Field Spectroscopy in the VNIR-SWIR region to discriminate between Mediterranean native plants and exotic-invasive shrubs based on leaf tannin content

The invasive shrub, Acacia longifolia, native to southeastern Australia, has a negative impact on vegetation and ecosystem functioning in Portuguese dune ecosystems. In order to spectrally discriminate A. longifolia from other non-native and native species, we developed a classification model based on leaf reflectance spectra (350–2500 nm) and condensed leaf tannin content. High variation of leaf tannin content is common for Mediterranean shrub and tree species, in particular between N-fixing and non-N-fixing species, as well as within the genus, Acacia. However, variation in leaf tannin content has not been studied in coastal dune ecosystems in southwest Portugal. We hypothesized that condensed tannin concentration varies significantly across species, further allowing for distinguishing invasive, nitrogen-fixing A. longifolia from other vegetation based on leaf spectral reflectance data. Spectral field measurements were carried out using an ASD FieldSpec FR spectroradiometer attached to an ASD leaf clip in order to collect 750 in situ leaf reflectance spectra of seven frequent plant species at three study sites in southwest Portugal. We applied partial least squares (PLS) regression to predict the obtained leaf reflectance spectra of A. longifolia individuals to their corresponding tannin concentration. A. longifolia had the lowest tannin concentration of all investigated species. Four wavelength regions (675–710 nm, 1060–1170 nm, 1360–1450 nm and 1630–1740 nm) were identified as being highly correlated with tannin concentration. A spectra-based classification model of the different plant species was calculated using a principal component analysis-linear discriminant analysis (PCA-LDA). The best prediction of A. longifolia was achieved by using wavelength regions between 1360–1450 nm and 1630–1740 nm, resulting in a user’s accuracy of 98.9%. In comparison, selecting the entire wavelength range, the best user accuracy only reached 86.5% for A. longifolia individuals

Spatial Analysis of Land Cover Determinants of Malaria Incidence in the Ashanti Region, Ghana

Malaria belongs to the infectious diseases with the highest morbidity and mortality worldwide. As a vector-borne disease malaria distribution is strongly influenced by environmental factors. The aim of this study was to investigate the association between malaria risk and different land cover classes by using high-resolution multispectral Ikonos images and Poisson regression analyses. The association of malaria incidence with land cover around 12 villages in the Ashanti Region, Ghana, was assessed in 1,988 children <15 years of age. The median malaria incidence was 85.7 per 1,000 inhabitants and year (range 28.4–272.7). Swampy areas and banana/plantain production in the proximity of villages were strong predictors of a high malaria incidence. An increase of 10% of swampy area coverage in the 2 km radius around a village led to a 43% higher incidence (relative risk [RR] = 1.43, p<0.001). Each 10% increase of area with banana/plantain production around a village tripled the risk for malaria (RR = 3.25, p<0.001). An increase in forested area of 10% was associated with a 47% decrease of malaria incidence (RR = 0.53, p = 0.029)

Multispectral, aerial disease detection for myrtle rust (Austropuccinia psidii) on a lemon myrtle plantation

Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussed herein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models

Linking Land Surface Phenology and Vegetation-Plot Databases to Model Terrestrial Plant α-Diversity of the Okavango Basin

In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology (LSP) metrics to predict plant α-diversity on a regional scale. We gathered data on plant α-diversity, measured as species density, from 999 vegetation plots sized 20 m × 50 m covering all major vegetation units of the Okavango basin in the countries of Angola, Namibia and Botswana. As predictor variables, we used MODIS LSP metrics averaged over 12 years (250-m spatial resolution) and three topographic attributes calculated from the SRTM digital elevation model. Furthermore, we tested whether additional climatic data could improve predictions. We tested three predictor subsets: (1) remote sensing variables; (2) climatic variables; and (3) all variables combined. We used two statistical modeling approaches, random forests and boosted regression trees, to predict vascular plant α-diversity. The resulting maps showed that the Miombo woodlands of the Angolan Central Plateau featured the highest diversity, and the lowest values were predicted for the thornbush savanna in the Okavango Delta area. Models built on the entire dataset exhibited the best performance followed by climate-only models and remote sensing-only models. However, models including climate data showed artifacts. In spite of lower model performance, models based only on LSP metrics produced the most realistic maps. Furthermore, they revealed local differences in plant diversity of the landscape mosaic that were blurred by homogenous belts as predicted by climate-based models. This study pinpoints the high potential of LSP metrics used in conjunction with biodiversity data derived from vegetation-plot databases to produce spatial information on a regional scale that is urgently needed for basic natural resource management applications

Developing a spectral disease index for myrtle rust (Austropuccinia psidii)

This is the code required to reproduce the article Heim, RHJ; Wright, IJ; Allen, AP; Geedicke, I and Oldeland, J 2019 Developing a spectral disease index for myrtle rust (Austropuccinia psidii), Plant Pathology, XX(xx), pp. XXX. DOI: 10.1111/ppa.1299

