Mapping understory vegetation using phenological characteristics derived from remotely sensed data

Tuanmu, Mao‐Ning; Viña, Andrés; Bearer, Scott; Xu, Weihua; Ouyang, Zhiyun; Zhang, Hemin; Liu, Jianguo

doi:10.1016/j.rse.2010.03.008

Cited by 126 publications

(75 citation statements)

References 66 publications

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“…In the 1970s, the area of the reserve was extended to 2000 km 2 to accommodate approximately 10% of the total wild giant panda population (State Forestry Administration, 2006). Aside from giant pandas, there are thousands of plant and other animal species found within the reserve (Li et al, 1992;Tuanmu et al, 2010). Wolong is internationally important because it is also part of UNE-SCO's Man and Biosphere Reserve network (Liu et al, 1999b).…”

Section: Study Areamentioning

confidence: 99%

Natural recovery and restoration in giant panda habitat after the Wenchuan earthquake

Zhang

Hull

Huang

et al. 2014

Forest Ecology and Management

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a b s t r a c tNatural disasters affect forest ecosystems in profound and complex ways. Artificial restoration projects have been conducted worldwide to repair disaster damage to forests, but the efficacy of such projects in light of naturally occurring recovery processes is rarely evaluated. To fill such an important knowledge gap, we investigated forest recovery and restoration in the world-renowned Wolong Nature Reserve in Sichuan, China after the catastrophic Wenchuan earthquake (magnitude 8.0) in 2008, which caused considerable damage to the forest and habitat of the endangered giant panda. This was the first multi-year field study to document natural recovery of forests in response to this disaster. Forest sampling conducted in panda habitat over a four-year period after the earthquake revealed that natural recovery was rapid, with vegetation covering roughly 70% of once denuded sites by the fourth sampling year. Vegetation recovery was further improved in sampled artificial restoration sites, which recovered from an average of 30% vegetation cover to 70% in only one year. Factors including soil cover and slope were correlated with successful vegetation recovery. New information learned from the multi-year field data provided a finer scale context for understanding the effects of disasters, a novel contribution considering that the majority of previous work has been conducted at the broader scale using remote sensing. Spatial analysis revealed that restoration sites were mainly distributed in areas of suitable slopes and elevations, but a measurable proportion (30-40%) were located too far from the existing panda distribution area and too close to human settlements. The restoration project has thus far had limited direct effect on giant panda conservation, but has indirect effects on improving forest cover in areas previously affected by human disturbances. This study provides a useful reference for understanding conditions affecting forest recovery, which can inform decision-making surrounding the implementation of forest restoration projects and conservation of endangered species, not only in China but also around the world.

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Section: Study Areamentioning

confidence: 99%

Natural recovery and restoration in giant panda habitat after the Wenchuan earthquake

Zhang

Hull

Huang

et al. 2014

Forest Ecology and Management

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“…For example, MODIS Vegetation Index (VI) time series have improved the classification of abandoned farmland, different crop types, and semi-arid vegetation by capturing the specific phenological pattern of each land cover type. Other studies developed phenological metrics from NDVI or EVI time series to describe patterns in vegetation phenology, e.g., length and peak of the vegetation season, and to use this information for mapping different land cover types [15][16][17], including different forest types [18].…”

Section: Introductionmentioning

confidence: 99%

Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

et al. 2013

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Abstract:We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia's biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4% and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS.

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“…Previous research in Gatineau Park revealed that areas with large and continuous patches of vegetation can be detected with 30 m resolution Landsat 8 imagery acquired during the period when wild leek emerges [48], suggesting that this free source of satellite imagery may be suitable as the basis for wild leek monitoring in the park. The ability of Landsat imagery to detect understory species has been demonstrated before, where invasive bamboo species [22] and European buckthorn [21] were mapped under deciduous forests. An advantage of using a time series to capture phenological differences throughout the year, as achieved by these studies, is that prior knowledge or testing on the optimal dates is not needed.…”

Section: Discussionmentioning

confidence: 83%

“…Using a set threshold has the benefit of being easily implemented, but might lack the flexibility and precision to separate other species present on the forest floor. Therefore, this method is not easily applicable to different species, compared to other methods based on phenological differences [21,22]. Furthermore, improved accuracies might be achieved by using hyperspectral remote sensing data if available, to better discriminate wild leek from other forest floor species, which have been shown to display differences in spectral characteristics [18].…”

Section: Discussionmentioning

confidence: 99%

“…A study in the Oak Openings Region in Ohio presented a method to map the European buckthorn, an invasive species that develops leaves earlier and remains green later than surrounding vegetation, using Landsat time series [21]. Another study also used Landsat imagery to successfully map the distribution of two understory bamboo species in the Wolong Nature Reserve in China [22]. In those studies, land surface phenology metrics were extracted, where pixels dominated by specific understory vegetation types exhibited distinct signals throughout the course of a year.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Wild Leek through the Forest Canopy Using a UAV

Leduc

Knudby

2018

Remote Sensing

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Abstract:Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This paper explores the accuracy with which wild leek can be mapped with a low-flying UAV. Nadir video imagery was obtained using a commercial UAV during the spring of 2017 in Gatineau Park, Quebec. Point clouds were generated from the video frames with the Structure-from-Motion framework, and a multiscale curvature classification was used to separate points on the ground, where wild leek grows, from above-ground points belonging to the forest canopy. Five-cm resolution orthomosaics were created from the ground points, and a threshold value of 0.350 for the green chromatic coordinate (GCC) was applied to delineate wild leek from wood, leaves, and other plants on the forest floor, with an F1-score of 0.69 and 0.76 for two different areas. The GCC index was most effective in delineating bigger patches, and therefore often misclassified patches smaller than 30 cm in diameter. Although short flight times and long data processing times are presently technical challenges to upscaling, the low cost and high accuracy of UAV imagery provides a promising method for monitoring the spatial distribution of this endangered species.

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Mapping understory vegetation using phenological characteristics derived from remotely sensed data

Cited by 126 publications

References 66 publications

Natural recovery and restoration in giant panda habitat after the Wenchuan earthquake

Natural recovery and restoration in giant panda habitat after the Wenchuan earthquake

Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

Mapping Wild Leek through the Forest Canopy Using a UAV

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