5 research outputs found

    Transforming scientific research and development in precision agriculture : the case of hyperspectral sensing and imaging : a thesis presented in partial fulfilment of the requirements for the degree of Doctor in Philosophy in Agriculture at Massey University, Manawatū, New Zealand. EMBARGOED until 30 September 2023.

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    Embargoed until 30 September 2023There has been increasing social and academic debate in recent times surrounding the arrival of agricultural big data. Capturing and responding to real world variability is a defining objective of the rapidly evolving field of precision agriculture (PA). While data have been central to knowledge-making in the field since its inception in the 1980s, research has largely operated in a data-scarce environment, constrained by time-consuming and expensive data collection methods. While there is a rich tradition of studying scientific practice within laboratories in other fields, PA researchers have rarely been the explicit focal point of detailed empirical studies, especially in the laboratory setting. The purpose of this thesis is to contribute to new knowledge of the influence of big data technologies through an ethnographic exploration of a working PA laboratory. The researcher spent over 30 months embedded as a participant observer of a small PA laboratory, where researchers work with nascent data rich remote sensing technologies. To address the research question: “How do the characteristics of technological assemblages affect PA research and development?” the ethnographic case study systematically identifies and responds to the challenges and opportunities faced by the science team as they adapt their scientific processes and resources to refine value from a new data ecosystem. The study describes the ontological characteristics of airborne hyperspectral sensing and imaging data employed by PA researchers. Observations of the researchers at work lead to a previously undescribed shift in the science process, where effort moves from the planning and performance of the data collection stage to the data processing and analysis stage. The thesis develops an argument that changing data characteristics are central to this shift in the scientific method researchers are employing to refine knowledge and value from research projects. Importantly, the study reveals that while researchers are working in a rapidly changing environment, there is little reflection on the implications of these changes on the practice of science-making. The study also identifies a disjunction to how science is done in the field, and what is reported. We discover that the practices that provide disciplinary ways of doing science are not established in this field and moments to learn are siloed because of commercial constraints the commercial structures imposed in this case study of contemporary PA research

    UNDERSTANDING THE BIODIVERSITY PATTERNS OF CRYPTOGAMS (BRYOPHYTES AND LICHENS) IN BOREAL FORESTS THROUGH REMOTE SENSING/COMPRENDRE LES PATRONS DE BIODIVERSITÉ DES CRYPTOGAMES (BRYOPHYTES ET LICHENS) DANS LES FORÊTS BORÉALES GRÂCE À LA TÉLÉDÉTECTION

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    Anglais : Cryptogams (bryophytes and lichens) are ubiquitous non-vascular species that contribute significantly to total biodiversity and play an essential ecological role in ecosystem functioning worldwide. Specifically, cryptogams influence water, carbon and nutrient cycles, as well as physical and chemical weathering, and increase stability of soils, preventing their erosion and regulating their temperature and humidity. Cryptogams facilitate ecosystem recovery following disturbances, and provide microhabitats for micro- and macroorganisms, and a food source for invertebrates and herbivores. These species are also reliable and highly sensitive indicators to environmental disturbances and currently face numerous human-induced threats mainly derived from land use and climate change. Despite this, cryptogams are generally neglected in conservation planning mostly due to current knowledge gaps in their diversity, ecology and distribution, which jeopardizes the maintenance of their species and ecological role. New technologies and data sources such as remote sensing (RS) can significantly help to fill these gaps and ultimately improve the representation of cryptogams in systematic conservation planning. The contribution of RS to cryptogam biodiversity assessments can be particularly valuable in vast and largely unknown regions such as boreal forests, where these species and their habitats face increasing human-induced threats. The general objective of this thesis is to elucidate the role that RS can play in the evaluation and generation of information on cryptogam biodiversity in a boreal context. The study region is located in the Canadian boreal forest, within the Eeyou-Istchee James Bay region in Northern Quebec. As specific objectives, Chapter