A spatial–temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery

Liu, Desheng; Kelly, Maggi; Gong, Peng

doi:10.1016/j.rse.2005.12.012

Cited by 131 publications

(71 citation statements)

References 33 publications

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“…Only when more comprehensive test data sets covering major environmental types of the world can we make more appropriate selection of algorithms for a particular application of remote sensing classification. Another important aspect that has not been assessed in this research is feature extraction and use of non-spectral features whose effectiveness has been demonstrated in the literature [45][46][47][48][49][50][51]. Furthermore, use of multisource data including optical, thermal and microwave data in urban land classification should be systematically evaluated [52,53].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

et al. 2014

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Abstract:Although a large number of new image classification algorithms have been developed, they are rarely tested with the same classification task. In this research, with the same Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, we tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years. Our analysis focused primarily on the spectral information provided by the TM data. We assessed all algorithms in a per-pixel classification decision experiment and all supervised algorithms in a segment-based experiment. We found that when sufficiently representative training samples were used, most algorithms performed reasonably well. Lack of training samples led to greater classification accuracy discrepancies than classification algorithms themselves. Some algorithms were more tolerable to insufficient (less representative) training samples than

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Section: Discussionmentioning

confidence: 99%

Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

et al. 2014

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“…With high spatial resolution imagery, single pixels no longer capture the characteristics of classification targets. Instead, adjacent pixels tend to belong to the same class or some compatible classes with an ecological or functional association [129,130].…”

Section: Thoughts On Urban Morphology and Functionmentioning

confidence: 99%

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

et al. 2011

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Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond "hard infrastructure" by addressing "humans as sensors", mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

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“…Carpenter et al (1999) produced a lifeform map for the Sierra Nevada mountain range in California from Landsat TM data by applying the ARTMAP neural network method. Liu et al (2006) mapped the distribution of forest disease, sudden oak death, in northern California from two-year images obtained by Airborne Data Acquisition and a Registration system. Mallinis et al (2008) used an object-based classification method to delineate vegetation polygons in a conifer forest from Quickbird imagery.…”

Section: Introductionmentioning

confidence: 99%

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Fry

Collins

et al. 2015

Canadian Journal of Remote Sensing

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Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites.Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l'analysé ecologique, et la modélisation hydrologique. Les approches d'imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu'un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cetté etude présente une stratégie qui utiliseà la fois l'imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétationà grandeséchelles spatiales. Notre approche comprenait l'utilisation d'un algorithme du critère d'information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d'étude dans les Sierra Nevada, en Californie, ainsi qu'une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d'étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d'étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de...

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A spatial–temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery

Cited by 131 publications

References 33 publications

Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

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