519 research outputs found

    A novel approach for multispectral satellite image classification based on the bat algorithm

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    Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems

    A novel approach for multispectral satellite image classification based on the bat algorithm

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    Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems

    Machine Learning for Robust Understanding of Scene Materials in Hyperspectral Images

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    The major challenges in hyperspectral (HS) imaging and data analysis are expensive sensors, high dimensionality of the signal, limited ground truth, and spectral variability. This dissertation develops and analyzes machine learning based methods to address these problems. In the first part, we examine one of the most important HS data analysis tasks-vegetation parameter estimation. We present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited and/or spectral variability is high. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. The efficacy of the proposed methods is demonstrated by comparing them against state-of-the art approaches on three real-world HS datasets and one synthetic dataset. In the second part, we demonstrate how Bayesian optimization can be applied to jointly tune the different components of hyperspectral data analysis frameworks for better performance. Experimental validation on the spatial-spectral classification framework consisting of a classifier and a Markov random field is provided. In the third part, we investigate whether high dimensional HS spectra can be reconstructed from low dimensional multispectral (MS) signals, that can be obtained from much cheaper, lower spectral resolution sensors. A novel end-to-end convolutional residual neural network architecture is proposed that can simultaneously optimize both the MS bands and the transformation to reconstruct HS spectra from MS signals by analyzing a large quantity of HS data. The learned band can be implemented in sensor hardware and the learned transformation can be incorporated in the data processing pipeline to build a low-cost hyperspectral data collection system. Using a diverse set of real-world datasets, we show how the proposed approach of optimizing MS bands along with the transformation rather than just optimizing the transformation with fixed bands, as proposed by previous studies, can drastically increase the reconstruction accuracy. Additionally, we also investigate the prospects of using reconstructed HS spectra for land cover classification

    Landscape functional connectivity and animal movement: application of remote sensing for increasing efficiency of road mitigation measures

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    Roads are a major threat to wildlife due to induced mortality and restrictions to animal movement. A central issue in conservation biology is the accurate site identification for the implementation of multispecies mitigation measures, on roads. Those measures entail high costs and methodological challenges and their efficiency highly depend on the right location. The aim of this PhD is to inform, through remote sensing and connectivity modelling, how to increase the efficiency of planning mitigation measures to reduce roadkill and promote connectivity; and demonstrate the usefulness of remote sensing in defining suitable areas for the conservation of an endangered species that often occurs in the vicinity of roads. To do so, we first assessed whether occurrence-based strategies were able to infer functional connectivity, compared to those more complex and financially demanding based on telemetry, with respect to daily and dispersal movements. Secondly, we assessed whether remote sensing data were sufficiently informative to identify key habitats for a threatened species around road verges. Thirdly, we assessed the predictive and prioritisation ability of road mitigation units intercepting multispecies corridors to prevent vulnerability to roadkill. Findings revealed that simple models are suitable as complex ones for both daily and dispersal movements, allowing for costly-effective connectivity assessments. Results demonstrated the ability of free remote sensing data to identify microhabitat conditions in verges and surrounding landscape, for a threatened rodent, allowing for the delimitation of refugee areas and definition of monitoring strategies for the species. Undemanding data (occurrence and remote sensing) were able to describe species-specific ecological requirements for birds, bats and non-flying mammals as well as roadkill patterns, possibly due to similar overlapping corridors and habitats, despite some mismatches that occurred for highly mobile species. This framework ensured high efficiency in prioritisation of multispecies roadkill mitigation planning, resilient to long-term landscape dynamics; Conectividade funcional da paisagem e movimento animal: aplicação da detecção remota para aumentar a eficiência de medidas de mitigação em estradas. Resumo: As estradas constituem uma enorme ameaça para a vida selvagem devido à mortalidade. Uma questão central é a identificação dos locais para implementar medidas de mitigação multiespécies, em estradas. Essas medidas envolvem custos elevados e desafios metodológicos e sua eficiência depende muito da localização correcta. O objetivo deste doutoramento é informar, através de detecção remota e conectividade, como aumentar a eficiência do planeamento de medidas de mitigação para reduzir atropelamentos e promover a conectividade; e demonstrar a utilidade da detecção remota na definição de áreas adequadas para a conservação de espécies ameaçadas que podem ocorrer nas proximidades de estradas. Portanto, primeiro avaliamos se os dados resultantes de amostragens simples eram capazes de inferir conectividade funcional, em comparação com estratégias complexas, respeito aos movimentos diários e de dispersão. Segundo, avaliamos se os dados de detecção remota eram suficientemente informativos para identificar habitats-chave para uma espécie ameaçada em torno das margens das estradas. Terceiro, avaliamos a capacidade preditiva e de prioritização das unidades de mitigação de estradas que cruzam corredores multi-espécies para reduzir o risco de atropelamentos. Os resultados revelaram que os modelos simples são adequados quanto os complexos para os movimentos diários e de dispersão. Os resultados demonstraram a capacidade dos dados de detecção remota gratuitos em identificar condições de microhabitats nos habitats de berma e na paisagem circundante, para um roedor ameaçado, permitindo a delimitação de áreas de refúgio. Dados pouco exigentes (ocorrência e detecção remota) foram capazes de descrever os requisitos ecológicos específicos de aves, morcegos e mamíferos não voadores, bem como padrões de atropelamentos, possivelmente devido a corredores e habitats semelhantes, apesar de haver algumas incompatibilidades para espécies de maior mobilidade. Essa estrutura foi capaz de garantir uma elevada eficiência na prioritização de planeamento de mitigação de atropelamentos para multi-espécies, resiliente à dinâmica da paisagem de longo prazo

