Crop delineation using hybrid classification procedures: a case study in Scott, Saskatchewan

Non-Peer ReviewedCurrently, crop insurance companies rarely work in co-operation with remote sensing scientists as they believe that the data quality and resolution are too low to accurately delineate crop areas and predict yields. This is due to the cost of high spatial and temporal resolution data, which generally exceeds that of sending a field team to randomly inspect cropped areas. However, methods have been initiated recently, that increase the classification accuracy of medium resolution and coarse resolution data. In this study, SPOT-4 20 m resolution images for June, July and August were provided by Agriculture Financial Services Corporation (AFSC), Alberta for the area of Scott, Saskatchewan to ascertain the classification accuracy of current methodology and evaluate the possible applications of remote sensing data. Results show that hybrid classification and using normalized difference vegetation index (NDVI) are able to produce 85% classification accuracy for a three image multi-temporal stack. Using the normalized moisture difference index with the mid-infrared band for the August image resulted in 90% classification accuracy, although average per-crop-classifications were low. The best classification result was a July-August standard multi-image stack using hybrid classification (green, red, NIR-NDVI ISODATA for each image and the near-infrared band), offering higher per-crop classification accuracy than for any single image classification. The accuracy changes little with adding the June scene to the July/August multi-image stack

Compiling a land audit in large rural areas: Results from the methodology applied in the non-urban areas of the Matzikama municipal area

To compile a comprehensive land audit in large, mainly rural-based municipalities such as the Matzikama Municipality in the Western Cape warrants an alternative methodology than that conventionally done through exhaustive property visits. This study attempts to showcase such an alternative methodology to compile the land audit for the municipality. The end result of the audit was a geographical information system (GIS) database that contains a wide variety of information required for spatial planning and land use management purposes. Each of these elements required a unique data-collection methodology that included spatial data collection; aerial photography and satellite image pre-processing; mapping of property boundaries; defining area of interest; determining land ownership through property valuation rolls; establishing the status of access roads and routes; mapping current land uses, and overlaying land use control measures in order to infer land uses and deriving potential land use zoning. The methodology applied succeeded in successfully linking land parcels as follows: valuation data: 3 731 out of 4 176 (89.3%) were linked; state land audit: 378 out of 4 176 (9.1%) were linked, and deeds data: 1 680 out of 4 176 (40.2%) were linked. The study found that creating and updating land audits require advanced skills in GIS and it is recommended that municipalities employ suitably qualified officials in this regard. Working with outdated planning scheme legislation/policy can become a time-consuming and costly exercise for municipalities

Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images

This paper presents a semantic edge-aware multi-task neural network (SEANet) to obtain closed boundaries when delineating agricultural parcels from remote sensing images. It derives closed boundaries from remote sensing images and improves conventional semantic segmentation methods for the extraction of small and irregular agricultural parcels. SEANet integrates three correlated tasks: mask prediction, edge prediction, and distance map estimation. Related features learned from these tasks improve the generalizability of the network. We regard boundary extraction as an edge detection task and extract rich semantic edge features at multiple levels to improve the geometric accuracy of parcel delineation. Moreover, we develop a new multi-task loss that considers the uncertainty of different tasks. We conducted experiments on three high-resolution Gaofen-2 images in Shandong, Xinjiang, and Sichuan provinces, China, and on two medium-resolution Sentinel-2 images from Denmark and the Netherlands. Results showed that our method produced a better layout of agricultural parcels, with higher attribute and geometric accuracy than the existing ResUNet, ResUNet-a, R2UNet, and BsiNet methods on the Shandong and Denmark datasets. The total extraction errors of the parcels produced by our method were 0.214, 0.127, 0.176, 0.211, and 0.184 for the five datasets, respectively. Our method also obtains closed boundaries by one single segmentation, leading to superiority as compared with existing multi-task networks. We showed that it could be applied to images with different spatial resolutions for parcel delineation. Finally, our method trained on the Xinjiang dataset could be successfully transferred to the Shandong dataset with different dates and landscapes. Similarly, we obtained satisfactory results when transferring from the Denmark dataset to the Netherlands dataset. We conclude that SEANet is an accurate, robust, and transferable method for various areas and different remote sensing images. The codes of our model are available at https://github.com/long123524/SEANet_torch.</p

