Assessing the effect of seasonality on leaf and canopy spectra for the discrimination of an alien tree Species, Acacia Mearnsii, from co-occurring native species using parametric and nonparametric classifiers

The tree Acacia mearnsii is native to south-eastern Australia but has become an aggressive invader in many countries. In South Africa, it is a significant threat to the conservation of biomes. Detecting and mapping its early invasion is critical. The current ground-based methods to map A. mearnsii are accurate but are neither economical nor practical. Remote sensing (RS) provides accurate and repeatable spatial information on tree species. The potential of RS technology to map A. mearnsii distributions remains poorly understood, mainly due to a lack of knowledge on the spectral properties of A. mearnsii relative to co-occurring native plants. We investigated the spectral uniqueness of A. mearnsii compared to co-occurring native plant species within the South African landscape. We explored full-range (400-2500 nm), leaf and canopy hyperspectral reflectance of the species. The spectral reflectance was collected biweekly from December 23, 2016 and May 31, 2017. We conducted a time series analysis, to assess the effect of seasonality on species discrimination. For comparison, two classification models were employed: parametric interval extended canonical variate discriminant (iECVA-DA) and nonparametric random forest discriminant classifiers (RF-DA). The results of this paper suggest that phenology plays a crucial role in discriminating between A. mearnsii and sampled species. The RF classifier discriminated A. mearnsii with slightly higher accuracies (from 92% to 100%) when compared with the iECVA-DA (from 85% to 93%). The study showed the potential of RS to discriminate between A. mearnsii and co-occurring plant species.The Council for Scientific and Industrial Research and the National Research Foundation (NRF).http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36hj2020Plant Production and Soil Scienc

<i>Ailanthus altissima</i> mapping from multi-temporal very high resolution satellite images

This study presents the results of multi-seasonal WorldView-2 (WV-2) satellite images classification for the mapping of Ailanthus altissima (A. altissima), an invasive plant species thriving in a protected grassland area of Southern Italy. The technique used relied on a two-stage hybrid classification process: the first stage applied a knowledge-driven learning scheme to provide a land cover map (LC), including deciduous vegetation and other classes, without the need of reference training data; the second stage exploited a data-driven classification to: (i) discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map; (ii) determine the most favourable seasons for such recognition. In the second stage, when a traditional Maximum Likelihood classifier was used, the results obtained with multi-temporal July and October WV-2 images, showed an output Overall Accuracy (OA) value of ?91%. To increase such a value, first a low-pass median filtering was used with a resulting OA of 99.2%, then, a Support Vector Machine classifier was applied obtaining the best A. altissima User\u27s Accuracy (UA) and OA values of 82.47% and 97.96%, respectively, without any filtering. When instead of the full multi-spectral bands set some spectral vegetation indices computed from the same months were used the UA and OA values decreased. The findings reported suggest that multi-temporal, very high resolution satellite imagery can be effective for A. altissima mapping, especially when airborne hyperspectral data are unavailable. Since training data are required only in the second stage to discriminate A. altissima from other deciduous plants, the use of the first stage LC mapping as pre-filter can render the hybrid technique proposed cost and time effective. Multi-temporal VHR data and the hybrid system suggested may offer new opportunities for invasive plant monitoring and follow up of management decision

Hyperspectral Data Analysis in R: The hsdar Package

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new hsdar package for R statistical software, which performs a variety of analysis steps taken during a typical hyperspectral remote sensing approach. The package introduces a new class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. In addition, the package provides methods to directly use the functionality of the caret package for machine learning tasks. Two case studies demonstrate the package's range of functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the human larynx is detected from hyperspectral data

Understanding Striga occurrence and risk under changing climatic conditions across different agroecological farming systems at local and regional scales122

Philosophiae Doctor - PhDThe invasion by Striga in most cereal crop fields in Africa has posed an acute threat to food security and socioeconomic integrity. Consequently, numerous technological and research developments have been made to minimize and even control the Striga impacts on crop production. So far, efforts to control Striga have primarily focused on the manipulation of the genetics of the host crops, as well as understanding the phenological and physiological traits, along with the chemical composition of the weed

Three dimensional estimation of vegetation moisture content using dual-wavelength terrestrial laser scanning

