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

Automatic detection of uprooted orchards based on orthophoto texture analysis

Permanent crops, such as olive groves, vineyards and fruit trees, are important in European agriculture because of their spatial and economic relevance. Agricultural geographical databases (AGDBs) are commonly used by public bodies to gain knowledge of the extension covered by these crops and to manage related agricultural subsidies and inspections. However, the updating of these databases is mostly based on photointerpretation, and thus keeping this information up-to-date is very costly in terms of time and money. This paper describes a methodology for automatic detection of uprooted orchards (parcels where fruit trees have been eliminated) based on the textural classification of orthophotos with a spatial resolution of 0.25 m. The textural features used for this classification were derived from the grey level co-occurrence matrix (GLCM) and wavelet transform, and were selected through principal components (PCA) and separability analyses. Next, a Discriminant Analysis classification algorithm was used to detect uprooted orchards. Entropy, contrast and correlation were found to be the most informative textural features obtained from the co-occurrence matrix. The minimum and standard deviation in plane 3 were the selected features based on wavelet transform. The classification based on these features achieved a true positive rate (TPR) of over 80% and an accuracy (A) of over 88%. As a result, this methodology enabled reducing the number of fields to photointerpret by 60–85%, depending on the membership threshold value selected. The proposed approach could be easily adopted by different stakeholders and could increase significantly the efficiency of agricultural database updating tasks.This study was funded by the Spanish National Institute for Agricultural and Food Research and Technology (INIA) through its training program for researchers

Detection of Tree Crowns in Very High Spatial Resolution Images

The requirements for advanced knowledge on forest resources have led researchers to develop efficient methods to provide detailed information about trees. Since 1999, orbital remote sensing has been providing very high resolution (VHR) image data. The new generation of satellite allows individual tree crowns to be visually identifiable. The increase in spatial resolution has also had a profound effect in image processing techniques and has motivated the development of new object-based procedures to extract information. Tree crown detection has become a major area of research in image analysis considering the complex nature of trees in an uncontrolled environment. This chapter is subdivided into two parts. Part I offers an overview of the state of the art in computer detection of individual tree crowns in VHR images. Part II presents a new hybrid approach developed by the authors that integrates geometrical-optical modeling (GOM), marked point processes (MPP), and template matching (TM) to individually detect tree crowns in VHR images. The method is presented for two different applications: isolated tree detection in an urban environment and automatic tree counting in orchards with an average performance rate of 82% for tree detection and above 90% for tree counting in orchards

Machine learning and high spatial resolution multi-temporal Sentinel-2 imagery for crop type classification

