Spatiotemporal analysis of gapfilled high spatial resolution time series for crop monitoring.

[ES] La obtención de mapas fiables de clasificación de cultivos es importante para muchas aplicaciones agrícolas, como el monitoreo de los campos y la seguridad alimentaria. Hoy en día existen distintas bases de datos de cobertura terrestre con diferentes escalas espaciales y temporales cubriendo diferentes regiones terrestres (por ejemplo, Corine Land cover (CORINE) en Europa o Cropland Data Layer (CDL) en Estados Unidos (EE.UU.)). Sin embargo, estas bases de datos son mapas históricos y por lo tanto no reflejan los estados fenológicos actuales de los cultivos. Normalmente estos mapas requieren un tiempo específico (anual) para generarse basándose en las diferentes fenologías de cada cultivo. Los objetivos de este trabajo son dos: 1- analizar la distribución espacial de los cultivos a diferentes regiones espaciales para identificar las áreas más representativas. 2- analizar el rango temporal utilizado para acelerar la generación de mapas de clasificación. El análisis se realiza sobre el contiguo de Estados Unidos (CONUS, de sus siglas en inglés) en 2019. Para abordar estos objetivos, se utilizan diferentes fuentes de datos. La capa CDL, una base de datos robusta y completa de mapas de cultivo en el CONUS, que proporciona datos anuales de cobertura terrestre rasterizados y georeferenciados. Así como, datos multiespectrales a 30 metros de resolución espacial, preprocesados para rellenar los posibles huecos debido a la presencia de nubes y aerosoles en los datos. Este conjunto de datos ha sido generado por la fusión de sensores Landsat y Moderate Resolution Imaging Spectroradiometer (MODIS). Para procesar tal elevada cantidad de datos se utilizará Google Earth Engine (GEE), que es una aplicación que procesa la información en la nube y está especializada en el procesamiento geoespacial. GEE se puede utilizar para obtener mapas de cultivos a nivel mundial, pero requiere algoritmos eficientes. En este estudio se analizarán diferentes algoritmos de aprendizaje de máquina (machine learning) para analizar la posible aceleración de la obtención de los mapas de clasificación de cultivo. En GEE hay diferentes tipos de algoritmos de clasificación disponibles, desde simples árboles de decisión (decision trees) hasta algoritmos más complejos como máquinas de vectores soporte (SVM) o redes neuronales (neural networks). Este estudio presentará los primeros resultados para la generación de mapas de clasificación de cultivos utilizando la menor cantidad posible de información, a nivel temporal, con una resolución espacial de 30 metros.[EN] Reliable crop classification maps are important for many agricultural applications, such as field monitoring and food security. Nowadays there are already several crop cover databases at different scales and temporal resolutions for different parts of the world (e. g. Corine Land cover in Europe (CORINE) or Cropland Data Layer (CDL) in the United States (US)). However, these databases are historical crop cover maps and hence do not reflect the actual crops on the ground. Usually, these maps require a specific time (annually) to be generated based on the diversity of the different crop phenologies. The aims of this work are two: 1- analyzing the multi-scale spatial crop distribution to identify the most representative areas. 2- analyzing the temporal range used to generate crop cover maps to build maps promptly. The analysis is done over the contiguous US (CONUS) in 2019. To address these objectives, different types of data are used. The CDL, a robust and complete cropland mapping in the CONUS, which provides annual land cover data raster geo-referenced. And, multispectral high-resolution gap-filled data at 30 meter spatial resolution used to avoid the presence of clouds and aerosols in the data. This dataset has been generated by the fusion of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). To process this large amount of data will be used Google Earth Engine (GEE) which is a cloud-based application specialized in geospatial processing. GEE can be used to map crops globally, but it requires efficient algorithms. In this study, different machine learning algorithms will be analyzed to generate the promptest classification crop maps. Several options are available in GEE from simple decision trees to more complex algorithms like support vector machines or neural networks. This study will present the first results and the potential to generate crop classification maps using as less possible temporal range information at 30 meters spatial resolution.Rajadel Lambistos, C. (2020). Análisis espaciotemporal de series temporales sin huecos de alta resolución espacial. Universitat Politècnica de València. http://hdl.handle.net/10251/155879TFG

Challenges and opportunities of using ecological and remote sensing variables for crop pest and disease mapping

Crop pest and diseases are responsible for major economic losses in the agricultural systems in Africa resulting in food insecurity. Potential yield losses for major crops across Africa are mainly caused by pests and diseases. Total losses have been estimated at 70% with approximately 30% caused by inefficient crop protection practices. With newly emerging crop pests and disease, monitoring plant health and detecting pathogens early is essential to reduce disease spread and to facilitate effective management practices. While many pest and diseases can be acquired from another host or via the environment, the majority are transmitted by biological vectors. Thus, vector ecology can serve an indirect explanation of disease cycles, outbreaks, and prevalence. Hence, better understanding of the vector niche and the dependence of pest and disease processes on their specific spatial and ecological contexts is therefore required for better management and control. While research in disease ecology has revealed important life history of hosts with the surrounding environment, other aspects need to be explored to better understand vector transmission and control strategies. For instance, choosing appropriate farming practices have proved to be an alternative to the use of synthetic pesticides. For instance, intercropping can serve as a buffer against the spread of plant pests and pathogens by attracting pests away from their host plant and also increasing the distance between plants of the same species, making it more exigent for the pest to target the main crop. Many studies have explored the potential applications of geospatial technology in disease ecology. However, pest and disease mapping in crops is rather crudely done thus far, using Spatial Distribution Models (SDM) on a regional scale. Previous research has explored climatic data to model habitat suitability and the distribution of different crop pests and diseases. However, there are limitation to using climate data since it ignores the dispersal and competition from other factors which determines the distribution of vectors transmitting the disease, thus resulting in model over prediction. For instance, vegetation patterns and heterogeneity at the landscape level has been identified to play a key role in influencing the vector-host-pathogen transmission, including vector distribution, abundance and diversity at large. Such variables can be extracted from remote sensing dataset with high accuracy over a large extent. The use of remotely sensed variables in modeling crop pest and disease has proved to increase the accuracy and precision of the models by reducing over fitting as compared to when only climatic data which are interpolated over large areas thus disregarding landscape heterogeneity.When used, remotely sensed predictors may capture subtle variances in the vegetation characteristic or in the phenology linked with the niche of the vector transmitting the disease which cannot be explained by climatic variables. Subsequently, the full potential of remote sensing applications to detect changes in habitat condition of species remains uncharted. This study aims at exploring the potential behind developing a framework which integrates both ecological and remotely sensed dataset with a robust mapping/modelling approach with aim of developing an integrated pest management approach for pest and disease affecting both annual and perrennial crops and whom currently there is no cure or existing germplasm to control further spread across sub Saharan Africa.Herausforderungen und Möglichkeiten der Verwendung von ökologischen und Fernerkundungsvariablen für die Schädlings- und Krankheitskartierung Pflanzenschädlinge und Krankheiten in der Landwirtschaft sind für große wirtschaftliche Verluste in Afrika verantwortlich, die zu Ernährungsunsicherheit führen. Die Verluste werden auf 70% geschätzt, wobei etwa 30% auf ineffiziente Pflanzenschutzpraktiken zurückzuführen sind. Bei neu auftretenden Pflanzenschädlingen und Krankheiten ist die Überwachung des Pflanzenzustands und die frühzeitige Erkennung von Krankheitserregern unerlässlich, um die Ausbreitung von Krankheiten zu reduzieren und effektive Managementpraktiken zu erleichtern. Während viele Schädlinge und Krankheiten von einem anderen Wirt oder über die Umwelt erworben werden können, wird die Mehrheit durch biologische Vektoren übertragen. Daraus folgt, dass die Vektorökologie als indirekte Erklärung von Krankheitszyklen, Ausbrüchen und Prävalenz untersucht werden sollte. Um effektive Vektorkontrollmaßnahmen zu entwickeln ist ein besseres Verständnis der ökologischen Vektor-Nischen und der Abhängigkeit von Schädlings- und Krankheits-Prozessen von ihrem spezifischen räumlichen und ökologischen Kontext wichtig. Während die Forschung in der Krankheitsökologie wichtige Lebenszyklen von Wirten mit der Umgebung schon gut aufgezeigt hat, müssen weitere Aspekte noch besser untersucht werden, um Vektorübertragungs- und Kontroll-Strategien zu entwickeln. So hat sich beispielsweise die Wahl geeigneter Anbaumethoden als Alternative zum Einsatz synthetischer Pestizide erwiesen. In einigen Fällen wurde der Zwischenfruchtanbau als ‚Puffer' gegen die Ausbreitung von Pflanzenschädlingen und Krankheitserregern vorgeschlagen. Bei diesem Anbausystem werden Schädlinge von ihrer Wirtspflanze abgezogen und auch der Abstand zwischen Pflanzen derselben Art vergrößert (was eine Übertragung erschwert). Viele Studien haben bereits die Einsatzmöglichkeiten von Geodaten in der Krankheitsökologie untersucht. Die Kartierung von Schädlingen und Krankheiten in Nutzpflanzen ist jedoch bisher eher großskalig erfolgt, unter der Zunahme von sogenannten ‚Spatial Distribution Models (SDM)' auf regionaler Ebene. Etliche Studien haben diesbezüglich klimatische Daten verwendet, um die Eignung und Verteilung verschiedener Pflanzenschädlinge und Krankheiten zu modellieren. Es gibt jedoch Einschränkungen bei der Verwendung von Klimadaten, da dabei andere landschaftsbezogene Verbreitungs-Faktoren ignoriert werden, die die Verteilung der Vektoren und Krankheitserreger bestimmen, was zu einer Modell-Überprognose führt. Vegetationsmuster und Heterogenität auf Landschaftsebene beeinflussen maßgeblich die Diversität und Verteilung eines Vektors und spielen somit eine wichtige Rolle bei der Vektor-Wirt-Pathogen-Übertragung. Bei der Verwendung von Fernerkundungsdaten können subtile Abweichungen in der Vegetationscharakteristik oder in der Phänologie, die mit der Nische des Vektors verbunden sind, besser erfasst werden. Es besteht noch Forschungs-Bedarf hinsichtlich der Rolle von Fernerkundungsdaten bei der Verbesserung von Artenmodellen, die zum Ziel haben den Lebensraum von Krankheitsvektoren besser zu erfassen. Ziel dieser Studie ist es, das Potenzial für die Entwicklung eines Rahmens zu untersuchen, der sowohl ökologische als auch aus der Ferne erfasste Daten mit einem robusten Mapping- / Modellierungsansatz kombiniert, um einen integrierten Ansatz zur Schädlingsbekämpfung für Schädlinge und Krankheiten zu entwickeln, der sowohl einjährige als auch mehrjährige Kulturpflanzen betrifft Keine Heilung oder vorhandenes Keimplasma zur weiteren Verbreitung in Afrika südlich der Sahara

Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems

Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems.Thesis (PhD) -- Faculty of Science and Agriculture, 202