Exploitation des images satellitaires Modis-Terra pour la caractérisation des états de surface : cas de la Tunisie

Bien que de nombreuses incertitudes demeurent sur la rapidité, l'amplitude et la répartition géographique du changement climatique, sa réalité fait aujourd'hui consensus au sein de la communauté scientifique, et l'occurrence des sécheresses et des dégradations du couvert végétal et des zones humides dans tous les continents soulignent l'importance de ce phénomène. Les pays de l'Afrique du nord, et la Tunisie en particulier, sont parmi les régions les plus vulnérables à cause de leurs situations géographiques particulières limitées par le Sahara au sud et la mer au nord. Dans ce contexte de changement global, le suivi spatio-temporel de l'état de surface en Tunisie permettra de comprendre l'étendue, l'amplitude et le déroulement de ce phénomène dans la région. Les images satellitaires hebdomadaires de MODIS-Terra épurées des effets atmosphériques, des nuages et de leur ombre et ayant de bonnes résolutions temporelle et radiométrique sont un bon outil pour le suivi temporel de l'état de surface. Ainsi, des méthodes de classification non supervisée (ISODATA) et supervisée (Maximum de vraisemblance et Fuzzy) sont utilisées pour les classifier. Elles aboutissent à des séries temporelles traduisant l'évolution des surfaces occupées par les sols secs, les sols humides, la végétation et les plans d'eau de 2000 à 2009 ainsi qu'à la détection de leur changement. L'analyse spectrale et le filtrage numérique ont servi pour montrer que l'évolution temporelle de ces quatre classes est à la base annuelle, et qu'elle est liée à la pluviométrie. Cependant, une variabilité à grande échelle, à l'ordre de 8-9 ans, peut être mise en question à cause de sa faible puissance dans les séries temporelles de 10 ans obtenues

a Berlin case study

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt...thesi

Agricultural land use and associated nutrient flows in peri-urban production systems

Urban and peri-urban agriculture could play a pivotal role as a recipient of organic waste. But recycling and reuse of solid and liquid organic waste in peri-urban agriculture requires planning tools flexible enough to capture the diversity of farming systems and to assess their nutrient status over spatial and temporal scales. This work aims at developing a methodology to determine nutrient flows and budgets at farm, village and communal level of peri-urban agricultural systems of Hanoi, Vietnam, by taking into account spatial and temporal variability of crop and nutrient manageme

A methodology to produce geographical information for land planning using very-high resolution images