    Retrieval of maize leaf area index using hyperspectral and multispectral data

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    Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study areainfo:eu-repo/semantics/publishedVersio

    The future of Earth observation in hydrology

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    In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

    High-throughput phenotyping of yield parameters for modern grapevine breeding

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    Weinbau wird auf 1% der deutschen Agrarfläche betrieben. Auf dieser vergleichsweise kleinen Anbaufläche wird jedoch ein Drittel aller in der deutschen Landwirtschaft verwendeten Fungizide appliziert, was auf die Einführung von Schaderregern im 19. Jahrhundert zurück zu führen ist. Für einen nachhaltigen Anbau ist eine Reduktion des Pflanzenschutzmittelaufwands dringend notwendig. Dieses Ziel kann durch die Züchtung und den Anbau neuer, pilzwiderstandsfähiger Rebsorten erreicht werden. Die Rebenzüchtung als solche ist sehr zeitaufwendig, da die Entwicklung neuer Rebsorten 20 bis 25 Jahre dauert. Der Einsatz der markergestützten Selektion (MAS) erhöht die Effizienz der Selektion in der Rebenzüchtung fortwährend. Eine weitere Effizienzsteigerung ist mit der andauernden Verbesserung der Hochdurchsatz Genotypisierung zu erwarten. Im Vergleich zu den Methoden der Genotypisierung ist die Qualität, Objektivität und Präzision der traditionellen Phänotypisierungsmethoden begrenzt. Die Effizienz in der Rebenzüchtung soll mit der Entwicklung von Hochdurchsatz Methoden zur Phänotypisierung durch sensorgestützte Selektion weiter gesteigert werden. Hierfür sind bisher vielfältige Sensortechniken auf dem Markt verfügbar. Das Spektrum erstreckt sich von RGB-Kameras über Multispektral-, Hyperspektral-, Wärmebild- und Fluoreszenz- Kameras bis hin zu 3D-Techniken und Laserscananwendungen. Die Phänotypisierung von Pflanzen kann unter kontrollierten Bedingungen in Klimakammern oder Gewächshäusern beziehungsweise im Freiland stattfinden. Die Möglichkeit einer standardisierten Datenaufnahme nimmt jedoch kontinuierlich ab. Bei der Rebe als Dauerkultur erfolgt die Aufnahme äußerer Merkmale, mit Ausnahme junger Sämlinge, deshalb auch überwiegend im Freiland. Variierende Lichtverhältnisse, Ähnlichkeit von Vorder- und Hintergrund sowie Verdeckung des Merkmals stellen aus methodischer Sicht die wichtigsten Herausforderungen in der sensorgestützen Merkmalserfassung dar. Bis heute erfolgt die Aufnahme phänotypischer Merkmale im Feld durch visuelle Abschätzung. Hierbei werden die BBCH Skala oder die OIV Deskriptoren verwendet. Limitierende Faktoren dieser Methoden sind Zeit, Kosten und die Subjektivität bei der Datenerhebung. Innerhalb des Züchtungsprogramms kann daher nur ein reduziertes Set an Genotypen für ausgewählte Merkmale evaluiert werden. Die Automatisierung, Präzisierung und Objektivierung phänotypischer Daten soll dazu führen, dass (1) der bestehende Engpass an phänotypischen Methoden verringert, (2) die Effizienz der Rebenzüchtung gesteigert, und (3) die Grundlage zukünftiger genetischer Studien verbessert wird, sowie (4) eine Optimierung des weinbaulichen Managements stattfindet. Stabile und über die Jahre gleichbleibende Erträge sind für eine Produktion qualitativ hochwertiger Weine notwendig und spielen daher eine Schlüsselrolle in der Rebenzüchtung. Der Fokus dieser Studie liegt daher auf Ertragsmerkmalen wie der Beerengröße, Anzahl der Beeren pro Traube und Menge der Trauben pro Weinstock. Die verwandten Merkmale Traubenarchitektur und das Verhältnis von generativem und vegetativem Wachstum wurden zusätzlich bearbeitet. Die Beurteilung von Ertragsmerkmalen auf Einzelstockniveau ist aufgrund der genotypischen Varianz und der Vielfältigkeit des betrachteten Merkmals komplex und zeitintensiv. Als erster Schritt in Richtung Hochdurchsatz (HT) Phänotypisierung von Ertragsmerkmalen wurden zwei voll automatische Bildinterpretationsverfahren für die Anwendung im Labor entwickelt. Das Cluster Analysis Tool (CAT) ermöglicht die bildgestützte Erfassung der Traubenlänge, -breite und -kompaktheit, sowie der