Time Series of Images to Improve Tree Species Classification

Miyoshi, Gabriela Takahashi; Imai, Nilton Nobuhiro; Moraes, Marcus Vinícius Antunes de; Tommaselli, Antônio Maria Garcia; Näsi, Roope

doi:10.5194/isprs-archives-xlii-3-w3-123-2017

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Hill et al (2010) had reported that the ability to discriminate and map temperate deciduous tree species in airborne multispectral imagery increased using timeseries data: from kappa 0.67 for a single image to kappa 0.85 for three images combined (one from autumn, one from the green-up, and one from full-leaf phase). Miyoshi et al (2017) showed that use of layer stacking from time-series images improved classification, and they achieved an 18% increase in kappa coefficient. Recent studies have used all available cloud-free satellite scenes during a year to classify tree species.…”

Section: Assessment Of Classification Accuracymentioning

confidence: 99%

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

Grigorieva¹,

Brovkina²,

Saidov³

2020

Silva Fenn.

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This study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectralâtemporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time.

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Section: Assessment Of Classification Accuracymentioning

confidence: 99%

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

Grigorieva¹,

Brovkina²,

Saidov³

2020

Silva Fenn.

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“…Consequently, to detect small differences in the spectral signatures of species, instruments with many contiguous bands at small bandwidths (<20 nm) tend to produce better classification accuracy [13]. The most important regions of the electromagnetic spectrum for distinguishing plant species are the near infra-red (NIR) and short-wave infrared (SWIR) regions [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…One way to enhance the spectral differences between species is to utilize multiple images, taken across a growing season [22,33]. Studies have shown that, when using a single image to map plant species, an image that is taken at peak productivity often produces the most accurate results [20,30,33]. Recently there has been a surge of interest in the use of multiple images acquired across time (time series imagery) on the accuracy of mapping plant species [20,34].…”

Section: Introductionmentioning

confidence: 99%

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Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem

et al. 2020

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In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.

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“…In recent years, a growing number of studies have taken advantage of UAVs to obtain information on seasonal changes for different tree species in order to understand their distributions. Lisein et al [16] and Miyoshi et al [17] found that phenological information can improve the classification results of tree species classification. They also state that the information acquired in leaf transition states makes the most significant contribution.…”

Section: Introductionmentioning

confidence: 99%

Seeing Trees from Drones: The Role of Leaf Phenology Transition in Mapping Species Distribution in Species-Rich Montane Forests

Jiang

Kong

Zhang

et al. 2023

Forests

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The complex topography of subtropical montane forests favors the coexistence of diverse plant species, making these species-rich forests a high priority for biodiversity monitoring, prediction, and conservation. Mapping tree species distribution accurately in these areas is an essential basis for biodiversity research and is often challenging due to their complex structure. Remote sensing has widely been used for mapping tree species, but relatively little attention has been paid to species-rich montane forests. In this study, the capability of high-resolution UAV remote sensing imagery for mapping six tree species, standing dead trees, and canopy gaps was tested in a subtropical montane forest at an elevation of 816~1165 m in eastern China. Spectral, spatial geometrical, and textural features in a specific phenological period when obvious color differences among the leaves of different species were extracted, and four object-based classification algorithms (K-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), and random forest (RF)) were used for tree species classification. We found that: (1) mapping tree species distribution using low-cost UAV RGB imagery in a specific leaf phenological period has great application potential in subtropical montane forests with complex terrain. (2) Plant spectral features in the leaf senescence period contributed significantly to species classification, while the contribution of textural features was limited. The highest classification accuracy was 83% using KNN with the combination of spectral and spatial geometrical features. (3) Topographical complexity had a significant impact on mapping species distribution. The classification accuracy was generally higher in steep areas, especially in the low slope area.

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Time Series of Images to Improve Tree Species Classification

Cited by 4 publications

References 19 publications

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem

Seeing Trees from Drones: The Role of Leaf Phenology Transition in Mapping Species Distribution in Species-Rich Montane Forests

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