II aims to predict and map diversity (species richness) patterns of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) using RS data; Chapter III focusses on producing predictive models of rare bryophyte species using RS-derived predictors in an Ensembles of Small Models (ESMs) framework; and Chapter IV is intended to describe and model the lichen alpha diversity (species richness) and beta diversity (species turnover) components parallelly using two set of RS-derived variables (Red and NIR; EVI2) from two sensors (Wordlview-3, WV3; Sentinel-2, S2) at different high spatial resolutions (1.2m; 10m), and ii) to identify which habitat types represent lichen biodiversity hotspots. The Random Forest algorithm used in Chapter II allowed us to develop spatially explicit models and to generate predictive cartography at 30m resolution of total bryophyte, moss, liverwort and sphagna richness. These models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), and consistently identified vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze-thaw events) as the most important predictors for all bryophyte groups modeled. Guild-level models identified differences in important factors determining the richness of each of the guilds and thus in their predicted richness patterns, which provide valuable information for management and conservation strategies for bryophytes. The RS-based ESMs developed in Chapter III built from Random Forest and Maxent techniques using predictors related to topography (TPI) and vegetation (EVI2, NDWI1, Vegetation Continuous fields, and PALSAR HVHH) yielded poor to excellent prediction accuracy (AUC > 0.5) for 38 of the 52 modeled species despite their low number of occurrences ( 0.8 for 19 species. The actual presences of the 38 species modeled better than random (AUC ≤ 0.5) were accurately predicted, as supported by the high sensitivity values obtained that ranged from 0.8 to 1 with an average of 0.959 ± 0.063. The distribution of these 38 species and the richness patterns both for total rare bryophytes and rare species at the guild level were mapped at 30m resolution. Chapter III also revealed a spatial concordance between rare (present chapter) and overall bryophyte richness patterns (Chapter II) in different regions of the study area, which has important implications for conservation planning. In Chapter IV, a total of 116 lichen species were identified. While high lichen richness was generally found across our plots (36.5 ± 9 species), those richer in microhabitats often harbored more species (R2 = 0.22) regardless of the habitat type. Differences in species composition were identified among plots (25.6% explained by PCoA) and habitat types (PERMANOVA R2 = 0.35), both being supported by differences in microhabitat composition (Mantel r = 0.22 and PERMANOVA R2 = 0.29, respectively). Rocky outcrops and undisturbed coniferous forests represented the main lichen biodiversity hotspots, while other habitat types were also important for maintaining overall biodiversity. Red and NIR variables were effective for modeling alpha and beta diversity at both resolutions, while EVI2, either from WV3 or S2, was only informative for assessing beta diversity. Poisson models explained up to 32% of the variation in lichen richness. Generalized dissimilarity models described well the relationship between beta diversity and spectral dissimilarity (R2 from 0.25 to 0.30), except for the S2 EVI2 model (R2 = 0.07), confirming that more spectrally and thus environmentally different areas tend to harbor different lichen communities. While WV3 often outperformed the S2 sensor, the latter still provides a powerful tool for the study of lichens and their conservation. This thesis demonstrated the ability for RS at medium and high spatial resolutions to characterize the habitat of inconspicuous cryptogam species, to capture diverse meaningful ecological features shaping their distribution, and thus to better understand and/or predict their biodiversity patterns. RS-based modeling frameworks proved to be informative even when the available baseline information on cryptogam biodiversity was limited. By identifying environmental drivers of cryptogam biodiversity that can guide specific management actions, and by providing predictive mapping of their spatial patterns at high level of detail across the landscape, this work unequivocally highlighted the high potential of RS technology for conservation purposes of cryptogams. This thesis thus represents a very important step to achieve the inclusion of these inconspicuous and generally overlooked species into systematic conservation planning. Français : Les cryptogames (bryophytes et lichens) sont des espèces non vasculaires omniprésentes qui contribuent de manière significative à la biodiversité et jouent un rôle écologique essentiel dans le fonctionnement des écosystèmes à l'échelle mondiale. Plus précisément, les cryptogames influencent les cycles de l'eau, du carbone et des nutriments, ainsi que