Actualmente, os municípios são obrigados a produzir, no âmbito da elaboração dos instrumentos de gestão territorial, cartografia homologada pela autoridade nacional. O Plano Director Municipal (PDM) tem um período de vigência de 10 anos. Porém, no que diz respeito à cartografia para estes planos, principalmente em municípios onde a pressão urbanística é elevada, esta periodicidade não é compatível com a dinâmica de alteração de uso do solo. Emerge assim, a necessidade de um processo de produção mais eficaz, que permita a obtenção de uma nova cartografia de base e temática mais frequentemente. Em Portugal recorre-se à fotografia aérea como informação de base para a produção de cartografia de grande escala. Por um lado, embora este suporte de informação resulte em mapas bastante rigorosos e detalhados, a sua produção têm custos muito elevados e consomem muito tempo. As imagens de satélite de muito alta-resolução espacial podem constituir uma alternativa, mas sem substituir as fotografias aéreas na produção de cartografia temática, a grande escala. O tema da tese trata assim da satisfação das necessidades municipais em informação geográfica actualizada. Para melhor conhecer o valor e utilidade desta informação, realizou-se um inquérito aos municípios Portugueses. Este passo foi essencial para avaliar a pertinência e a utilidade da introdução de imagens de satélite de muito alta-resolução espacial na cadeia de procedimentos de actualização de alguns temas, quer na cartografia de base quer na cartografia temática. A abordagem proposta para solução do problema identificado baseia-se no uso de imagens de satélite e outros dados digitais em ambiente de Sistemas de Informação Geográfica. A experimentação teve como objectivo a extracção automática de elementos de interesse municipal a partir de imagens de muito alta-resolução espacial (fotografias aéreas ortorectificadas, imagem QuickBird, e imagem IKONOS), bem como de dados altimétricos (dados LiDAR). Avaliaram-se as potencialidades da informação geográfica extraídas das imagens para fins cartográficos e analíticos. Desenvolveram-se quatro casos de estudo que reflectem diferentes usos para os dados geográficos a nível municipal, e que traduzem aplicações com exigências diferentes. No primeiro caso de estudo, propõe-se uma metodologia para actualização periódica de cartografia a grande escala, que faz uso de fotografias aéreas vi ortorectificadas na área da Alta de Lisboa. Esta é uma aplicação quantitativa onde as qualidades posicionais e geométricas dos elementos extraídos são mais exigentes. No segundo caso de estudo, criou-se um sistema de alarme para áreas potencialmente alteradas, com recurso a uma imagem QuickBird e dados LiDAR, no Bairro da Madre de Deus, com objectivo de auxiliar a actualização de cartografia de grande escala. No terceiro caso de estudo avaliou-se o potencial solar de topos de edifícios nas Avenidas Novas, com recurso a dados LiDAR. No quarto caso de estudo, propõe-se uma série de indicadores municipais de monitorização territorial, obtidos pelo processamento de uma imagem IKONOS que cobre toda a área do concelho de Lisboa. Esta é uma aplicação com fins analíticos onde a qualidade temática da extracção é mais relevante.Currently, the Portuguese municipalities are required to produce homologated cartography, under the Territorial Management Instruments framework. The Municipal Master Plan (PDM) has to be revised every 10 years, as well as the topographic and thematic maps that describe the municipal territory. However, this period is inadequate for representing counties where urban pressure is high, and where the changes in the land use are very dynamic. Consequently, emerges the need for a more efficient mapping process, allowing obtaining recent geographic information more often. Several countries, including Portugal, continue to use aerial photography for large-scale mapping. Although this data enables highly accurate maps, its acquisition and visual interpretation are very costly and time consuming. Very-High Resolution (VHR) satellite imagery can be an alternative data source, without replacing the aerial images, for producing large-scale thematic cartography. The focus of the thesis is the demand for updated geographic information in the land planning process. To better understand the value and usefulness of this information, a survey of all Portuguese municipalities was carried out. This step was essential for assessing the relevance and usefulness of the introduction of VHR satellite imagery in the chain of procedures for updating land information. The proposed methodology is based on the use of VHR satellite imagery, and other digital data, in a Geographic Information Systems (GIS) environment. Different algorithms for feature extraction that take into account the variation in texture, color and shape of objects in the image, were tested. The trials aimed for automatic extraction of features of municipal interest, based on aerial and satellite high-resolution (orthophotos, QuickBird and IKONOS imagery) as well as elevation data (altimetric information and LiDAR data). To evaluate the potential of geographic information extracted from VHR images, two areas of application were identified: mapping and analytical purposes. Four case studies that reflect different uses of geographic data at the municipal level, with different accuracy requirements, were considered. The first case study presents a methodology for periodic updating of large-scale maps based on orthophotos, in the area of Alta de Lisboa. This is a situation where the positional and geometric accuracy of the extracted information are more demanding, since technical mapping standards must be complied. In the second case study, an alarm system that indicates the location of potential changes in building areas, using a QuickBird image and LiDAR data, was developed for the area of Bairro da Madre de Deus. The goal of the system is to assist the updating of large scale mapping, providing a layer that can be used by the municipal technicians as the basis for manual editing. In the third case study, the analysis of the most suitable roof-tops for installing solar systems, using LiDAR data, was performed in the area of Avenidas Novas. A set of urban environment indicators obtained from VHR imagery is presented. The concept is demonstrated for the entire city of Lisbon, through IKONOS imagery processing. In this analytical application, the positional quality issue of extraction is less relevant.GEOSAT – Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images (PTDC/GEO/64826/2006), e-GEO – Centro de Estudos de Geografia e Planeamento Regional, da Faculdade de Ciências Sociais e Humanas, no quadro do Grupo de Investigação Modelação Geográfica, Cidades e Ordenamento do Territóri

Landnutzungsdynamiken und deren ökologische Auswirkungen auf Teneriffa (Kanarische Inseln). Analyse und Bewertung landwirtschaftlicher Entwicklungsprozesse mit Methoden der Fernerkundung und Landnutzungsmodellierung