Beerengröße. Informationen über Anzahl, Größe (Länge, Breite) und das Volumen der einzelnen Beeren liefert das Berry Analysis Tool (BAT). Beide Programme ermöglichen eine gleichzeitige Erhebung mehrerer, präziser phänotypischer Merkmale und sind dabei schnell, benutzerfreundlich und kostengünstig. Die Möglichkeit, den Vorder- und Hintergrund in einem Freilandbild zu unterscheiden, ist besonders in einem frühen Entwicklungsstadium der Rebe aufgrund der fehlenden Laubwand schwierig. Eine Möglichkeit, die beiden Ebenen in der Bildanalyse zu trennen, ist daher unerlässlich. Es wurde eine berührungsfreie, schnelle sowie objektive Methode zur Bestimmung des Winterschnittholzgewichts, welches das vegetative Wachstum der Rebe beschreibt, entwickelt. In einem innovativen Ansatz wurde unter Kombination von Tiefenkarten und Bildsegmentierung die sichtbare Winterholzfläche im Bild bestimmt. Im Zuge dieser Arbeit wurde die erste HT Phänotypisierungspipeline für die Rebenzüchtung aufgebaut. Sie umfasst die automatisierte Bildaufnahme im Freiland unter Einsatz des PHENObots, das Datenmanagement mit Datenanalyse sowie die Interpretation des erhaltenen phänotypischen Datensatzes. Die Basis des PHENObots ist ein automatisiert gesteuertes Raupenfahrzeug. Des Weiteren umfasst er ein Multi-Kamera- System, ein RTK-GPS-System und einen Computer zur Datenspeicherung. Eine eigens entwickelte Software verbindet die Bilddaten mit der Standortreferenz. Diese Referenz wird anschließend für das Datenmanagement in einer Datenbank verwendet. Um die Funktionalität der Phänotypisierungspipeline zu demonstrieren, wurden die Merkmale Beerengröße und -farbe im Rebsortiment des Geilweilerhofes unter Verwendung des Berries In Vineyard (BIVcolor) Programms erfasst. Im Durschnitt werden 20 Sekunden pro Weinstock für die Bildaufnahme im Feld benötigt, gefolgt von der Extraktion der Merkmale mittels automatischer, objektiver und präziser Bildauswertung. Im Zuge dieses Versuches konnten mit dem PHENObot 2700 Weinstöcke in 12 Stunden erfasst werden, gefolgt von einer automatischen Bestimmung der Merkmale Beerengröße und -farbe aus den Bildern. Damit konnte die grundsätzliche Machbarkeit bewiesen werden. Diese Pilotpipeline bietet nun die Möglichkeit zur Entwicklung weiterer innovativer Programme zur Erhebung neuer Merkmale sowie die Integration zusätzlicher Sensoren auf dem PHENObot.Grapevine is grown on about 1% of the German agricultural area requiring one third of all fungicides sprayed due to pathogens being introduced within the 19th century. In spite of this requirement for viticulture a reduction is necessary to improve sustainability. This objective can be achieved by growing fungus resistant grapevine cultivars. The development of new cultivars, however, is very time-consuming, taking 20 to 25 years. In recent years the breeding process could be increased considerably by using marker assisted selection (MAS). Further improvements of MAS applications in grapevine breeding will come along with developing of faster and more cost efficient high-throughput (HT) genotyping methods.Complementary to genotyping techniques the quality, objectivity and precision of current phenotyping methods is limited and HT phenotyping methods need to be developed to further increase the efficiency of grapevine breeding through sensor assisted selection. Many different types of sensors technologies are available ranging from visible light sensors (Red Green Blue (RGB) cameras), multispectral, hyperspectral, thermal, and fluorescence cameras to three dimensional (3D) camera and laser scan approaches. Phenotyping can either be done under controlled environments (growth chamber, greenhouse) or can take place in the field, with a decreasing level of standardization. Except for young seedlings, grapevine as a perennial plant needs ultimately to be screened in the field. From a methodological point of view a variety of challenges need to be considered like the variable light conditions, the similarity of fore- and background, and in the canopy hidden traits.The assessment of phenotypic data in grapevine breeding is traditionally done directly in the field by visual estimations. In general