Tree Crown Detection on Multispectral VHR Satellite Imagery

Daliakopoulos, Ioannis N.; Grillakis, Manolis; Koutroulis, Aristeidis; Tsanis, Ioannis K.

doi:10.14358/pers.75.10.1201

Cited by 42 publications

(34 citation statements)

References 21 publications

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“…Using pansharpened QuickBird imagery and relying on a combination of blob detection in the red band and NDVI (Normalised Difference Vegetation Index) thresholding, Reference [2] achieved acceptable accuracy in detecting trees of different types in, including olive trees. Reference [3] reached a user's accuracy of 100% for olive tree canopies (although the producer's accuracy was lower than 50%) with a fully-automated, multi-scale hierarchical classification algorithm applied on IKONOS pansharpened imagery (overall accuracy at the scale of trees was 74%).…”

Section: Introductionmentioning

confidence: 99%

Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring

Karydas¹,

Gewehr²,

Iatrou³

et al. 2017

J. Imaging

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Abstract:The objective of this study was to develop a methodology for mapping olive plantations on a sub-tree scale. For this purpose, multispectral imagery of an almost 60-ha plantation in Greece was acquired with an Unmanned Aerial Vehicle. Objects smaller than the tree crown were produced with image segmentation. Three image features were indicated as optimum for discriminating olive trees from other objects in the plantation, in a rule-based classification algorithm. After limited manual corrections, the final output was validated by an overall accuracy of 93%. The overall processing chain can be considered as suitable for operational olive tree monitoring for potential stresses.

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Section: Introductionmentioning

confidence: 99%

Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring

Karydas¹,

Gewehr²,

Iatrou³

et al. 2017

J. Imaging

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“…Template matching segmentation methods rely on a generalized tree model. The choice of tree model varies from illumination patterns (Hung et al, 2012) to ellipses (Larsen and Rudemo, 1998), ellipsoids (Wolf and Heipke, 2007), Gaussian blobs (Brandtberg et al, 2003;Daliakopoulos et al, 2009;Pirotti, 2010), conic and parabolic surfaces (Persson et al, 2002;Tittmann et al, 2012). Region growing algorithms (Culvenor, 2002;Erikson, 2003;Hirschmugl et al, 2007) start with seed pixels and progressively grow regions by iteratively including adjacent pixels until a threshold of expansion or stopping criteria are met.…”

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

118

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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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“…Agronomic applications need to work on tree level, and the increase of spatial resolution of satellite imagery in the last decade has enabled researchers to develop methods for tree canopy detection. Multispectral sensors have the additional advantage of recording separately the Visible Red (R) and the Near Infrared (NIR) band, which are important for vegetation mapping [5].…”

Section: Introductionmentioning

confidence: 99%

“…There are several methods which have been studied for automatic tree delineation, such as local spatial maxima definition, valley following and clustering [5,6]. All methods have to face the problems of tree canopy overlapping, the small size of young trees, the analysis of pixels with mixed reflectance of soil and tree, and the internal heterogeneity of spectral information of trees and background.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

et al. 2014

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The Common Agricultural Policy of the European Union grants subsidies for olive production. Areas of intensified olive farming will be of major importance for the increasing demand for oil production of the next decades, and countries with a high ratio of intensively and super-intensively managed olive groves will be more competitive than others, since they are able to reduce production costs.It can be estimated that about 25-40% of the Sicilian oliviculture must be defined as "marginal". Modern olive cultivation systems, which permit the mechanization of pruning and harvest operations, are limited.Agronomists, landscape planners, policy decision-makers and other professionals have a growing need for accurate and cost-effective information on land use in general and agronomic parameters in the particular. The availability of high spatial resolution imagery has enabled researchers to propose analysis tools on agricultural parcel and tree level.In our study, we test the performance of WorldView-2 imagery relative to the detection of olive groves and the delineation of olive tree crowns, using an object-oriented approach of image classification in combined use with LIDAR data.We selected two sites, which differ in their environmental conditions and in their agronomic parameters of olive grove cultivation. The main advantage of the proposed methodology is the low necessary quantity of data input and its automatibility. However, it should be applied in other study areas to test if the good results of accuracy assessment can be confirmed.Data extracted by the proposed methodology can be used as input data for decision-making support systems for olive grove management.

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Tree Crown Detection on Multispectral VHR Satellite Imagery

Cited by 42 publications

References 21 publications

Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring

Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring

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

Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

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