Mapping coffee plantations with Landsat imagery: an example from El Salvador

Ortega-Huerta, Miguel A.; Komar, Oliver; Price, Kevin P.; Ventura, Hugo J.

doi:10.1080/01431161.2011.591442

Cited by 30 publications

(26 citation statements)

References 63 publications

(55 reference statements)

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“…Sesnie et al [37], working in the same region, was able to classify a general tree plantation class with ~90% accuracy using Landsat and ancillary data and a labor-intensive image-subsetting technique; however, class accuracy decreased to 55% when implemented at larger image extents. Spectral confusion with native vegetation is a well-known challenge in agroforestry and tree crop systems, particularly in mapping shade coffee [68,69], oil palm [70][71][72][73], and evergreen rubber tree plantations [74,75].…”

Section: Introductionmentioning

confidence: 99%

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

et al. 2015

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An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p < 0.0001) OPEN ACCESSRemote Sens. 2015, 7 5661 of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

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Section: Introductionmentioning

confidence: 99%

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

et al. 2015

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“…The remote sensing variables such as spectral bands, vegetation indices, and textures, and supervised classification algorithms such as MLC, neural network, and support vector machine may be satisfied for overall land cover classification, but may not provide the best result for an individual class, that is, no one approach is optimal for each type of land cover [7,51,70]. We need to develop a specific approach to accurately extract a given class, and such approaches are not universal, as they depend on specific land covers such as rubber plantation [14,19,21] and hickory plantation [37]. So far no one approach can be used for extracting different tree species, thus we have to develop specific approaches depending on the tree species.…”

Section: Discussionmentioning

confidence: 99%

“…Extraction of individual tree species such as Torreya, hickory and rubber plantations is often difficult due to its similar spectral features with other forest types [13][14][15]. Fassnacht et al [16] provide a comprehensive review of tree species classification from different types of remotely sensed data.…”

Section: Introductionmentioning

confidence: 99%

“…Fassnacht et al [16] provide a comprehensive review of tree species classification from different types of remotely sensed data. Many previous studies have explored the approaches to map individual tree species or plantations (e.g., rubber, hickory, oil palm, and coffee) using different remote sensing data [13,14,[17][18][19][20][21][22][23]. Selection of suitable remote sensing data is important for a successful classification of tree species.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

Wang

2017

Remote Sensing

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Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a timely way. However, to date there are no suitable approaches available for mapping Torreya forest distribution, especially the new Torreya plantations, due to the complex landscapes. This research used high spatial resolution Chinese Gaofen (GF-1) and Ziyuan (ZY-3) satellite images and digital elevation model (DEM) data to extract old Torreya forests and new Torreya plantations using a newly proposed expert rules-based approach. Different variables such as spectral bands, vegetation indices, textural images, and DEM-derived variables were examined, and separability analyses of different land covers were explored. An expert rules-based approach was developed for the extraction of old Torreya forests and new Torreya plantations. The accuracy assessment using field survey data and Google Earth images indicates that this newly-proposed approach can effectively distinguish both old Torreya forests and new Torreya plantations from other land covers with producer's accuracies of 84% and 92%, and user's accuracies of 77% and 85%, respectively, much better classification accuracies than the maximum likelihood classifier. This new approach may be used for other study area for extracting Torreya forest distribution. This research provides valuable data sources for better managing existing Torreya forests and planning potential Torreya expansions in this region in the near future.

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“…(Moreira et al (2004)) demonstrated that coffee fields have a high spectral variability due to differences in age, plant spacing and cultivars. In addition, topographical effects and spectral confusion between coffee and other tree crops may lead to poor classification accuracies (Cordero-Sancho & Sader, 2007;Gomez et al, 2010;Ortega-Huerta et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Smallholder coffee terroirs of Rwanda : linking local to global trade through geographical characterization

Mukashema¹

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Mapping coffee plantations with Landsat imagery: an example from El Salvador

Cited by 30 publications

References 63 publications

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

Smallholder coffee terroirs of Rwanda : linking local to global trade through geographical characterization

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