Developing a spectral disease index for myrtle rust ( Austropuccinia psidii

Since 2010 Australian ecosystems and managed landscapes have been severely threatened by the invasive fungal pathogen Austropuccinia psidii. Detecting and monitoring disease outbreaks is currently only possible by human assessors, which is slow and labour intensive. Over the last 25 years, spectral vegetation indices (SVIs) have been designed to assess variation in biochemical or biophysical traits of vegetation. However, diagnosis of individual diseases based on classical SVIs is currently not possible because they lack disease specificity. Here, a novel spectral disease index (SDI), the lemon myrtle–myrtle rust index (LMMR), has been developed. The index was designed from hyperspectral leaf-clip data collected at a lemon myrtle plantation in New South Wales, Australia. A total of 236 fungicide-treated (disease free) and 228 untreated (diseased) lemon myrtle leaves were sampled and a random forest classifier was used to show that the LMMR discriminates those classes with an overall accuracy of 90%. Compared to three classical SVIs (PRI, MCARI, NBNDVI), commonly applied for stress detection, the LMMR clearly improved classification accuracies (58%, 67%, 60%, respectively). If the LMMR can be validated on independent datasets from similar and different host species, it could enable land managers to reduce disease impact by earlier control. There might also be potential to collect useful data for epidemiology models. Calculating the LMMR based on hyperspectral data collected from aerial platforms (e.g. drones) would allow for rapid and high-capacity screening for disease outbreaks

Developing a spectral disease index for myrtle rust (Austropuccinia psidii)

Since 2010 Australian ecosystems and managed landscapes have been severely threatened by the invasive fungal pathogen Austropuccinia psidii. Detecting and monitoring disease outbreaks is currently only possible by human assessors, which is slow and labour intensive. Over the last 25 years, spectral vegetation indices (SVIs) have been designed to assess variation in biochemical or biophysical traits of vegetation. However, diagnosis of individual diseases based on classical SVIs is currently not possible because they lack disease specificity. Here, a novel spectral disease index (SDI), the lemon myrtle–myrtle rust index (LMMR), has been developed. The index was designed from hyperspectral leaf-clip data collected at a lemon myrtle plantation in New South Wales, Australia. A total of 236 fungicide-treated (disease free) and 228 untreated (diseased) lemon myrtle leaves were sampled and a random forest classifier was used to show that the LMMR discriminates those classes with an overall accuracy of 90%. Compared to three classical SVIs (PRI, MCARI, NBNDVI), commonly applied for stress detection, the LMMR clearly improved classification accuracies (58%, 67%, 60%, respectively). If the LMMR can be validated on independent datasets from similar and different host species, it could enable land managers to reduce disease impact by earlier control. There might also be potential to collect useful data for epidemiology models. Calculating the LMMR based on hyperspectral data collected from aerial platforms (e.g. drones) would allow for rapid and high-capacity screening for disease outbreaks

Die mitteleuropäischen Datenbanken im Global Index of Vegetation-Plot Databases (GIVD)

Der Global Index of Vegetation-Plot Databases (GIVD) ist eine Metadatenbank von Vegetations - datenbanken weltweit, die im Jahr 2010 von einem internationalen Leitungsgremium ins Leben gerufen wurde und auf einem Server in Greifswald beheimatet ist. Ziel von GIVD ist es, einen besseren Überblick über die zunehmende Zahl von Vegetationsdatenbanken zu geben und ihren Inhalt für übergreifende vegetationsökologische Analysen zu erschließen. Im vorliegenden Beitrag analysieren wir, welche Daten aus Mitteleuropa (incl. Benelux-Länder) in GIVD derzeit registriert sind. Am 20. März 2011 stammten 1,35 Millionen der insgesamt registrierten 2,45 Millionen Vegetationsaufnahmen aus den 12 betrachteten Ländern. Mit über 600.000 digital verfügbaren Vegetationsaufnahmen entsprechend einer Dichte von 18 km–2 sind die Niederlande weltweit führend.The Global Index of Vegetation-Plot Databases (GIVD) is a metadatabase of vegetation databases worldwide that was initiated by an international Steering Committee in 2010 and that is hosted on a server in Greifswald. GIVD aims at providing a better overview on the growing number of vegetationplot databases and increasing their accessibility for overarching analyses. In this article, we analyse which data from central Europe (including the Benelux countries) are available in GIVD. On 20 March 2011, 1.35 million of the total 2.45 million registered relevés originated from one of the covered twelve countries. With more than 600,000 digitally available relevés, corresponding to a density of 18 km–2, the Netherlands are globally leading in this respect

Table 2 Overview of the established BIOTA Observatories in Africa & Appendix

The international, interdisciplinary biodiversity research project BIOTA AFRICA initiated a standardized biodiversity monitoring network along climatic gradients across the African continent. Due to an identified lack of adequate monitoring designs, BIOTA AFRICA developed and implemented the standardized BIOTA Biodiversity Observatories, that meet the following criteria (a) enable long-term monitoring of biodiversity, potential driving factors, and relevant indicators with adequate spatial and temporal resolution, (b) facilitate comparability of data generated within different ecosystems, (c) allow integration of many disciplines, (d) allow spatial up-scaling, and (e) be applicable within a network approach. A BIOTA Observatory encompasses an area of 1 km2 and is subdivided into 100 1-ha plots. For meeting the needs of sampling of different organism groups, the hectare plot is again subdivided into standardized subplots, whose sizes follow a geometric series. To allow for different sampling intensities but at the same time to characterize the whole square kilometer, the number of hectare plots to be sampled depends on the requirements of the respective discipline. A hierarchical ranking of the hectare plots ensures that all disciplines monitor as many hectare plots jointly as possible. The BIOTA Observatory design assures repeated, multidisciplinary standardized inventories of biodiversity and its environmental drivers, including options for spatial up- and downscaling and different sampling intensities. BIOTA Observatories have been installed along climatic and landscape gradients in Morocco, West Africa, and southern Africa. In regions with varying land use, several BIOTA Observatories are situated close to each other to analyze management effects