Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters

Chubey, Michael S.; Franklin, Steven E.; Wulder, Michael A.

doi:10.14358/pers.72.4.383

Cited by 172 publications

(129 citation statements)

References 90 publications

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“…VIF was normally employed to analyze multicollinearity and some variables indicating on collinearity (multicollinearity) might be removed, which resulted in model that explained less variance than the best possible full model with more variables. Therefore, more robust statistical methods, which did not need to make any assumptions about the data, such as artificial Neural Networks (ANN) [89][90][91], Classification and Regression Tree Analysis (CART) [22,92,93], and Random forests (RF) [88,94,95] were widely used to investigate complex relationship between forests stand variables and remotely sensed data. These robust statistical techniques should be given first priority in future remote sensing studies as many researches have already demonstrated that nonlinear interactions might exist between the observed data and remotely sensed data [88,90,96].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

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Section: Discussionmentioning

confidence: 99%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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“…The forest description of smaller-scale forests is less reliable due to inconsistent area definition and management are similar to real land cover features in size and shape [17]. The approach allows use of multiple image elements, parameters and scales such as texture, shape and context, as opposed to pixel-based classification that solely relies on the pixel value.…”

Section: Introductionmentioning

confidence: 99%

“…The subsequent subsets are separated further until no further division is possible or the tree reaches a defined maximum depth [26,27]. CART has been used in a number of studies for land cover classification [28], delineating forest boundaries [22] and extracting forest variables [17].…”

Section: Introductionmentioning

confidence: 99%

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Xu¹,

Morgenroth²,

Manley³

2017

Forests

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Abstract:In New Zealand, approximately 70% of plantation forests are large-scale (over 1000 ha) with accurate resource description. In contrast, the remaining 30% of plantation forests are small-scale (less than 1000 ha). It is forecasted that these small-scale forests will supply nearly 40% of the national wood production in the next decade. However, in-depth description of these forests, especially those under 100 ha, is very limited. This research evaluates the use of remote sensing datasets to map and estimate the net stocked plantation area for small-scale forests. We compared a factorial combination of two classification approaches (Nearest Neighbour (NN), Classification and Regression Tree (CART)) and two remote sensing datasets (RapidEye, RapidEye plus LiDAR) for their ability to accurately classify planted forest area. CART with a combination of RapidEye and LiDAR metrics outperformed the other three combinations producing the highest accuracy for mapping forest plantations (user's accuracy = 90% and producer's accuracy = 88%). This method was further examined by comparing the mapped plantations with manually digitised plantations based on aerial photography. The mapping approach overestimated the plantation area by 3%. It was also found that forest patches exceeding 10 ha achieved higher conformance with the digitised areas. Overall, the mapping approach in this research provided a proof of concept for deriving forest area and mapping boundaries using remote sensing data, and is especially relevant for small-scale forests where limited information is currently available.

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“…A decision tree classification was used in an object-based analysis of IKONIS imagery for forest inventories (Chubey et al, 2006). Hay and Castilla (2006) applied Object-based Image Analysis for partitioning remotely sensed imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Combining Pixel-Based and Object-Oriented Support Vector Machines using Bayesian Probability Theory

Salah

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This study employed a hybrid system for the combination of pixel-based (PB) and object-oriented (OO) Support Vector Machines (SVMs) based on Bayesian Probability Theory (BPT) for improved land cover classification. A set of uncorrelated feature attributes have been generated from a one-meter IKONOS satellite image. Four different SVMs kernels were compared and tested to classify buildings, trees, roads and ground from satellite image and the generated attributes. The kernels used include: linear, polynomial, radial basis function (RBF), and sigmoid. PB and OO SVMs have been applied to classify the image. BPT was then applied for combining the class memberships from the PB and OO classifiers. Accuracy assessment was carried out using reference data sets derived from the one-meter IKONOS image. The outcomes demonstrate that the OO method has achieved an overall kappa coefficient of 0.8286, compared with 0.6327 that was derived from the conventional PB method. The improvement in overall kappa obtained from the combined system was 0.0608 over the OO SVMs.

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Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters

Cited by 172 publications

References 90 publications

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Combining Pixel-Based and Object-Oriented Support Vector Machines using Bayesian Probability Theory

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