Thesis (MPhil)--Stellenbosch University, 2019.ENGLISH SUMMARY : Spatially-explicit crop type information is useful for estimating agricultural production areas. Such information is used for various monitoring and decision-making applications, including crop insurance, food supply-demand logistics, commodity market forecasting and environmental modelling. Traditional methods, such as ground surveys and agricultural censuses, involve high production costs and are often labour intensive, which limit their use for timely and accurate crop type data production. Remote sensing, however, offers a dependable, cost-effective and timely way of mapping crop types. Although remote sensing approaches – particularly using multitemporal techniques – have been successfully employed for producing crop type information, this information is mostly available post-harvest. Thus, researchers and decision-makers have to wait several months after harvest to have such information, which is usually too late for many applications. The availability and accessibility of imagery collected with optical sensors make such data preferable for mapping crop types. However, these sensors are subject to cloud-interference, which has been recognised as a source of error in the retrieval of surface parameters. It is therefore important to assess the strengths and weaknesses of using multi-temporal optical imagery for differentiating crop types. This study utilises Sentinel-2A and 2B imagery to perform several experiments in selected parts of the Western Cape, South Africa, to undertake this assessment. The first three experiments assessed the significance of image selection on the accuracies of crop type classification. A recommended number of Sentinel-2 images was selected, using two different methods. The first of the three experiments was conducted with uni-temporal images. Based on the performance rankings of the uni-temporal images, five images with the highest ranks were used to set up Experiment 2. The third experiment was undertaken with a handpicked set of five images, based on crop developmental stages. The two image selection methods were compared to each other and subsequently to the entire time-series, to determine the significance of selecting images for crop type mapping. These classifications were undertaken with several supervised machine learning classifiers and one parametric classifier. Results showed no significant difference in classification accuracies between the two image selection methods and the entire time-series. Overall, the support vector machine (SVM) and random forest (RF) algorithms outperformed all the other classifiers. The fourth experiment was undertaken by chronologically adding images to the classifiers. The progression of classification accuracies against time and the increase in the number of images were analysed to determine the earliest period (pre-harvest) when crops can be classified with sufficient accuracies. The highest pre-harvest accuracy achieved was then compared to that obtained at the end of the season, including images acquired post-harvest, to assess the effectiveness of machine learning classifiers for classifying crop types when only pre-harvest images are used. The results of this experiment showed that machine learning classifiers can classify crops when only preharvest images are used, with accuracies similar to those obtained when the entire time-series is used. Satisfactory classification accuracies were attainable as early as Aug/Sept (eight weeks before harvest). The fifth to tenth experiments were undertaken to assess the impact of cloud cover and image compositing on crop type classification accuracies. The fifth and sixth experiments were performed with non-composited images. Experiment Five (5) was undertaken with cloud-free images only, while the sixth experiment involved using all available images, including cloudcontaminated observations. The seventh to tenth experiments were undertaken with monthly image composites computed using four different image compositing approaches. All these experiments were undertaken using several machine learning classifiers. The results showed that machine learning classifiers performed best when all images – including cloud-contaminated images – are used as input to the classifiers. Image compositing had a detrimental effect on classification accuracies. Generally, multi-temporal Sentinel-2 data hold great potential for operational crop type map production early in the season. However, more work is needed to develop simple workflows for eliminating cloud cover, particularly for crop type mapping in areas characterised by frequent overcast conditions.AFRIKAANSE OPSOMMING : Eksperiment 2 op te stel. Die derde eksperiment is gedoen met ’n uitgesoekte stel van vyf beelde, gebaseer op stadiums van gewasontwikkeling. Die twee beeldseleksiemetodes is met mekaar vergelyk en gevolglik met die hele tydreeks, om te bepaal wat die betekenis daarvan is om beelde te kies vir gewastipe-kartering. Hierdie klassifikasies is onderneem met verskeie masjienlerende klassifiseerders en een parametriese klassifiseerder, onder toesig. Resultate het geen beduidende verskil in klassifikasie-akkuraathede gewys tussen die twee beeldseleksiemetodes en die algehele tydreeks nie. In die geheel het die steunvektormasjien- (SVM) en lukrake-woud- (“random forest”, RF) -algoritmes beter presteer as al die ander klassifiseerders. Die vierde eksperiment is onderneem deur beelde chronologies by die klassifiseerders te voeg. Die progressie van klassifikasie-akkuraathede teenoor tyd en die toename in die aantal beelde is geanaliseer om die vroegste periode (voor-oes) te bepaal wanneer gewasse met voldoende akkuraathede geklassifiseer kan word. Die hoogste voor-oes-akkuraatheid is toe vergelyk met dit wat teen die end van die seisoen behaal is, insluitend beelde wat na-oes ingesamel is, om die doeltreffendheid van masjienlerende klassifiseerders te bepaal by die klassifisering van gewastipes wanneer slegs voor-oes-beelde gebruik is. Die resultate van hierdie eksperiment het gewys dat masjienlerende klassifiseerders gewasse kan klassifiseer wanneer slegs voor-oes-beelde gebruik is, met akkuraathede wat soortgelyk is aan dit wat behaal is wanneer die hele tydreeks gebruik is. Bevredigende klassifikasie-akkuraathede is so vroeg as Aug/Sep behaal (agt weke voor oes). Die vyfde tot tiende eksperimente is onderneem om die impak van wolkbedekking en beeldsamestelling op klassifikasie-akkuraathede van gewastipes te bepaal. Die vyfde en sesde eksperimente is met nie-saamgestelde beelde uitgevoer. Eksperiment Vyf (5) is slegs met wolkvrye beelde gedoen, terwyl die sesde eksperiment die gebruik van alle beskikbare beelde, insluitend wolkgekontamineerde observasies, betrek het. Die sewende tot tiende eksperimente is onderneem met maandelikse beeldsamestellings wat bereken is deur middel van die gebruik van vier verskillende benaderings tot beeldsamestelling. Al hierdie eksperimente is met behulp van verskeie masjienlerende klassifiseerders uitgevoer. Die resultate het gewys dat masjienlerende klassifiseerders die beste presteer het wanneer alle beelde – insluitend wolkgekontamineerde beelde – as invoer aan die klassifiseerders gebruik word. Beeldsamestelling het ’n nadelige uitwerking op klassifikasie-akkuraathede gehad. Oor die algemeen het multitemporale Sentinel-2-data vroeg in die seisoen goeie potensiaal vir operasionele gewastipe-kaartproduksie. Meer werk is nietemin nodig om eenvoudige werkvloei te ontwikkel om wolkbedekking te elimineer, veral vir gewastipe-kartering in areas wat gereeld gekenmerk word deur oortrokke toestande.Master

