Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography

Cleve, Casey; Kelly, Maggi; Kearns, Faith R.; Moritz, Max A.

doi:10.1016/j.compenvurbsys.2007.10.001

Cited by 252 publications

(128 citation statements)

References 24 publications

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“…tree crowns, forest stands), what is confirmed in a number of previously published works (Blaschke 2010). Using the OBIA method, there were achieved better results of forest and landscape classification, in an average of several tens of percent (Cleve et al 2008;Myint et al 2011;Rittl et al 2013). Worse results related to the accuracy of pixel-based classification were achieved due to the fact that inside the identified tree crowns may occur pixels representing the shadow, errors or noise, i.e.…”

Section: Introductionsupporting

confidence: 64%

Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

Sedliak

Sačkov

Kulla

2017

Central European Forestry Journal

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Remote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous -coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class "coniferous" increased from 82.93% to 85.73% and accuracy of class "deciduous" increased from 84.79% to 90.16%.

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Section: Introductionsupporting

confidence: 64%

Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

Sedliak

Sačkov

Kulla

2017

Central European Forestry Journal

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“…Object-based classification of land use and land cover often results in higher accuracies, when compared to pixel-based classifications [41][42][43][44]. Some studies also used hierarchical classification approaches, mainly to integrate different data types into a comprehensive mapping framework [45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

et al. 2014

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Abstract:The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because agricultural expansion entails substantial environmental trade-offs, intensification and recultivation are currently gaining increasing attention. Assessing where these strategies may be pursued, however, requires improved spatial information on land use intensity, including where farmland is active and fallow. We developed a framework to integrate optical and radar data in order to advance the mapping of three farmland management regimes: (1) large-scale, mechanized agriculture; (2) small-scale, subsistence agriculture; and (3) fallow or abandoned farmland. We applied this framework to our study area in western Ukraine, a region characterized by marked spatial heterogeneity in management intensity due to the legacies from Soviet land management, the breakdown of the Soviet Union in 1991, and the recent integration of this region into world markets. We mapped land management regimes using a hierarchical, Remote Sens. 2014, 6 5280 object-based framework. Image segmentation for delineating objects was performed by using the Superpixel Contour algorithm. We then applied Random Forest classification to map land management regimes and validated our map using randomly sampled in-situ data, obtained during an extensive field campaign. Our results showed that farmland management regimes were mapped reliably, resulting in a final map with an overall accuracy of 83.4%. Comparing our land management regimes map with a soil map revealed that most fallow land occurred on soils marginally suited for agriculture, but some areas within our study region contained considerable potential for recultivation. Overall, our study highlights the potential for an improved, more nuanced mapping of agricultural land use by combining imagery of different sensors.

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“…A 7, 4 and 2 band combination was adopted by the study. The images were filtered with 3 x 3 majority filter prior to classification to replace each pixel value with a new value (McDonnell, 1981;Cleve et al, 2008).The images were subjected to supervised classification procedure with maximum likelihood algorithm. A supervised maximum likelihood methodology was adopted because each land use/cover class had a Gaussian distribution (Dewan & Yamaguchi, 2009).…”

Section: Estimating Land Use Changementioning

confidence: 99%

The Effect of Land Use on Carbon Stocks and Implications for Climate Variability on the Slopes of Mount Elgon, Eastern Uganda

Mugagga¹

2015

IJRD

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We investigated the impact of land use change on carbon stocks and its implications to climate variability in Mountain environments. Remotely sensed biophysical data was used to determine the extent of land use change over the last two decades. Land uses were stratified thus; forest under restoration, woodlots/plantations, agricultural land, regenerating forest and intact forest. Carbon in above and below ground biomass was measured using the allometric equations, whilst soil samples were analyzed for total carbon by the dry combustion method. The differences in carbon stocks in various land uses were analyzed using Analysis of variance (ANOVA). Top soil layers (0-10cm) were found to store more carbon than the deeper ones. Furthermore, intact forest stored more soil organic carbon (> 45t/ha) compared to other land uses with the least in land under agriculture (about 1.5 t/ha). The decimation of land uses with high carbon stocks was noted to reduce the potential of Mt. Elgon as a carbon sink. This therefore calls for the scaling out and up of forest restoration programmes in and

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Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography

Cited by 252 publications

References 24 publications

Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

The Effect of Land Use on Carbon Stocks and Implications for Climate Variability on the Slopes of Mount Elgon, Eastern Uganda

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