Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Räumliche Vorhersage des Befallsrisikos an Winterweizen durch die Erreger Blumeria graminis f. sp. tritici (Echter Mehltau) und Puccinia triticina (Braunrost) in Schleswig-Holstein mittels maschineller Lernverfahren

Wheat is one of the most important cereals in the world. Phytopathogens such as powdery mildew or brown rust can considerably reduce wheat yields. By treatment with fungicides, infections with these pathogens can be contained. It is of decisive importance to be informed about upcoming dangerous infestation events in real time to be able to respond to them. To define which infestation events are to be classified as yield-relevant, this work uses the damage threshold concept. This concept assumes that the exceedance of a 70 % threshold value for powdery mildew and of a 30 % threshold value for brown rust of infected plants in a field would threaten the yield of the complete stock of winter wheat and, thus, suggests the application of fungicides. The main objective of this thesis is the spatial prediction of the probability of exceedance above this damage threshold. In order to achieve this goal, a concept was developed that regionalises the hourly weather data on a daily basis and subsequently uses these data as input parameters for predicting the pathogen-specific behaviour. Besides, the modelling concept uses supervised machine learning techniques to generate models based on these aggregated weather data, regionalised climate data and manually collected infestation data. The following learning methods are used to predict the occurrence of infestation spatially: k-Nearest Neighbor, Decision Trees, Boosted Decision Trees and Random Forests. The concept was examined iteratively using various evaluation methods, and thus the pathogen-specific performance of the models was tested concerning the prediction of the probability of infestation. The model results generated with the machine learning methods were then integrated into a web-based prediction system, which provides interested users with the probability of dangerous infestations.Weizen ist eines der bedeutendsten Getreide der Welt. Phytopathogene wie der Echte Mehltau oder der Braunrost können den Weizenertrag deutlich reduzieren. Durch Behandlung mit Fungiziden können Befälle mit diesen Erregern eingedämmt werden. Von entsprechender Bedeutung ist es, bevorstehende gefährliche Befallsereignisse vorherzusagen, um auf diese reagieren zu können. Um festzulegen, welche Befallsereignisse als ertragsrelevant zu klassifizieren sind, verwendet diese Arbeit das Schadschwellenkonzept nach \citet{Klink1997 . Entsprechend dieses Konzeptes, gehen von mehr als 70 % mit dem Echten Mehltau oder von mehr als 30 % mit dem Braunrost befallenen Pflanzen eines Bestandes eine Gefährdung des Weizenertrages aus, welche mit Fungiziden abgewandt werden sollte. Das Hauptziel dieser Arbeit ist die flächenhafte Vorhersage der Wahrscheinlichkeit einer überschreitung dieser Schadschwelle. Um dieses Ziel zu erreichen, wurde ein Konzept entwickelt, welches die stündlichen Wetterdaten tagesaktuell interpoliert und diese mit dem erregerspezifischen Verhalten in Beziehung setzt. Darüber hinaus verwendet das Konzept überwachte maschinelle Lernverfahren, um Modelle zu generieren, die auf diesen oben genannten Wetterdaten, den regionalisierten langfristigen Klimadaten und manuell erfassten Befallsdaten basieren. Zur räumlichen Vorhersage der Befallsereignisse werden dabei folgende Lernverfahren eingesetzt: k-Nearest Neighbor, Decision Trees, Boosted Decision Trees und Random Forests. Das Konzept wurde unter Verwendung verschiedener Evaluierungsverfahren iterativ überprüft und somit die erregerspezifische Performanz der Modelle hinsichtlich der Vorhersage der Befallswahrscheinlichkeit getestet. Die mit den maschinellen Lernverfahren erstellten Modellergebnisse wurden im Anschluss in ein webbasiertes Vorhersagesystem eingebunden, welches interessierten Nutzern die Wahrscheinlichkeit gefährlicher Befälle bereitstellt

