The utility of very-high resolution unmanned aerial vehicles (UAV) imagery in monitoring the spatial and temporal variations in leaf moisture content of smallholder maize farming systems.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize moisture stress, resulting from rainfall variability, is a primary challenge in the production of rain-fed maize farming, especially in water-scarce regions such as southern Africa. Quantifying maize moisture variations throughout the growing season can support agricultural decision-making and prompt the rapid and robust detection of smallholder maize moisture stress. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit near real-time information for determining maize moisture content at farm scale. Therefore, this study evaluated the utility of UAV derived multispectral imagery in estimating maize leaf moisture content indicators on smallholder farming systems throughout the maize growing season. The first objective of the study was to conduct a comparative analysis in order to evaluate the performance of five regression techniques (support vector regression, random forest regression, decision trees regression, artificial neural network regression and the partial least squares regression) in predicting maize water content indicators (i.e. equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf area (SLA)), and determine the most suitable indicator of smallholder maize water content variability based on multispectral UAV data. The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising maize moisture indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC and SLA were derived from the random forest regression algorithm with a relative root mean square error (rRMSE) of 3.13%, 1% and 3.48 %, respectively. Additionally, EWT and FMC yielded the highest predictive performance of maize leaf moisture and demonstrated the best correlation with remotely sensed data. The study’s second objective was to evaluate the utility of UAVderived multispectral imagery in estimating the temporal variability of smallholder maize moisture content across the maize growing season using the optimal maize moisture indicators. The findings illustrated that the NIR and red-edge wavelengths were influential in characterising maize moisture variability with the best models for estimating maize EWT and FMC resulting in a rRMSE of 2.27 % and 1%, respectively. Furthermore, the early reproductive stage was the most optimal for accurately estimating maize EWT and FMC using UAVproximal remote sensing. The findings of this study demonstrate the prospects of UAV- derived multispectral data for deriving insightful information on maize moisture availability and overall health conditions. This study serves as fundamental step towards the creation of an early maize moisture stress detection and warning systems, and contributes towards climate change adaptation and resilience of smallholder maize farming

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

A comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (uav)-based proximal and remotely sensed data

: Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Precision irrigation management through thermal and multispectral remote sensing: An integration of sensing systems and analytical techniques

Doctor of PhilosophyDepartment of Biological & Agricultural EngineeringAjay ShardaIrrigation water management starts with quantifying irrigation prescriptions based on crop water requirements at a spatial scale. For determining the water requirement of the plants, canopy temperature-dependent crop water stress could provide a potential solution. The use of a small unmanned aerial system (sUAS) with a thermal infrared (TIR) camera has long been established as an effective method of measuring plant canopy temperatures at a spatial scale. However, concerns still exist about the accuracy of these systems in collecting canopy temperatures and estimating crop water stress. The overall goal of this research is to assess high spatial resolution crop water stress in a corn field and evaluate UAS-based thermal imagery’s capacity to provide precise canopy temperature. As a part of this study, the capacity and feasibility of UAV-based imagery to detect infield crop health variability were also evaluated against aircraft and satellite imagery. To analyze the effects of flying altitude and camera view angle on thermal infrared imagery, thermal cameras with different focal lengths (9 mm, 13 mm, and 19 mm) were flown at different altitudes (30 m, 50 m, and 70 m). The orthomosaics generated from images were examined for the accuracy of the corn-canopy temperature sensing, ability to differentiate between hot and cold surfaces, ease of image stitching, geometric accuracy, image quality, and spatial resolution. The results indicated that a canopy temperature map of crops with a temperature error of less than 2° C from the actual canopy temperature can be produced with the combination of appropriate camera focal length, altitude, and image calibration techniques. A narrow-angle thermal camera flying at low altitudes (<50 m) was found to be the least suitable combination for corn canopy temperature sensing. The most appropriate combination for temperature estimation of corn canopies was with a 13 mm focal length camera flying at an altitude of 50 m above ground level. To quantify corn’s crop water stress index (CWSI), images were collected using a thermal camera and a multispectral camera mounted on a Matrice 100 sUAS, over a four acres corn field divided into three irrigation levels (50%, 75%, and 100% irrigation level). Field-specific water stress baselines were developed and used in CWSI quantification to consider the effect of the instant local environment. High-resolution precise crop water stress maps developed from thermal images were capable of inter-row and intra-row detection of corn water stress. The vegetative indices significantly explained variation in crop water stress, with NDRE (Normalized Difference Red Edge index) having the highest R2 value of 0.8 and NDVI (Normalized Difference Vegetation Index) having an R2 value of 0.7. Field-measured leaf water potential also significantly affected water stress but showed a weaker correlation with R2 values of 0.6. Overall results from this study showed that the combination of thermal imaging and NIR imaging could be utilized to determine accurate crop water stress on the spatial scale for irrigation water management and scheduling. A comparative assessment of UAS (Matrice-100), aircraft (Ceres Imaging), and satellite (Landsat-8) imagery to detect infield crop health variability for the implementation of a precision irrigation system was also accomplished. Spatial maps of canopy temperature and NDVI were developed using the images from different imaging platforms and analyzed for capacity to capture water requirements and crop health accurately. UAV imagery outperformed the other two platforms by providing the highest number of pixels and variations in temperatures and NDVI values to represent a given target area. Moderate and low spatial resolution imagery from aircraft (1-1.5 m/pixel) and satellite (30 m/pixel) was limited in detecting inter-row variability and outputting the average pixels of the crop canopy and inter-row space. Whereas high-resolution UAV imagery (1.5 cm/pixel – 6 cm/pixel) precisely distinguished inter-row gap from plants and provided crop-only pixels without mixing with background soil. UAV imagery and aircraft imagery remains competitive in detecting crop variability between two nozzles of an irrigation pivot. UAV imagery was much more sensitive and precise in detecting minute changes as compared to other platforms. Satellite imagery was limited in capturing the variations at this small scale. In summary, this study provided an appropriate combination of camera focal length and flying altitude to accurately and efficiently estimate canopy temperature and crop water stress in corn. Methods were developed to precisely detect inter and intra-row crop water stress and health variability using low-altitude high-resolution UAV imagery. Detailed insight into the capacity of different remote sensing platforms was provided to detect crop health variability in small-scale farms and implement crop irrigation management based on crop canopy temperatures

Assimilation de données satellitaires pour le suivi des ressources en eau dans la zone Euro-Méditerranée

Une estimation plus précise de l'état des variables des surfaces terrestres est requise afin d'améliorer notre capacité à comprendre, suivre et prévoir le cycle hydrologique terrestre dans diverses régions du monde. En particulier, les zones méditerranéennes sont souvent caractérisées par un déficit en eau du sol affectant la croissance de la végétation. Les dernières simulations du GIEC (Groupe d'Experts Intergouvernemental sur l'Evolution du Climat) indiquent qu'une augmentation de la fréquence des sécheresses et des vagues de chaleur dans la région Euro-Méditerranée est probable. Il est donc crucial d'améliorer les outils et l'utilisation des observations permettant de caractériser la dynamique des processus des surfaces terrestres de cette région. Les modèles des surfaces terrestres ou LSMs (Land Surface Models) ont été développés dans le but de représenter ces processus à diverses échelles spatiales. Ils sont habituellement forçés par des données horaires de variables atmosphériques en point de grille, telles que la température et l'humidité de l'air, le rayonnement solaire et les précipitations. Alors que les LSMs sont des outils efficaces pour suivre de façon continue les conditions de surface, ils présentent encore des défauts provoqués par les erreurs dans les données de forçages, dans les valeurs des paramètres du modèle, par l'absence de représentation de certains processus, et par la mauvaise représentation des processus dans certaines régions et certaines saisons. Il est aussi possible de suivre les conditions de surface depuis l'espace et la modélisation des variables des surfaces terrestres peut être améliorée grâce à l'intégration dynamique de ces observations dans les LSMs. La télédétection spatiale micro-ondes à basse fréquence est particulièrement utile dans le contexte du suivi de ces variables à l'échelle globale ou continentale. Elle a l'avantage de pouvoir fournir des observations par tout-temps, de jour comme de nuit. Plusieurs produits utiles pour le suivi de la végétation et du cycle hydrologique sont déjà disponibles. Ils sont issus de radars en bande C tels que ASCAT (Advanced Scatterometer) ou Sentinel-1. L'assimilation de ces données dans un LSM permet leur intégration de façon cohérente avec la représentation des processus. Les résultats obtenus à partir de l'intégration de données satellitaires fournissent une estimation de l'état des variables des surfaces terrestres qui sont généralement de meilleure qualité que les simulations sans assimilation de données et que les données satellitaires elles-mêmes. L'objectif principal de ce travail de thèse a été d'améliorer la représentation des variables des surfaces terrestres reliées aux cycles de l'eau et du carbone dans le modèle ISBA grâce à l'assimilation d'observations de rétrodiffusion radar (sigma°) provenant de l'instrument ASCAT. Un opérateur d'observation capable de représenter les sigma° ASCAT à partir de variables simulées par le modèle ISBA a été développé. Une version du WCM (water cloud model) a été mise en œuvre avec succès sur la zone Euro-Méditerranée. Les valeurs simulées ont été comparées avec les observations satellitaires. Une quantification plus détaillée de l'impact de divers facteurs sur le signal a été faite sur le sud-ouest de la France. L'étude de l'impact de la tempête Klaus sur la forêt des Landes a montré que le WCM est capable de représenter un changement brutal de biomasse de la végétation. Le WCM est peu efficace sur les zones karstiques et sur les surfaces agricoles produisant du blé. Dans ce dernier cas, le problème semble provenir d'un décalage temporel entre l'épaisseur optique micro-ondes de la végétation et l'indice de surface foliaire de la végétation. Enfin, l'assimilation directe des sigma° ASCAT a été évaluée sur le sud-ouest de la France.More accurate estimates of land surface conditions are important for enhancing our ability to understand, monitor, and predict key variables of the terrestrial water cycle in various parts of the globe. In particular, the Mediterranean area is frequently characterized by a marked impact of the soil water deficit on vegetation growth. The latest IPCC (Intergovernmental Panel on Climate Change) simulations indicate that occurrence of droughts and warm spells in the Euro-Mediterranean region are likely to increase. It is therefore crucial to improve the ways of understanding, observing and simulating the dynamics of the land surface processes in the Euro-Mediterranean region. Land surface models (LSMs) have been developed for the purpose of representing the land surface processes at various spatial scales. They are usually forced by hourly gridded atmospheric variables such as air temperature, air humidity, solar radiation, precipitation, and are used to simulate land surface states and fluxes. While LSMs can provide a continuous monitoring of land surface conditions, they still show discrepancies due to forcing and parameter errors, missing processes and inadequate model physics for particular areas or seasons. It is also possible to observe the land surface conditions from space. The modelling of land surface variables can be improved through the dynamical integration of these observations into LSMs. Remote sensing observations are particularly useful in this context because they are able to address global and continental scales. Low frequency microwave remote sensing has advantages because it can provide regular observations in all-weather conditions and at either daytime or night-time. A number of satellite-derived products relevant to the hydrological and vegetation cycles are already available from C-band radars such as the Advanced Scatterometer (ASCAT) or Sentinel-1. Assimilating these data into LSMs permits their integration in the process representation in a consistent way. The results obtained from assimilating satellites products provide land surface variables estimates that are generally superior to the model estimates or satellite observations alone. The main objective of this thesis was to improve the representation of land surface variables linked to the terrestrial water and carbon cycles in the ISBA LSM through the assimilation of ASCAT backscatter (sigma°) observations. An observation operator capable of representing the ASCAT sigma° from the ISBA simulated variables was developed. A version of the water cloud model (WCM) was successfully implemented over the Euro-Mediterranean area. The simulated values were compared with those observed from space. A more detailed quantification of the influence of various factors on the signal was made over southwestern France. Focusing on the Klaus storm event in the Landes forest, it was shown that the WCM was able to represent abrupt changes in vegetation biomass. It was also found that the WCM had shortcomings over karstic areas and over wheat croplands. It was shown that the latter was related to a discrepancy between the seasonal cycle of microwave vegetation optical depth (VOD) and leaf area index (LAI). Finally, the direct assimilation of ASCAT sigma° observations was assessed over southwestern France

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions

Unmanned aerial vehicles (UAVs) equipped with spectral sensors have become useful in the fast and non-destructive assessment of crop growth, endurance and resource dynamics. This study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages. In addition, the raw image data with high ground resolution (20 cm) were resampled to 30, 50 and 100 cm to determine the influence of resolution on the estimation of phenotype variables by using vegetation indices (VIs). Quadratic polynomial regression was applied to the quantitative analysis at different resolutions and growth stages. The coefficient of determination (R2) and root mean square error results indicated the significant accuracy of the LAI estimation, wherein the highest R2 values were attained by RVI = 0.93 and MTVI2 = 0.89 at the elongation stage. The noise equivalent of sensitivity and uncertainty analyses at the different growth stages accounted for the sensitivity of VIs, which revealed the optimal VIs of RVI, MTVI2 and MSAVI in the LAI estimation. LMA and SLA, which showed significant accuracies at (R2 = 0.85, 0.81) and (R2 = 0.85, 0.71), were estimated on the basis of the predicted leaf dry weight and LAI at the elongation and flowering stages, respectively. No significant variations were observed in the measured regression coefficients using different resolution images. Results demonstrated the significant potential of UAV-onboard multispectral sensor and empirical method for the non-destructive retrieval of crop canopy variables

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done