 to ECOPOTENTIAL project. The Espacio Natural de Doñana provided permits for field work in protected areas with restricted access. We are grateful to the Instituto Nacional de Técnica Aeroespacial (INTA), Spain, for performing the airborne campaign and the geometric correction of the images. J.B. has to acknowledge a sabbatical stay at Pye Laboratory of the Commonwealth Scientific and Research Organization (CSIRO) Marine and Atmospheric Sciences, Australia, and at the Climate Change Cluster (C3) of the University of Technology Sydney, Australia, funded by the Spanish Ministry of Education, during data analysis and writing of this paper. This publication is a contribution from CEIMAR and also a contribution from CEICAMBIO. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI

The role of remote sensing in invasive alien plant species detection and the assessment of removal programs in two selected reserves in the eThekwini Municipality, KwaZulu-Natal Province.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Durban 2016.One of the major current concerns by conservationists is alien invasive plants due to their rapid spread and threat to biodiversity. The detection of Invasive Alien Plant Species (IAPs) can aid in monitoring and managing their invasion on ecosystems. In South Africa approximately 10 million hectares of land have been invaded. To combat this invasion, the Working for Water program was initiated in 1995 aimed at manually removing them. Multispectral imagery can facilitate identification, assess removal initiatives and improve efficiency of IAP removal. The aim of this study is to determine the most appropriate sensor to detect three IAPs (Acacia podalyriifolia, Chromolaena odorata and Litsea glutinosa) and assess clearing programs of these species in two protected areas (Paradise Valley and Roosfontein Nature Reserves) within the eThekwini municipality, in KwaZulu-Natal province, South Africa using remote sensing. The three satellite sensors examined in this study included Landsat 7 ETM+, SPOT 5 and WorldView-2. The study also assessed four image classifiers (Parallelepiped, Maximum Likelihood, Spectral Angle Mapper and Iterative Self Organising Data Analysis Technique) in the detection of the selected IAPs. These sensors and techniques were compared based on their level of accuracy at detecting selected IAPs. The results of the study showed that WorldView-2 imagery and the Maximum Likelihood classifier had the highest overall accuracy (66.67%) , resulting in the successful classification of two (Acacia podalyriifolia and Chromolaena odorata) out of the three target species. This is due to the high spatial resolution of WorldView-2 imagery. This combination was then used to asses clearing of the selected IAPs by examining species distribution and density before and after clearing. Here the overall accuracies for the Paradise Valley and Roosfontein Nature Reserves were successful with accuracies above 85%. The density and distribution of all three IAPs decreased substantially in both sites except for the L. glutinosa species located in the Paradise Valley Nature Reserve which showed no significant decrease. These results show that geospatial data (especially remote sensing data) can be successfully used in both the detection of IAPs and the assessment of their removal

Application of advanced techniques for the remote detection, modelling and spatial analysis of mesquite (prosopis spp.) invasion in Western Australia

Invasive plants pose serious threats to economic, social and environmental interests throughout the world. Developing strategies for their management requires a range of information that is often impractical to collect from ground based surveys. In other cases, such as retrospective analyses of historical invasion rates and patterns, data is rarely, if ever, available from such surveys. Instead, historical archives of remotely sensed imagery provides one of the only existing records, and are used in this research to determine invasion rates and reconstruct invasion patterns of a ca 70 year old exotic mesquite population (Leguminoseae: Prosopis spp.) in the Pilbara Region of Western Australia, thereby helping to identify ways to reduce spread and infill. A model was then developed using this, and other, information to predict which parts of the Pilbara are most a risk. This information can assist in identifying areas requiring the most vigilant intervention and pre-emptive measures. Precise information of the location and areal extent of an invasive species is also crucial for land managers and policy makers for crafting management strategies aimed at control, confinement or eradication of some or all of the population. Therefore, the third component of this research was to develop and test high spectral and spatial resolution airborne imagery as a potential monitoring tool for tracking changes at various intervals and quantifying the effectiveness of management strategies adopted. To this end, high spatial resolution digital multispectral imagery (4 channels, 1 m spatial resolution) and hyperspectral imagery (126 channels, 3 m spatial resolution) was acquired and compared for its potential for distinguishing mesquite from coexisting species and land covers.These three modules of research are summarised hereafter. To examine the rates and patterns of mesquite invasion through space and time, canopies were extracted from a temporal series of panchromatic aerial photography over an area of 450 ha using unsupervised classification. Non-mesquite trees and shrubs were not discernible from mesquite using this imagery (or technique) and so were masked out using an image acquired prior to invasion. The accuracy of the mesquite extractions were corroborated in the field and found to be high (R2 = 0.98, P36 m2 (66-94%) with both approaches and image types. However, both approaches used on the hyperspectral imagery were more reliable at capturing patches >36 m2 than the DMSI using either approach. The lowest omission and commission rates were obtained using pairwise separation on the hyperspectral imagery, which was significantly more accurate than DMSI using an overall separation approach (Z=2.78, P36 m2. However, hyperspectral imagery processed using pairwise separation appears to be superior, even though not statistically different to hyperspectral imagery processed using overall separation or DMSI processed using pairwise separation at the 95% confidence level. Mapping smaller patches may require the use of very high spatial resolution imagery, such as that achievable from unmanned airborne vehicles, coupled with a hyperspectral instrument. Alternatively, management may continue to rely on visual airborne surveys flown at low altitude and speed, which have proven to be capable at mapping small and isolated mesquite shrubs in the study area used in this research