PREDICTION OF SUSCEPTIBILITY FOR OLD TREES (> 100 YEARS OLD) TO FALL IN BOGOR BOTANICAL GARDEN

Since the establishment of the Bogor Botanical Garden (BBG) in 1817, the protection of the tree collections, even the loss of aging trees (> 100 years old), has been one of its most important tasks. Abiotic factors such as intense extreme events, i.e., heavy rainfall and strong winds, as well as biotic factors from human activities, pests and diseases, and the deterioration of the health of the plant collection with age, has threatened the survival of the old tree collections. As the BBG has many functions for conservation and human ecological activities, tree fall accidents have become a primary concern in preventing the loss of biodiversity and human life. Therefore, disaster map zonation is required to prevent and minimize such accident together with a prediction of which individual specimen is likely to fall. We examined the health of 154 to determine the falling probability of 1106 aged trees based on several factors that might cause the fall in the past and to make model predictions generated by nine supervised machine learning algorithms to get a binary value of falling probability and then classified into four categories (neglectable, low, moderate, and high probability of falling). Inverse Distance Weighted interpolation method was used to depict a zone map of trees prone to fall in BBG. We found 885 susceptible trees, of which 358 individual trees were highly susceptible to fall (red zone color), dominated by families from Fabaceae, Lauraceae, Moraceae, Meliaceae, Dipterocarpaceae, Sapindaceae, Rubiaceae, Myrtaceae, Araucariaceae, Malvaceae, and Anacardiaceae. This result was based on Random Forest model due to its highest accuracy among algorithms and its lowest false negative (FN) value. The FN value was important to minimize error calculation on aged trees that were not prone to fall but turned out to be prone to fall. The dominant factor contributing to high falling intensity was hollow and brittle on the tree trunks where many were found to have pests inside damaged parts such as termites, wood-borers, and bark-eaters. Several trees were found to have combined damages with more than a single causative factor that exacerbated tree’s health and increased falling probability

Data conditioning and climate sensitivity analysis of a probablistic rainfall-runoff model

The Munster Blackwater catchment, in the South West of Ireland, was regularly subject to flooding, prior to flood allevation works. The towns of Mallow and Fermoy within the catchment suffered many disturbances for their inhabitants with sometimes severe economic losses. A good knowledge of rainfall-runoff processes is important in order to understand the causes of flooding to be able to develop new infrastructure to manage flooding. The first part of this project focuses on the rainfall and river flow data collection from different sources: the 15-minute time step precipitation data from the OPW, the 15-minute river level/river flow from the OPW and the EPA and the precipitation data from MÉRA (Met Éireann ReAnalysis- Climate ReAnalysis). MÉRA is a very high resolution climate reanalysis dataset which was used to calculate the monthly and annual rainfall in a specific year, for example for 2010 for selected locations (the nearest point to each rain gauge). Initial analysis of the measured OPW data shows significant numbers of missing values and outliers for the precipitation data. A method was developed to cluster the rain gauges with similar precipitation patterns based on the amount of precipitation of the nearest points to these rain gauges from MÉRA. Then a gap filling method was applied in each cluster to fill the missing values of each rain gauge with its cluster members. Other methods were also examined to obtain quality controlled data. The second part of this project applies a conceptual hydrological model, PDM (Probability Distributed Model) developed by Moore (Moore, 2007) to the Munster Blackwater catchment. The model considers each point of a catchment as a single storage unit with a specific storage capacity (depth) that can be described by a Pareto distribution. PDM is suitable for a variety of catchments, and has minimal data and computational requirements. The input is 15-minute precipitation data from different rain gauges and 15-minute river level/river flow data from river stations along the river. The calibration was applied on three subcatchments of the Munster Blackwater catchment. The validation was applied for years between 2010 to 2017. The calibrations and validations indicate that the PDM model can explain most of the variability of observed flows in the different subcatchments over a period of years, especially when a high standard of data quality is available, for example in 2015. Then validation of the model for flood events was examined. Validation was applied for the highest flood event in each year during 2010 to 2017. The accuracy of the model runs are different for each subcatchment with the best accuracy of 93% in the Dromcummer subcatchment and the accuracies in Mallow Rail BR and Killavullen being 80 % and 78% respectively. The model estimates the peak and low flow very well in Dromcummer. The computed flow is underestimated in Mallow and overestimated in Killavullen. The third part of the project is to use the PDM model in a precipitation and river flow sensitivity analysis. This was achieved by increasing the precipitation amounts in the datasets by 10, 15, 20, 25 and 30% to examine how the peak flows and low flows respond. It was found that the peak flows increase by amounts similar to the precipitation increases. The low flows increase at a much lower rate than the precipitation increases. It is known that in a scenario of climate change for a warming world that the precipitation increases by a maximum of 7% per degree C increase in accordance with the Clausius-Clapeyron equation. However as a warming world also increases evaporation and will likely impact the soil moisture status, it is considered that flood flows might increase at a rate less than the precipitation increases. This can be examined by increasing the value of potential evaporation by 10, 15, 20, 25 and 30% .These conditions were not included in this and it is ecommended that further research be done in this area for Ireland