Das heutige Landschaftsbild der Kanarischen Insel Teneriffa ist das Ergebnis einer beständigen kulturlandschaftlichen Entwicklung, die im Wesentlichen mit dem Beginn der europäischen Kolonialisierung im 15. Jahrhundert ihren Anfang nahm. Während bis Mitte des 20. Jahrhunderts fast ausschließlich landwirtschaftliche Inwertsetzungsprozesse die Landschaft prägten, spielen in jüngster Zeit vor allem der Tourismus und die damit verbundenen Wirtschaftssektoren eine maßgebliche Rolle bei der anthropogenen Umgestaltung des Raums. Der damit einhergehende sozioökonomische Wandel von einer Agrar- zu einer Dienstleistungsgesellschaft führt zu einer Umorientierung der Erwerbstätigen von der Landwirtschaft hin zum Tourismus und zu ansteigenden Migrationsbewegungen in die urbanen Tourismuszentren. Hierdurch kommt es einerseits zu enormen Siedlungsexpansionen in den Küstenzonen und andererseits zu einer verstärkten Aufgabe von landwirtschaftlichen Nutzflächen im ländlichen Raum. Im Rahmen der vorliegenden Arbeit wird eine umfassende Analyse, Simulation und ökologische Bewertung der agrarischen Landnutzungsdynamiken auf Teneriffa durchgeführt. Die Ergebnisse liefern ein ganzheitliches Bild zur bisherigen sowie möglichen zukünftigen räumlichen Entwicklung der kanarischen Landwirtschaft und den damit verbundenen ökologischen Auswirkungen auf den teilweise stark fragmentierten Naturraum. Ausgangspunkt der Untersuchung bildet die objektbasierte Landnutzungs- und Landbedeckungsklassifikation (LULC-Klassifikation) von SPOT 1-Daten (1986/88), SPOT 4-Daten (1998) sowie RapidEye-Daten (2010) und die anschließende Change Detection-Analyse in Form eines modifizierten, halbautomatisierten Post-Klassifikations-Vergleichs. Ein weiterer objektbasierter Klassifikationsprozess für hochauflösende RGB-Orthophotos dient darüber hinaus zur Erfassung der landwirtschaftlich beeinflussten Gesamtfläche Teneriffas. Hauptaugenmerk dieses Verfahrens liegt auf der texturbasierten Detektion von Agrarflächen inklusive landwirtschaftlich stillgelegter Areale bzw. Dauerbrachen, die in den Multispektraldaten aufgrund fortgeschrittener Sukzessionsprozesse nicht mehr von der natürlichen oder naturnahen Landbedeckung unterschieden werden können. Die Klassifikationsergebnisse münden anschließend in den Aufbau eines auf Dyna-CLUE 2 (Dynamic Conversion of Land Use and its Effects Model, Version 2) basierenden, räumlich expliziten Landnutzungsmodells, das nach einer Parametrisierung und Kalibrierung zur Simulation der möglichen zukünftigen Entwicklung des Agrarsektors bis 2030 herangezogen werden kann. Ein Trendszenario zeigt in diesem Zusammenhang auf, welche agrarischen Landnutzungsveränderungen auftreten, wenn sich der bisherige Trend mit einer Steigerung der Intensivlandwirtschaft und einer weiteren räumlichen Abnahme von Ackerflächen vor allem in den Peripheriegebieten fortsetzt. Ein zweites, alternatives Szenario prognostiziert hingegen, welche landwirtschaftlichen Veränderungen durch eine erfolgreiche Umsetzung von Agrarprogrammen und -maßnahmen der EU zu erwarten sind. Im Rahmen einer ökologischen Analyse wird schließlich eruiert, welche Areale des Lorbeer- und Kiefernwaldes sowie des Sukkulentenbuschs in der Vergangenheit unter landwirtschaftlichem Einfluss standen und somit als Regenerationsflächen gelten. Darüber hinaus wird ermittelt, wie sich das zukünftige Regenerationspotenzial für die einzelnen Vegetationsformationen unter Berücksichtigung der landwirtschaftlichen Entwicklungsszenarien darstellt. Nach einer Bewertung und Interpretation der gewonnenen Ergebnisse werden abschließend raumplanerische Vorschläge unterbreitet, wie der kanarische Lorbeerwald zukünftig stärker vor kulturlandschaftlichen Raumentwicklungsprozessen geschützt werden könnte