The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees

Sesnie, Steven E.; Finegan, Bryan; Gessler, Paul E.; Thessler, Sirpa; Bendaña, Zayra Ramos; Smith, Alistair M. S.

doi:10.1080/01431160903140803

Cited by 81 publications

(53 citation statements)

References 59 publications

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“…This is a particular challenge in monsoon areas of Southeast Asia, where variation can be especially high among natural forest types in their physical structure and seasonal patterns of canopy cover [5]. Several studies have established that local floristic differences in tropical forest can be effectively mapped using medium-resolution multi-spectral imagery such as Landsat, typically in combination with topographic data [17][18][19][20][21]. However, increasing the variability within forest classes or the amount of spectral overlap between classes represents a considerable challenge to accurate discrimination of forest types [22].…”

Section: Mapping Of Forest Types and Degradation Extentmentioning

confidence: 99%

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

Connette

Oswald²,

Songer

et al. 2016

Remote Sensing

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Abstract:We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar's Tanintharyi Region and estimated the extent of degraded forest for each unique forest type. We mapped a total of 16 natural and human land use classes using both a Random Forest algorithm and a multivariate Gaussian model while considering scenarios with all natural forest classes grouped into a single intact or degraded category. Overall, classification accuracy increased for the multivariate Gaussian model with the partitioning of intact and degraded forest into separate forest cover classes but slightly decreased based on the Random Forest classifier. Natural forest cover was estimated to be 80.7% of total area in Tanintharyi. The most prevalent forest types are upland evergreen forest (42.3% of area) and lowland evergreen forest (21.6%). However, while just 27.1% of upland evergreen forest was classified as degraded (on the basis of canopy cover <80%), 66.0% of mangrove forest and 47.5% of the region's biologically-rich lowland evergreen forest were classified as degraded. This information on the current status of Tanintharyi's unique forest ecosystems and patterns of human land use is critical to effective conservation strategies and land-use planning.

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Section: Mapping Of Forest Types and Degradation Extentmentioning

confidence: 99%

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

Connette

Oswald²,

Songer

et al. 2016

Remote Sensing

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“…Spectral reflectance of tree plantations can be quite variable even within the same tree species, changing with plantation nutrition and disease status [57], degree of canopy disturbance [58], underlying soil type [59], and plantation age [52]. Tree plantation classification accuracy across landscapes is highly variable in the multispectral sensor literature, with frequent confusion between plantations and both secondary and mature forests, respectively [9,[60][61][62][63][64][65][66][67].…”

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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“…RF classifiers are not as susceptible as individual classification trees to over-fitting or "overtraining" [35][36][37][38], and they are especially good at modeling nonlinearity and interactions among predictor variables [39]. RF classifiers have demonstrated higher predictive accuracy than more traditional classification methods in both ecology [39] and remote sensing [40], and have demonstrated comparable predictive accuracy to other machine-learning methods (e.g., support vector machines) [36,41]. In addition, RF models are relatively robust to error or noise in training datasets [35,37,42], making them particularly appealing for mitigating potential errors in stable site identification or assignment of class labels to stable sites.…”

Section: Random Forest Classificationmentioning

confidence: 99%

Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

2016

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Abstract:Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG) algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time.

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The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees

Cited by 81 publications

References 59 publications

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

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

Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

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