l'altération physique et chimique des roches, et augmentent la stabilité des sols, empêchant leur érosion et régulant leur température et humidité. Les cryptogames facilitent le rétablissement des écosystèmes après des perturbations et fournissent des microhabitats pour des micro- et macro-organismes, ainsi qu'une source de nourriture pour des invertébrés et herbivores. Ces espèces sont également sont des indicateurs fiables mais très sensibles aux perturbations environnementales et sont actuellement confrontées à de nombreuses menaces d'origine humaine principalement dérivées de l'utilisation des terres et du changement climatique. Malgré cela, les cryptogames sont généralement négligés dans la planification de la conservation, principalement en raison des lacunes actuelles dans les connaissances sur leur diversité, écologie et distribution, ce qui met en péril le maintien de leur espèces et rôle écologique. Les nouvelles technologies et sources de données telles que la télédétection peuvent contribuer de manière significative à combler ces lacunes et, en fin de compte, à améliorer la représentation des cryptogames dans la planification systématique de la conservation. La contribution de la télédétection aux évaluations de la biodiversité des cryptogames peut être particulièrement précieuse dans des régions vastes et largement inconnues telles que les forêts boréales, où ces espèces et leurs habitats sont confrontés à des menaces croissantes d'origine humaine. L'objectif général de cette thèse est d'élucider le rôle que peut jouer la télédétection dans l'évaluation et la génération d'informations sur la biodiversité des cryptogames en contexte boréal. La région d'étude est située dans la forêt boréale canadienne, dans la région d'Eeyou-Istchee Baie-James dans le Nord du Québec. En tant qu'objectifs spécifiques, le chapitre II vise à prédire et à cartographier les patrons de diversité (richesse en espèces) i) des bryophytes totaux et ii) des guildes de bryophytes (mousses, hépatiques et sphaignes) à l'aide de données de télédétection; le chapitre III se concentre sur la production de modèles prédictifs d'espèces de bryophytes rares à l'aide de prédicteurs dérivés de la télédétection dans un cadre d'ensembles de petits modèles; et le chapitre IV est destiné à décrire et modéliser les composantes alpha (richesse des espèces) et beta (changements de composition de la communauté) de la biodiversité des lichens en utilisant en parallèle deux ensembles de variables dérivées de la télédétection (Red et NIR; EVI2) à partir de deux capteurs (Wordlview-3 , WV3 ; Sentinel-2, S2) à différentes résolutions spatiales élevées (1,2 m ; 10m), et ii) à identifier les types d'habitats qui représentent les points chauds de la biodiversité des lichens. L'algorithme Random Forest utilisé dans le chapitre II nous a permis de développer des modèles spatialement explicites et de générer une cartographie prédictive à 30m de résolution de la richesse totale en bryophytes, mousses, hépatiques et sphaignes. Ces modèles expliquent une fraction importante de la variation de la richesse totale en bryophytes et au niveau de la guilde, à la fois dans les ensembles de calibration (42 à 52 %) et de validation (38 à 48 %), et identifient systématiquement la végétation (principalement NDVI) et les variables climatiques (température , précipitations et événements de gel-dégel) comme les prédicteurs les plus importants pour tous les groupes de bryophytes modélisés. Les modèles au niveau de la guilde ont identifié des différences dans des facteurs importants déterminant la richesse de chacune des guildes et donc dans leurs modèles de richesse prédits, qui fournissent des informations précieuses pour les stratégies de gestion et de conservation des bryophytes. Les ensembles de petits modèles basés sur la télédétection développés au chapitre III construits à partir des techniques Random Forest et Maxent en utilisant des prédicteurs liés à la topographie (TPI) et à la végétation (EVI2, NDWI1, Vegetation Continuous fields et PALSAR HVHH) ont donné une précision de prédiction de faible à excellente (AUC > 0.5) pour 38 des 52 espèces modélisées malgré leur faible nombre d'occurrences ( 0.8 pour 19 espèces. Les présences réelles des 38 espèces modélisées mieux que aléatoires (AUC ≤ 0.5) ont été prédites avec précision, comme en témoignent les valeurs de sensibilité élevées obtenues allant de 0.8 à 1 avec une moyenne de 0.959 ± 0.063. La distribution de ces 38 espèces et les patrons de richesse à la fois pour les bryophytes rares totales et les espèces rares au niveau de la guilde ont été cartographiés à une résolution de 30m. Le chapitre III a également révélé une concordance spatiale entre les patrons de richesse en bryophytes rares (chapitre présent) et totaux (chapitre II) dans différentes régions de la zone d'étude, ce qui a des implications importantes pour la planification de la conservation. Au chapitre IV, un total de 116 espèces de lichens ont été identifiées. Alors qu'une grande richesse en lichens était généralement observée dans nos parcelles (36.5 ± 9 espèces), celles plus riches en microhabitats abritaient souvent plus d'espèces (R2 = 0.22) quel que soit le type d'habitat. Des différences dans la composition des espèces ont été identifiées entre les parcelles (25.6 % expliquées par la PCoA) et les types d'habitats (PERMANOVA R2 = 0.35), tous deux étayés par des différences dans la composition des microhabitats (Mantel r = 0.22 et PERMANOVA R2 = 0.29, respectivement). Les affleurements rocheux et les forêts de conifères non perturbées représentaient les principaux points chauds de la biodiversité des lichens, tandis que d'autres types d'habitats étaient également importants pour le maintien de la biodiversité totale Les variables Red et NIR étaient efficaces pour modéliser la diversité alpha et bêta aux deux résolutions, tandis que EVI2, soit de WV3 ou S2, n'était informatif que pour évaluer la diversité bêta. Les modèles de Poisson expliquaient jusqu'à 32% de la variation de la richesse en lichens. Les modèles de dissimilarité généralisée décrivaient bien la relation entre la diversité bêta et la dissimilarité spectrale (R2 de 0.25 à 0.30), sauf pour le modèle S2 EVI2 (R2 = 0.07), confirmant que des zones plus spectralement et donc environnementales différentes ont tendance à abriter différentes communautés de lichens. Alors que WV3 a souvent surpassé le capteur S2, ce dernier fournit toujours un outil puissant pour l'étude des lichens et leur conservation. Cette thèse a démontré la capacité de la télédétection à moyenne et haute résolution spatiale à caractériser l'habitat d'espèces cryptogames discrètes, à capturer diverses caractéristiques écologiques significatives façonnant leur distribution, et ainsi à mieux comprendre et/ou prédire leurs patrons de biodiversité. Les cadres de modélisation basés sur la télédétection se sont avérés informatifs même lorsque les informations de base disponibles sur la biodiversité des cryptogames étaient limitées. En identifiant les facteurs environnementaux de la biodiversité des cryptogames qui peuvent guider des actions de gestion spécifiques et en fournissant une cartographie prédictive de leurs patrons spatiaux à un niveau de détail élevé à travers le paysage, ce travail a mis en évidence sans équivoque le potentiel élevé de la technologie de télédétection à des fins de conservation des cryptogames. Cette thèse représente donc une étape très importante pour parvenir à l'inclusion de ces espèces discrètes et généralement négligées dans la planification systématique de la conservation

    Detection of New Zealand Kauri Trees with AISA Aerial Hyperspectral Data for Use in Multispectral Monitoring

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    The endemic New Zealand kauri trees (Agathis australis) are of major importance for the forests in the northern part of New Zealand. The mapping of kauri locations is required for the monitoring of the deadly kauri dieback disease (Phytophthora agathidicida (PTA)). In this study, we developed a method to identify kauri trees by optical remote sensing that can be applied in an area-wide campaign. Dead and dying trees were separated in one class and the remaining trees with no to medium stress symptoms were defined in the two classes “kauri” and “other”. The reference dataset covers a representative selection of 3165 precisely located crowns of kauri and 21 other canopy species in the Waitakere Ranges west of Auckland. The analysis is based on an airborne hyperspectral AISA Fenix image (437–2337 nm, 1 m2 pixel resolution). The kauri spectra show characteristically steep reflectance and absorption features in the near-infrared (NIR) region with a distinct long descent at 1215 nm, which can be parameterised with a modified Normalised Water Index (mNDWI-Hyp). With a Jeffries–Matusita separability over 1.9, the kauri spectra can be well separated from 21 other canopy vegetation spectra. The Random Forest classifier performed slightly better than Support Vector Machine. A combination of the mNDWI-Hyp index with four additional spectral indices with three red to NIR bands resulted in an overall pixel-based accuracy (OA) of 91.7% for crowns larger 3 m diameter. While the user’s and producer’s accuracies for the class “kauri” with 94.6% and 94.8% are suitable for management purposes, the separation of “dead/dying trees” from “other” canopy vegetation poses the main challenge. The OA can be improved to 93.8% by combining “kauri” and “dead/dying” trees in one class, separate classifications for low and high forest stands and a binning to 10 nm bandwidths. Additional wavelengths and their respective indices only improved the OA up to 0.6%. The method developed in this study allows an accurate location of kauri trees for an area-wide mapping with a five-band multispectral sensor in a representative selection of forest ecosystems

    New Zealand kauri trees : identification and canopy stress analysis with optical remote sensing and LiDAR data.

