Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery

Abstract

Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally.

Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests

“…Our results indicated that green and shortwave infrared wavelength regions were especially important for detecting the ferns and vines within the dense vegetation of tropical rainforests (Figure 5). This result is consistent with the previous reports that showed the potential of the visible green wavelength or shortwave infrared region to separate liana or fern from trees [21,[23][24][25][26]28,77]. On the other hand, the individual near-infrared wavelength region was less important, but vegetation indices calculated based on near-infrared regions with green (NDWI) or SWIR2 (NBR) were effective.…”

“…Matongera et al [22] have demonstrated the possibility of mapping the distribution of bracken fern (Pteridium spp.) thickets by using Landsat 8 imagery, and Chandler et al [23] have mapped the vine-laden forest by using the Sentinel-2 imagery of Bornean tropical rainforests. However, no study has classified both vegetation types, which together threaten forest recovery, within the same landscape.…”

“…For canopy-level separation between vines and trees in tropical forests, detailed studies using hyperspectral spectroscopy have been conducted and two wavelength regions have been reported to be important [24]: the visible green region, which represents the characteristics of chlorophyll and carotenoid concentration [21,[25][26][27], and the shortwave infrared (SWIR) region, which represents water content [26,27]. When using a multispectral optical satellite sensor, the visible green region of Sentinel-2 is especially helpful [23]. Ferns represent more distinct spectral characteristics from trees than vines do, and the visible green [28] and near-infrared (NIR) wavelength regions [29][30][31] have been shown to be useful for separating bracken ferns from trees in multispectral satellite imagery.…”

“…Ferns represent more distinct spectral characteristics from trees than vines do, and the visible green [28] and near-infrared (NIR) wavelength regions [29][30][31] have been shown to be useful for separating bracken ferns from trees in multispectral satellite imagery. In addition to the surface reflectance, vegetation indices have been used to enhance the optical properties of the ferns or vines [22,23,28]. For vegetation classification, besides vegetation indices, information of the gray-level co-occurrence matrix (GLCM) texture measures [32] and synthetic aperture radar (SAR) have also been commonly used to improve the classification accuracy [33][34][35][36]; however, no studies have applied either GLCM textures or SAR data to classify fern thickets or vine-laden forests.…”

Making (remote) sense of lianas

“…To make remote sense of lianas, different combinations of sensors and platforms are (to be) used. For example: Spatial and temporal liana distributions (section 2) can benefit from Sentinel‐2 satellite data (Chandler, van der Heijden, Boyd, & Foody, 2021; see panel [a]) and airborne hyper‐spectral and LiDAR data (e.g. Chandler, van der Heijden, Boyd, Cutler, et al, 2021; see [b]).…”