Automated tree crown detection and size estimation using multi-scale analysis of high-resolution satellite imagery

Skurikhin, Alexei N.; Garrity, S. R.; McDowell, Nate G.; Cai, D. Michael

doi:10.1080/2150704x.2012.749361

Cited by 36 publications

(18 citation statements)

References 39 publications

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“…Although not yet global in application, 1 m resolution optical imagery is valuable for detailed assessments of vegetation characteristics because it can resolve individual tree canopies (e.g., [32,33]). Microwave and other nonoptical remote-sensing techniques, while not the main object Creating a framework for global detection of disturbances and their causes Given the large recent advances and the range of techniques available, coupled with the increasing frequency of disturbances, it seems logical that remote sensing is poised to make large and important breakthroughs in disturbance monitoring.…”

Section: An Example Of the Challenges And Potential Of Remotely Sensementioning

confidence: 99%

Global satellite monitoring of climate-induced vegetation disturbances

McDowell

Coops

Beck

et al. 2015

Trends in Plant Science

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196

136

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Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide. Global disturbance detection Changing climate has been linked to an increased rate of vegetation disturbances and mortality, promoting major

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Section: An Example Of the Challenges And Potential Of Remotely Sensementioning

confidence: 99%

Global satellite monitoring of climate-induced vegetation disturbances

McDowell

Coops

Beck

et al. 2015

Trends in Plant Science

Self Cite

196

136

View full text Add to dashboard Cite

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“…Horváth et al (2006) modeled the tree crown distribution in a forest with a ''gas of circles'' and Zhang and Sohn (2010) utilized a Markov Random Field model to optimize the spatial configuration of the trees. Regardless of the underlying segmentation algorithm, some authors have emphasized the scale space aspect either explicitly by locally adaptive methods (Palenichka et al, 2013;Popescu and Wynne, 2004), or implicitly by using a localized image processing treatment established by scale approximation techniques such as granulometry (Heinzel et al, 2011), multi-scale analysis (Skurikhin et al, 2013) or area-based stem number approximation .…”

Section: )mentioning

confidence: 99%

A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

Strîmbu

2015

ISPRS Journal of Photogrammetry and Remote Sensing

116

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a b s t r a c tThis work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures.The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m 2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m 2 .More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent.

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“…Gaussian scale-space theory (Brandtberg and Walter, 1998;Lindeberg, 1998;Skurikhin et al, 2013;Wang, 2010;Witkin, 1983) has been proposed to bridge the gap between the complex object patterns, detailed geographical coordinates and sizes of objects. It is based upon detection of local maxima in the scale-space domain, detecting trees by providing their location and diameter (Mahour et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Scaling of remote sensing information for orchard management

Mahour¹

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After an exciting period of working and doing research at ITC Faculty, today is the day: writing this note of appreciation is the finishing touch on my PhD research. It has been a time of profound learning for me, not only in the scientific arena but also on a personal level. Doing this research has had a significant impact on me. I would like to deliberate on the people who have helped and encouraged me so much throughout this period. I would first like to express my special appreciations to my promoter Prof. Alfred Stein for his advice, support and continuous encouragements have contributed significantly to the completion of this research. I would remarkably like to single out my supervisor Dr. Valentyn Tolpekin: I want to thank you for your excellent assistance and for all of the opportunities I was given to conduct my research. I would like to extend my appreciation to Dr. Ali Sharifi for his support, advice and critical comments. I would also like to thank my family for the love of my life my loving mother Fariba, my father Jamshid, my brothers Mohamadamin and Amirali, and my lovely grandma, thank you for giving me a stable mind to think and work for science. I am thankful to you for all what you have done for me. Because of you, I am standing here.

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Automated tree crown detection and size estimation using multi-scale analysis of high-resolution satellite imagery

Cited by 36 publications

References 39 publications

Global satellite monitoring of climate-induced vegetation disturbances

Global satellite monitoring of climate-induced vegetation disturbances

A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

Scaling of remote sensing information for orchard management

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