Distribution and economic analysis of Prosopis juliflora in Ethiopia

Includes bibliographical references.2015 Fall.Invasive species are one of the drivers of biological and socio-economic changes around the world. Over the past 30-40 years, the non-native Prosopis juliflora plant has emerged as a major invader of the arid and semi-arid regions of Ethiopia. Information on its distribution, impact, use and management is highly needed to contain and prevent the spread of this highly invasive plant. In the first study, I used a correlative modeling framework to track and map the current and potential distribution of P. juliflora in Afar, north-eastern Ethiopia. Specifically, I used time-series of Moderate Resolution Imaging Specrtoradiometer (MODIS) satellite imagery, 143 species-occurrence records and the Maxent modeling technique to map its current distribution. I then used topo-climatic predictors, species-occurrence records and the Maxent software to map its forecasted distribution. I found that the current extent of P. juliflora invasion in the Afar region is approximately 3,605 Km2, while its predicted distribution is approximately 5,024 Km2. My findings demonstrates that MODIS vegetation indices and species-occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of its potential habitat in Ethiopia. In the second study, I used a participatory research framework to map P. juliflora over a fine geographic scale, and to identify the major resource related problems in the region. I learned about the introduction history, spread, impacts, uses and traditional management practices of P. juliflora in Afar by interviewing 108 pastoralists and agro-pastoralists. Additionally, I detected the land-cover categories most affected by P. juliflora invasion by superimposing community produced maps on ancillary land-cover layers, and performing overlay analysis. Prosopis juliflora has highly invaded grasslands and open areas in Afar. The species displaces useful native grass and forage species, which are important for sustaining the region's wildlife and livestock resources. In addition to threats from invasive species, Afar people face conflicts from neighboring Issa ethnic groups, and land-grabs from the central government and foreign investors. The findings demonstrates that participatory mapping methods are suitable for mapping species distribution, detecting land-cover changes, and managing invasive plants. High invasive species control costs have swayed most developing countries to adopt cost effective P. juliflora eradication and utilization practices. However, the effectiveness and economic viability of these new approaches have not been thoroughly tested. In the third study, I used an economic analysis framework to assess the economic feasibility of selected P. juliflora eradication and utilization methods that are practiced in southern Afar. The dominant P. juliflora eradication option was to convert infested lands into irrigated farms, while the preferred utilization options were to make animal fodder from P. juliflora seed pods, and to produce charcoal from P. juliflora wood. I interviewed 19 enterprise owners (i.e., farmers, flour producers and charcoal makers) and collected primary data on prices, yields, costs and revenues. I assessed the economic feasibility of the selected methods by performing enterprise, profitability, sensitivity and risk analyses over 10 years and an interest rate of 10% per year. Converting P. juliflora infested lands into irrigated agriculture is a profitable and risky P. juliflora eradication approach. Charcoal making is a moderately profitable and less risky utilization approach, while flour production is a risky and an un-profitable utilization approach. Introducing new changes in the production and management steps of flour production may be needed to make flour enterprises profitable. My overall economic analysis suggests that control through utilization may be one of the effective and economically viable P. juliflora management strategies currently accessible to Ethiopia. I generated reliable information on the distribution and impacts of P. juliflora in Afar by employing a wide variety of scientific approaches. My results can guide local level P. juliflora utilization and control efforts in Afar, while my methodologies can be replicated for managing invasive plants in other developing countries