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Proximal soil sensors and geostatistical tools in precision agriculture applications

Recognition of spatial variability is very important in precision agriculture applications. The use of proximal soil sensors and geostatistical techniques is highly recommended worldwide to detect spatial variation not only in fields but also within-field (micro-scale). This study involves, as a first step, the use of visible and near infrared (vis-NIR) spectroscopy to estimate soil key properties (6) and obtain high resolution maps that allow us to model the spatial variability in the soil. Different calibration models were developed using partial least square regression (PLSR) for different soil properties. These calibration models were evaluated by both cross-validation and independent validation. Results show good to excellent calibration models for most of soil properties under study in both cross-validation and independent validation. The on-line maps created using the collected on-line spectra and the calibration models previously estimated for each soil property were compared with three different maps (measured, predicted, error). The second step uses multivariate geostatistical analysis to develop three different geostatistical models (soil, spectral, fusion). The soil model includes 8 soil properties, spectral model includes 4 soil properties and the fusion model includes 12 soil properties. The three models were evaluated by cross-validation and the results show that the goodness of fitting can be considered as satisfactory for the soil model, whereas the performance of the spectral model was quite poor. Regarding the fusion model, it performed quite well, though the model generally underestimated the high values and overestimated the low values. An independent validation data set was used to evaluate the performance of the three models calculating three statistics: mean error (ME), as an indicator of bias; mean standardized squared error (MSSE), as an indicator of accuracy, and root mean squared error (RMSE), as an indicator of precision of estimation. Synthetically, the two, soil and fusion, models performed quite similarly, whereas the performance of the spectral model was much poorer. With regard to delineation of management zones (MZs), the factor cokriging analysis was applied using the three different models. The first factor (F1) for the soil and fusion models was related to soil properties that affect soil fertility, whereas for the spectral model was related to P (-0.88) and pH (-0.42). Based on the first factor of the soil and fusion models, three management zones were delineated and classified as low, medium and high fertility zones using isofrequency classes. Spatial similarity between the yield map and delineated MZs maps based on F1 for the soil and fusion models was calculated. The overall accordance between the two maps was 40.0 % for the soil model and 38.6 % for the fusion model. The two models performed quite similarly. These results can be interpreted as more than 50% of the yield variation was ascribable to more dynamic factors than soil parameters not included in this study, such as agro-meteorological conditions, plant diseases, nutrition stresses, etc. However, the results are quite promising for the application of the proposed approach in site-specific management.</br

Fine-scale Inventory of Forest Biomass with Ground-based LiDAR

Biomass measurement provides a baseline for ecosystem valuation required by modern forest management. The advent of ground-based LiDAR technology, renowned for 3D sampling resolution, has been altering the routines of biomass inventory. The thesis develops a set of innovative approaches in support of fine-scale biomass inventory, including automatic extraction of stem statistics, robust delineation of plot biomass components, accurate classification of individual tree species, and repeatable scanning of plot trees using a lightweight scanning system. Main achievements in terms of accuracy are a relative root mean square error of 11% for stem volume extraction, a mean classification accuracy of 0.72 for plot wood components, and a classification accuracy of 92% among seven tree species. The results indicate the technical feasibility of biomass delineation and monitoring from plot-level and multi-species point cloud datasets, whereas point occlusion and lack of fine-scale validation dataset are current challenges for biomass 3D analysis from ground.S.G.S. International Tuition Award from the University of Lethbridge The Dean's Scholarship from the University of Lethbridge Campus Alberta Innovates Program NSERC Discovery Grants Progra

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Annals [...].

Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin