Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal)

Madeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result.info:eu-repo/semantics/publishedVersio

Multiseasonal Remote Sensing of Vegetation with One-Class Classification – Possibilities and Limitations in Detecting Habitats of Nature Conservation Value

Mapping of habitats relevant for nature conservation often involves the identification of patches of target habitats in a complex mosaic of vegetation types extraneous for conservation planning. In field surveys, this is often a time-consuming and work-intensive task. Limiting the necessary ground reference to a small sample of target habitats and combining it with area-wide remote sensing data could greatly reduce and therefore support the field mapping effort. Conventional supervised classification methods need to be trained with a representative set of samples covering an exhaustive set of all classes. Acquiring such data is work intensive and hence inefficient in cases where only one or few classes are of interest. The usage of one-class classifiers (OCC) seems to be more suitable for this task – but has up until now neither been tested nor applied for large scale mapping and monitoring in programs such as those requested for the Natura 2000 European Habitat Directive or the High Nature Value (HNV) farmland Indicator. It is important to uncover the possibilities and mark the obstacles of this new approach since the usage of remote sensing for conservation purposes is currently controversially discussed in the ecology community as well as in the remote sensing community. Thus, the focal and innovative point of this thesis is to explore possibilities and limitations in the application of one-class classifiers for detecting habitats of nature conservation value with the help of multi-seasonal remote sensing and limited field data. The first study ascertains the usage of an OCC is suitable for mapping Natura 2000 habitat types. Applying the Maxent algorithm in combination with a low number of ground reference points of four habitat types and easily available multi-seasonal satellite imagery resulted in a combined habitat map with reasonable accuracy. There is potential in one-class classification for detecting rare habitats – however, differentiating habitats with very similar species composition remains challenging. Motivated by these positive results, the topic of the second study of this thesis is whether low and HNV grasslands can be differentiated with remotely-sensed reflectance data, field data and one-class classification. This approach could supplement existing field survey-based monitoring approaches such as for the HNV farmland Indicator. Three one-class classifiers together with multi-seasonal, multispectral remote sensing data in combination with sparse field data were analysed for their usage A) to classify HNV grassland against other areas and B) to differentiate between three quality classes of HNV grassland according to the current German HNV monitoring approach. Results indicated reasonable performances of the OCC to identify HNV grassland areas, but clearly showed that a separation into several HNV quality classes is not possible. In the third study the robustness and weak spots of an OCC were tested considering the effect of landscape composition and sample size on accuracy measurements. For this purpose artificial landscapes were generated to avoid the common problem of case-studies which usually can only make locally valid statements on the suitability of a tested approach. Whereas results concerning target sample size and the amount of similar classes in the background confirm conclusions of earlier studies from the field of species distribution modelling, results for background sample size and prevalence of target class give new insights and a basis for further studies and discussions. In conclusion the utilisation of an OCC together with reflectance and sparse field data for addressing rare vegetation types of conservation interest proves to be useful and has to be recommended for further research

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

Object-based classification of grasslands from high resolution satellite image time series using gaussian mean map kernels

This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the a-Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands

Multi-scalar remote sensing of the northern mixed prairie vegetation

Optimal scale of study and scaling are fundamental to ecological research, and have been made easier with remotely sensed (RS) data. With access to RS data at multiple scales, it is important to identify how they compare and how effectively information at a specific scale will potentially transfer between scales. Therefore, my research compared the spatial, spectral, and temporal aspects of scale of RS data to study biophysical properties and spatio-temporal dynamics of the northern mixed prairie vegetation. I collected ground cover, dominant species, aboveground biomass, and leaf area index (LAI) from 41 sites and along 3 transects in the West Block of Grasslands National Park of Canada (GNPC; +49°, -107°) between June-July of 2006 and 2007. Narrowband (VIn) and broadband vegetation indices (VIb) were derived from RS data at multiple scales acquired through field spectroradiometry (1 m) and satellite imagery (10, 20, 30 m). VIs were upscaled from their native scales to coarser scales for spatial comparison, and time-series imagery at ~5-year intervals was used for temporal comparison. Results showed VIn, VIb, and LAI captured the spatial variation of plant biophysical properties along topographical gradients and their spatial scales ranged from 35-200 m. Among the scales compared, RS data at finer scales showed stronger ability than coarser scales to estimate ground vegetation. VIn were found to be better predictors than VIb in estimating LAI. Upscaling at all spatial scales showed similar weakening trends for LAI prediction using VIb, however spatial regression methods were necessary to minimize spatial effects in the RS data sets and to improve the prediction results. Multiple endmember spectral mixture analysis (MESMA) successfully captured the spatial heterogeneity of vegetation and effective modeling of sub-pixel spectral variability to produce improved vegetation maps. However, the efficiency of spectral unmixing was found to be highly dependent on the identification of optimal type and number of region-specific endmembers, and comparison of spectral unmixing on imagery at different scales showed spectral resolution to be important over spatial resolution. With the development of a comprehensive endmember library, MESMA may be used as a standard tool for identifying spatio-temporal changes in time-series imagery. Climatic variables were found to affect the success of unmixing, with lower success for years of climatic extremes. Change-detection analysis showed the success of biodiversity conservation practices of GNPC since establishment of the park and suggests that its management strategies are effective in maintaining vegetation heterogeneity in the region. Overall, my research has advanced the understanding of RS of the northern mixed prairie vegetation, especially in the context of effects of scale and scaling. From an eco-management perspective, this research has provided cost- and time-effective methods for vegetation mapping and monitoring. Data and techniques tested in this study will be even more useful with hyperspectral imagery should they become available for the northern mixed prairie

Geomatics in support of the Common Agriculture Policy

The 2009 Annual Conference was the 15th organised by GeoCAP action of the Joint Research Centre in ISPRA. It was jointly organised with the Italian Agenzia per le erogazioni in agricoltura (AGEA, coordinating organism of the Italian agricultural paying agencies). The Conference covered the 2009 Control with Remote sensing campaign activities and ortho-imagery use in all the CAP management and control procedures. There has been a specific focus on the Land Parcel Identification Systems quality assessment process. The conference was structured over three days ¿ 18th to 20th November. The first day was mainly dedicated to future Common Agriculture Policy perspectives and futures challenges in Agriculture. The second was shared in technical parallel sessions addressing topics like: LPIS Quality Assurance and geodatabases features; new sensors, new software, and their use within the CAP; and Good Agriculture and Environmental Conditions (GAEC) control methods and implementing measures. The last day was dedicated to the review of the 2009 CwRS campaign and the preparation of the 2010 one. The presentations were made available on line, and this publication represents the best presentations judged worthy of inclusion in a conference proceedings aimed at recording the state of the art of technology and practice of that time.JRC.DG.G.3-Monitoring agricultural resource

Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal informatio