the BBCH scale is used to acquire and classify the stages of annual plant development or OIV descriptors are applied to assess the phenotypes into classes. Phenotyping is strongly limited by time, costs and the subjectivity of records. Therefore, only a comparably small set of genotypes is evaluated for certain traits within the breeding process. Due to that limitation, automation, precision and objectivity of phenotypic data evaluation is crucial in order to (1) reduce the existing phenotyping bottleneck, (2) increase the efficiency of grapevine breeding, (3) assist further genetic studies and (4) ensure improved vineyard management. In this theses emphasis was put on the following aspects: Balanced and stable yields are important to ensure a high quality wine production playing a key role in grapevine breeding. Therefore, the main focus of this study is on phenotyping different parameters of yield such as berry size, number of berries per cluster, and number of clusters per vine. Additionally, related traits like cluster architecture and vine balance (relation between vegetative and generative growth) were considered. Quantifying yield parameters on a single vine level is challenging. Complex shapes and slight variations between genotypes make it difficult and very time-consuming.As a first step towards HT phenotyping of yield parameters two fully automatic image interpretation tools have been developed for an application under controlled laboratory conditions to assess individual yield parameters. Using the Cluster Analysis Tool (CAT) four important phenotypic traits can be detected in one image: Cluster length, cluster width, berry size and cluster compactness. The utilization of the Berry Analysis Tool (BAT) provides information on number, size (length and width), and volume of grapevine berries. Both tools offer a fast, user-friendly and cheap procedure to provide several precise phenotypic features of berries and clusters at once with dimensional units in a shorter period of time compared to manual measurements.The similarity of fore- and background in an image captured under field conditions is especially difficult and crucial for image analysis at an early grapevine developmental stage due to the missing canopy. To detect the dormant pruning wood weight, partly determining vine balance, a fast and non-invasive tool for objective data acquisition in the field was developed. In an innovative approach it combines depth map calculation and image segmentation to subtract the background of the vine obtaining the pruning area visible in the image. For the implementation of HT field phenotyping in grapevine breeding a phenotyping pipeline has been set up. It ranges from the automated image acquisition directly in the field using the PHENObot, to data management, data analysis and the interpretation of obtained phenotypic data for grapevine breeding aims. The PHENObot consists of an automated guided tracked vehicle system, a calibrated multi camera system, a Real-Time-Kinematic GPS system and a computer for image data handling. Particularly developed software was applied in order to acquire geo referenced images directly in the vineyard. The geo-reference is afterwards used for the post-processing data management in a database. As phenotypic traits to be analysed within the phenotyping pipeline the detection of berries and the determination of the berry size and colour were considered. The highthroughput phenotyping pipeline was tested in the grapevine repository at Geilweilerhof to extract the characteristics of berry size and berry colour using the Berries In Vineyards (BIVcolor) tool. Image data acquisition took about 20 seconds per vine, which afterwards was followed by the automatic image analysis to extract objective and precise phenotypic data. In was possible to capture images of 2700 vines within 12 hours using the PHENObot and subsequently automatic analysis of the images and extracting berry size and berry colour. With this analysis proof of principle was demonstrated. The pilot pipeline providesthe basis for further development of additional evaluation modules as well as the integration of other sensors