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    The endemic New Zealand kauri trees (Agathis australis (D.Don) Lindl.) are a key species in New Zealand’s northern indigenous forests. As one of the largest and longest-lived trees in the world, mature kauri are a tourist attraction and have high cultural significance for local Māori. However, the trees are threatened by the deadly kauri dieback disease (Phytophthora agathidicida (PA)). Over the last decade, PA has been detected throughout most of the kauri distribution area. PA is a soil-borne pathogen that enters the trees via the root system and causes collar rot, thereby blocking the transport of water and nutrients to the canopy, eventually killing the tree. This thesis aims to develop methods based on remote sensing to automatically identify kauri trees and detect stress symptoms in their canopy. It is important to note that canopy stress symptoms are not proof of an infection. The reference data used here include 3165 precisely located crowns from three study sites in the Waitakere Ranges west of Auckland. They cover a representative range of both kauri and associated tree species in different forest ecotypes and stand situations. The selection of kauri crowns includes a range of phenological varieties, such as colour variants, growth stages and stress symptom levels. The structure of this thesis follows three research questions, which form the basis of three scientific papers. The first paper aims to identify kauri trees with optical remote sensing. A distinct spectral pattern of kauri crowns could be discovered with the use of an airborne AISA Fenix hyperspectral image in the far near-infrared part of the spectrum. The paper presents a method to distinguish kauri with no to medium symptoms from dead and dying tree crowns and other canopy species with no to medium symptoms. High user’s and producer’s accuracies of 94.6% and 94.8% for the class “kauri” were achieved in a Random Forest classification using five spectral indices on five wavelengths (670–1209 nm). The kauri spectra showed a high separability to the spectra of 21 other canopy species and vegetation. However, the distinction between dead and dying trees and other tree species turned out to be more difficult. A minimum crown diameter of 3 m was defined for the 1 m pixel resolution to minimize the effect of mixed pixels. The overall accuracy (OA) for the three target classes could be improved from 91.7% to 93.8% by combining “kauri” and “dead/dying” trees into one class, separately classifying low and high forest stands and a binning to 10 nm bandwidths. The second paper focuses on an analysis of reflectance patterns for different stress levels and growth stages in kauri crowns. The analysis was again based on hyperspectral images and 1258 manually edited reference crowns of “kauri” and “dead/dying” trees. The field assessment for stress symptoms was complemented with an evaluation of visible canopy symptoms in Red-Green-Blue (RGB) aerial images. An image guideline for stress assessment based on aerial images was developed. A Normalised Difference Vegetation Index (NDVI) in the near-infrared/red spectral range and indices with bands in the near-infrared and red-edge were identified as the most important band combinations to describe the full range of stress responses. However, pigment-sensitive indices with bands in the green and red spectral ranges are more important for describing first stress symptoms and stress responses in smaller trees with denser foliage. Five indices on six bands in the visible to near-infrared region (450–970 nm) achieved a correlation of 0.93 with a Random Forest regression for the description of five stress symptom levels from non-symptomatic to dead. A stratified approach with individual models for pre-segmented low and high forest stands improved the overall performance. Additional bands in the far near-infrared region improved the root mean square error (RMSE) slightly from 0.43 to 0.42 but not the correlation. In the third paper, the use of WorldView-2 satellite data (8 multispectral bands, 1.8 m pixel resolution pan-sharpened to 0.45 m) in combination with LiDAR data was tested for the stress detection with 1089 manually edited reference crowns of kauri and dead and dying trees. Five basic levels of canopy stress symptoms, from non-symptomatic to dead, were further refined for the first symptom stages based on field observations and aerial images. The minimum crown diameter for the use of WorldView-2 attributes for stress detection was defined as 4 m to avoid mixed pixels and to detect dying top branches in smaller crowns. Attributes from only the WorldView-2 image resulted in a correlation of 0.89 (RMSE 0.48, mean absolute error (MAE) 0.34) in a Random Forest regression for crowns larger than 4 m in diameter. This result can be improved to a correlation of 0.92 (RMSE 0.43, MAE 0.31) with additional LIDAR attributes, including intensity values. The selection of attributes confirms the findings from the second study, with an NDVI on near-infrared and red bands as the most important spectral index for the full range of stress symptoms. It also confirms the higher importance of pigment-sensitive indices with green, red and red-edge bands for the detection of first stress symptoms. These initial symptoms are more related to changes in the foliage than the crown architecture. The results of this thesis present a methodical basis for kauri identification and stress detection using remote sensing data. The methods presented here require further testing and refinement with reference data in other forest areas and should be applied in the full processing chain with automatic crown-segmentation. However, when this has been done, remote sensing methods have considerable potential for automated monitoring of canopy stress symptoms in kauri trees
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