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

Abstract

Forest degradation has been most frequently defined as an anthropogenic reduction in biomass compared with reference biomass in extant forests. However, so-defined “degraded forests” may widely vary in terms of recoverability. A prolonged loss of recoverability, commonly described as a loss of resilience, poses a true threat to global environments. In Bornean logged-over forests, dense thickets of ferns and vines have been observed to cause arrested secondary succession, and their area may indicate the extent of slow biomass recovery. Therefore, we aimed to discriminate the fern thickets and vine-laden forests from those logged-over forests without dense ferns and vines, as well as mapping their distributions, with the aid of Landsat-8 satellite imagery and machine learning modeling. During the process, we tested whether the gray-level co-occurrence matrix (GLCM) textures of Landsat data and Sentinel-1 C-band SAR data were helpful for this classification. Our study sites were Deramakot and Tangkulap Forest Reserves—commercial production forests in Sabah, Malaysian Borneo. First, we flew drones and obtained aerial images that were used as ground truth for the supervised classification. Subsequently, a machine-learning model with a gradient-boosting decision tree was iteratively tested in order to derive the best model for the classification of the vegetation. Finally, the best model was extrapolated to the entire forest reserve and used to map three classes of vegetation (fern thickets, vine-laden forests, and logged-over forests without ferns and vines) and two non-vegetation classes (bare soil and open water). The overall classification accuracy of the best model was 86.6%; however, by combining the fern and vine classes into the same category, the accuracy was improved to 91.5%. The GLCM texture variables were especially effective at separating fern/vine vegetation from the non-degraded forest, but the SAR data showed a limited effect. Our final vegetation map showed that 30.7% of the reserves were occupied by ferns or vines, which may lead to arrested succession. Considering that our study site was once certified as a well-managed forest, the area of degraded forests with a high risk of loss of resilience is expected to be much broader in other Bornean production forests.

Herbaceous species and dry forest species have more acquisitive leaf traits than woody species and wet forest species

“…These differences in leaf trait values may indicate their different roles in forest dynamics. Vine species can grow rapidly with their acquisitive trait values in (small) open areas such as recent abandoned land or canopy gaps, and grow on top of small trees, which hampers regeneration (Zhang et al., 2011), sometimes leading to arrested succession (Takeshige et al., 2022). In contrast, liana species can grow under resource‐limited conditions such as closed forests, and climb on larger trees and cast shade or physically damage the tree crown, thus reducing growth and survival of larger trees (Ingwell et al., 2010; Peña‐Claros et al., 2008).…”

“…Herbaceous life forms had higher resource acquisitive trait values (high PCA1, Figure 2a) with high leaf nutrient concentration (Figure 3a Therefore, such differences in leaf economic trait values between herbaceous and woody life forms in early succession may explain the life form replacement from herbaceous to woody in a changing environment during succession on abandoned agricultural land (Egler, 1954;Shipley et al, 2006;Tilman, 1985). which hampers regeneration (Zhang et al, 2011), sometimes leading to arrested succession (Takeshige et al, 2022). In contrast, liana species can grow under resource-limited conditions such as closed forests, and climb on larger trees and cast shade or physically damage the tree crown, thus reducing growth and survival of larger trees (Ingwell et al, 2010;Peña-Claros et al, 2008).…”

A regime shift in soil moisture triggered by selective logging in Bornean lowland tropical rainforests

“…Band 5 of Landsat 8) and shortwave-infrared (Band 7), respectively. This index has been broadly used for capturing land use and land cover change, and was proved efficient for identifying vegetation type, specifically in our study area that fern thickets and liana-laden forests rather than bare soil marked the degraded forest patches undergone severe disturbances (Ioki et al, 2022;Takeshige et al, 2022).…”

“…Contrarily, Tangkulap was under conventional logging until 2002, which formed extensive, severely degraded forest patches (Kitayama et al, 2018). All logging activities have been suspended in Tangkulap since 2003, yet highly degraded forests (fern thickets and liana-laden forests) still cover 35.1% of entire Tangkulap after two decades of logging (Takeshige et al, 2022).…”

Evolução e tendências do uso de Aeronaves Remotamente Pilotadas no Brasil (2017-2022) e suas implicações para o geoprocessamento

“…Embora muito bem documentado sua aplicação da agricultura (Barbosa et al, 2021;Shafi et al, 2020;Singh et al, 2022;Sosa et al, 2022) Propostas de aplicação mais adequadas à elevada resolução espacial dos dados derivados das RPAs surgem em momento posterior. Exemplo marcante refere-se às análises de vegetação agora embasada por mapeamentos de espécies (Luo et al, 2022;Takeshige et al, 2022). A resolução espacial é suficiente para identificar, por exemplo, a Rhizophora mangle, a Laguncularia racemosa e a Avicennia schaueriana em bosque de manguezal (Cao et al, 2018;Ruwaimana et al, 2018).…”