    Anthropogenic impact on ecosystems and land degradation in the Eastern Mongolian Steppe

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    Observing giant panda habitat and forage abundance from space

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    Giant pandas are obligate bamboo grazers. The bamboos favoured by giant pandas are typical forest understorey plants. Therefore, the availability and abundance of understorey bamboo is a key factor in determining the quantity and quality of giant panda food resources. However, there is little or no information about the spatial distribution or abundance of bamboo underneath the forest canopy, due to the limitations of traditional ground survey and remote sensing classification techniques. In this regard, the development of methods that can predict the understorey bamboo spatial distribution and cover abundance is critical for an improved understanding of the habitat, foraging behaviour and distribution of giant pandas, as well as facilitating an optimal conservation strategy for this endangered species. The objectives of this study were to develop innovative methods in remote sensing and GIS for estimating the giant panda habitat and forage abundance, and to explain the altitudinal migration and the spatial distribution of giant pandas in the fragmented forest landscape. It was concluded that 1) the vegetation indices derived from winter (leaf-off) satellite images can be successfully used to predict the distribution of evergreen understorey bamboo in a deciduous-dominated forest, 2) winter is the optimal season for quantifying the coverage of evergreen understorey bamboo in a mixed temperate forest, regardless of the classification methods used, 3) a higher mapping accuracy for understorey bamboo in a coniferous-dominated forest can be achieved by using an integrated neural network and expert system algorithm, 4) the altitudinal migration patterns of sympatric giant pandas and golden takins are related to satellite-derived plant phenology (a surrogate of food quality) and bamboo abundance (a surrogate of food quantity), 5) the driving force behind the seasonal vertical migration of giant pandas is the occurrence of bamboo shoots and the temperature variation along an altitudinal gradient, 6) the satellite-derived forest patches occupied by giant pandas were significantly larger and more contiguous than patches where giant pandas were not recorded, indicating that giant pandas appear sensitive to patch size and isolation effects associated with forest fragmentation. Overall, the study has been shown the potential of satellite remote sensing to map giant panda habitat and forage (i.e., understorey bamboo) abundance. The results are important for understanding the foraging behaviour and the spatial distribution of giant pandas, as well as the evaluation and modelling of giant panda habitat in order to guide decision-making on giant panda conservation. <br/
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