PhD ThesisLeaf Equivalent Water Thickness (EWT) is a water status metric widely used in vegetation health monitoring. Optical Remote Sensing (RS) data, spaceborne and airborne, can be used to estimate canopy EWT at landscape level, but cannot provide information about EWT vertical heterogeneity, or estimate EWT predawn. Dual-wavelength Terrestrial Laser Scanning (TLS) can overcome these limitations, as TLS intensity data, following radiometric corrections, can be used to estimate EWT in three dimensions (3D). In this study, a Normalized Difference Index (NDI) of 808 nm wavelength, utilized in the Leica P20 TLS instrument, and 1550 nm wavelength, employed in the Leica P40 and P50 TLS systems, was used to produce 3D EWT estimates at canopy level. Intensity correction models were developed, and NDI was found to be able to minimize the incidence angle and leaf internal structure effects. Multiple data collection campaigns were carried out. An indoors dry-down experiment revealed a strong correlation between NDI and EWT at leaf level. At canopy level, 3D EWT estimates were generated with a relative error of 3 %. The method was transferred to a mixed-species broadleaf forest plot and 3D EWT estimates were generated with relative errors < 7 % across four different species. Next, EWT was estimated in six short-rotation willow plots during leaf senescence with relative errors < 8 %. Furthermore, a broadleaf mixed-species urban tree plot was scanned during and two months after a heatwave, and EWT temporal changes were successfully detected. Relative error in EWT estimates was 6 % across four tree species. The last step in this research was to study the effects of EWT vertical heterogeneity on forest plot reflectance. Two virtual forest plots were reconstructed in the Discrete Anisotropic Radiative Transfer (DART) model. 3D EWT estimates from TLS were utilized in the model and Sentinel-2A bands were simulated. The simulations revealed that the top four to five metres of canopy dominated the plot reflectance. The satellite sensor was not able to detect severe water stress that started in the lower canopy layers. This study showed the potential of using dual-wavelength TLS to provide important insights into the EWT distribution within the canopy, by mapping the EWT at canopy level in 3D. EWT was found to vary vertically within the canopy, with EWT and Leaf Mass per Area (LMA) being highly correlated, suggesting that sun leaves were able to hold more moisture than shade leaves. The EWT vertical profiles varied between species, and trees reacted in different ways during drought conditions, losing moisture from different canopy layers. The proposed method can provide time series of the change in EWT at very high spatial and temporal resolutions, as TLS instruments are active sensors, independent of the solar illumination. It also has the potential to provide EWT estimates at the landscape level, if coupled with automatic tree ii segmentation and leaf-wood separation techniques, and thus filling the gaps in the time series produced from satellite data. In addition, the technique can potentially allow the characterisation of whole-tree leaf water status and total water content, by combining the EWT estimates with Leaf Area Index (LAI) measurements, providing new insights into forest health and tree physiology.Egyptian Ministry of Higher Educatio

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Characterizing an Annual Grass Invasion and Its Link to Environmental and Disturbance Factors Using Remote Sensing: New Tools and Applications

The spread of nonnative species across the globe has contributed to biodiversity loss and changes in ecosystem structure and function. Monitoring the introduction, naturalization, and spread of introduced species is critical in abating negative impacts wrought by species invasions. However, providing basic information concerning the presence or spread of many introduced species is often only considered once the invasion is already at an advanced stage, resulting in economic or ecological impacts. To better assess the present and future effects of and risk from introduced species, a clear understanding of invasive species populations' spatial and temporal patterns is needed. In some cases, remote sensing can serve as a useful information source that may be leveraged to characterize and monitor the invasion of nonnative species. This dissertation utilizes remote sensing and other geospatial data sources to better understand a nonnative annual grass (Ventenata dubia) invasion in the northwestern United States. Each research chapter builds a different facet of our understanding of this invasion by connecting land-surface processes, environmental conditions, and landscape disturbances. These three different topics help to describe the current state of the invasion, how it progressed to this state, and what this may mean for the future. The first research-chapter adapts image fusion methods to a cloud-computing environment in an effort to improve the spatial and temporal resolution of estimates of land surface phenology. The research focused on whether these methods would enable the estimation of phenology in heterogeneous landscapes that have historically been difficult to characterize. This chapter showed that high-quality image fusion results are possible with less processing time when image fusion is conducted in a cloud-computing environment. Further, this chapter showed that phenology estimated from these data can capture patterns occurring in grassland, shrubland, and open forest land cover types. The second research-chapter leverages the improved land surface phenology estimates from the first research-chapter to model the present distribution of the invasive annual grass species Ventenata dubia in the Blue Mountains Ecoregion of the interior Pacific Northwest. The results from this chapter suggest that both phenological and environmental information are needed to best detect populations of ventenata. The model based on phenological and environmental information predicted that ventenata was present in 7.8% of the Blue Mountains Ecoregion in 2017. The third research-chapter uses the information gained from the proceeding chapters to examine the change occurring over a decade of invasion by applying the model developed in the second research-chapter to the image archive and examining the invasion progression. Spatial and temporal patterns of the invasion were characterized by their association with the biophysical environment and the effect of wildfire on ventenata occurence was investigated. This analysis revealed that ventenata may have been introduced to lower shrubland ecosystems but has since transitioned to higher elevation dry conifer forests and areas with abundant ecotone. Furthermore, this chapter shows that wildfire occurrence and severity was associated with an increased probability of invasion in some parts of the interior Pacific Northwest. Although this research is focused on a specific annual grass species (Ventenata dubia), insights gained from this investigation are applicable to other invasive annual grasses. This research contributes to the scientific advancement in the study of exotic plant invasion and provides useful baseline ecological information that can be employed to inform both policy and management. Additionally, the methods developed for cloud-computing-based image fusion offer a useful tool to the remote sensing community that has the flexibility to be utilized for many applications