Deep learning for land cover and land use classification

Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications

Origins of sedentism: possible roles of ideology and shamanism in the transition

Recognising causal links between religious practices and socio-political structures, it is argued that the transition to settled life during the Neolithic was the product of social and political changes brought about by the institutionalisation and manipulation of ideology. These were employed by ambitious, influential individuals using sedentism as a strategy to achieve social control and the power, status and appropriated wealth (labour and resources) this engendered. A key factor in this was the materialisation of ideology, making visible the supernatural. Exploration of the ideopolitical nature of cultural elements — social, economic, and political — integral to the transition among Southwest Asian societies who experienced the profound changes involved, identified a nexus between increasing intensity of shamanistically manipulated ideology and progressive decrease in mobility. Furthermore, it reinforced the pivotal role played by shamanism in the transitional process, and that it was facilitated and maintained by the generation of ongoing socio-ideological stress. Emergence of personal and group individualism during the transition, but particularly in the latter part, saw competition in both hierarchical and heterarchical contexts for social control. In the course of this, shamanism was also employed by other influential individuals and became hybridised in the form of the quasi-divine shaman-priest-leaders operating ceremonial centres from which they dominated the activities of regional populations. A model derived from the archaeology of selected sites in Southwest Asia is presented that views the transition as a three-phase process reflecting the emergence and progressive intensification of a collective psychology, this manifest in new ideology, the growing importance of ‘place’, and individualism and social complexity not previously experienced. Also apparent is that initiation of the transition was associated with a new ideology and driven by shamanism, with the influence of the various agents involved becoming increasingly evident in a range of interrelated behavioural trends and developments. Each phase of the model sees ideology taken intentionally and necessarily to a higher level of intensity, providing a longer-term perspective on the relationship between ideology and economy. Evidence from the British Isles 5000-2000 calBC used for model validation confirmed that where ideology is evident in the archaeological record shamanism was influential, and emphasised the ideological context of the settlement foci and controlling agencies. Behavioural trends become more developed throughout, despite site context and location. While variation was apparent among the subregions in the extent to which a more settled way of life achieved, the overall effect in each was to bring dispersed communities together long-term, ideopolitically controlled in geographically confined contexts by site or wider location. People were being aggregated more regularly and co-operatively; this clearly facilitated by ideology. The British evidence also indicated that settled life did not necessarily equate precisely with the criteria of settled life, i.e., living permanently in durable structures on one site; rather, there was flexibility in the way these might be exhibited. Furthermore, full-time sedentism was shown to be preceded by permanent ceremonial structures and their ideological context

Semantic location extraction from crowdsourced data

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction

Nontimber Forest Products in the United States

Eric T. Jones is an instructor and research professor in the Department of Forest Ecosystems and Society at Oregon State University. Rebecca J. McLain is director of research at the National Policy Consensus Center at Portland State University. Susan Charnley is a research social scientist at the Pacific Northwest Research Station of the USDA Forest Service. James Weigand is an ecologist at the US Department of the Interior, Bureau of Land Management. With a New Preface by Eric T. Jones, Rebecca J. McLain, Susan Charnley, and James Weigand.This Kansas Open Books title is funded by a grant from the National Endowment for the Humanities and the Andrew W. Mellon Foundation Humanities Open Book Program.A quiet revolution is taking place in America's forests. Once seen primarily as stands of timber, our woodlands are now prized as a rich source of a wide range of commodities, from wild mushrooms and maple sugar to hundreds of medicinal plants whose uses have only begun to be fully realized. Now as timber harvesting becomes more mechanized and requires less labor, the image of the lumberjack is being replaced by that of the forager. This book provides the first comprehensive examination of nontimber forest products (NTFPs) in the United States, illustrating their diverse importance, describing the people who harvest them, and outlining the steps that are being taken to ensure access to them. As the first extensive national overview of NTFP policy and management specific to the United States, it brings together research from numerous disciplines and analytical perspectives-such as economics, mycology, history, ecology, law, entomology, forestry, geography, and anthropology—in order to provide a cohesive picture of the current and potential role of NTFPs. The contributors review the state of scientific knowledge of NTFPs by offering a survey of commercial and noncommercial products, an overview of uses and users, and discussions of sustainable management issues associated with ecology, cultural traditions, forest policy, and commerce. They examine some of the major social, economic, and biological benefits of NTFPs, while also addressing the potential negative consequences of NTFP harvesting on forest ecosystems and on NTFP species populations. Within this wealth of information are rich accounts of NTFP use drawn from all parts of the American landscape—from the Pacific Northwest to the Caribbean. From honey production to a review of nontimber forest economies still active in the United States—such as the Ojibway "harvest of plants" recounted here—the book takes in the whole breadth of recent NTFP issues, including ecological concerns associated with the expansion of NTFP markets and NTFP tenure issues on federally managed lands. No other volume offers such a comprehensive overview of NTFPs in North America. By examining all aspects of these products, it contributes to the development of more sophisticated policy and management frameworks for not only ensuring their ongoing use but also protecting the future of our forests