A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest

Abstract

Biodiversity monitoring and understanding ecological processes on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.

Effects of Spatial Resolution, Mapping Window Size, and Spectral Species Clustering on Remote Sensing of Plant Beta Diversity Using biodivMapR and Hyperspectral Imagery

“…The next step partitioned the selected components into a predefined number of clusters (spectral species) by using k‐means clustering and assigned a cluster ID to each pixel. We used 50 spectral species as most often done in other studies (Chraibi et al., 2022; Féret and de Boissieu, 2020; Gastauer et al., 2022; Liccari et al., 2022; Zhang et al., 2023) and then 20 spectral species in separate analyses for comparison of results. This range of spectral species approximately represents that found to be useful in other studies (Féret and de Boissieu, 2020; Sagang et al., 2022).…”

“…At coarser spatial resolutions, pixels may be clustered according to their “optical types” (Ustin & Gamon, 2010) representing local community characteristics, including species composition and community structure, instead of individual plant species (Féret & Asner, 2014; Rocchini et al., 2022). Thus, the degree of similarity in biodiversity between two areas of interest might be indicated by the similarity in such spectral species generated from mixed pixels (Chraibi et al., 2022; Liccari et al., 2022; Rocchini et al., 2021; Sagang et al., 2022). In this light, biodivMapR might effectively detect plant beta diversity using 30–45 m spatial resolution images recommended for the SBG mission and 30–60 m range currently available from existing hyperspectral satellite missions (e.g., PRISMA, DESIS, EMIT, EnMAP, HISUI).…”

“…This method depends on the assumption that local variation in spectral signatures among pixels corresponds to some combination of plant species diversity, phylogenetic diversity, functional diversity, and other environmental factors (i.e., the Spectral Variation Hypotheses; Palmer et al., 2002, Rocchini et al., 2004; Gamon et al., 2020). However, as the size of pixels increasingly exceeds the size of individual plant species, the assumption that spectral species relate to characteristics of individual plants quickly becomes untenable (Chraibi et al., 2022; Féret & Asner, 2014; Féret and de Boissieu, 2020; Gamon et al., 2020; Gholizadeh et al., 2019; Wang et al., 2018). Correlations between spectral diversity and plant species diversity, though often greatly underestimating the latter, have been detected at spatial resolutions ranging up to 30 m (Gamon et al., 2020; Gastauer et al., 2022; Gholizadeh et al., 2019; Kishore et al., 2023; Liccari et al., 2022; Rocchini, 2007; Zhang et al., 2023).…”

Using spectral diversity and heterogeneity measures to map habitat mosaics: An example from the Classical Karst

“…Following this approach, each pixel of the image is assigned to a spectral species, and metrics of α‐ and β‐diversity are inferred from the variation in spectral species (Féret & de Boissieu, 2020). So far, this method has been applied to tropical forests, based on very high‐resolution airborne imaging spectroscopy (2 m/pixel; Féret & Asner, 2014), to low‐resolution MODIS images of Europe (500 m/pixel; Rocchini, Salvatori, et al., 2021), and recently also to Sentinel‐2 data (10 m/pixel), in secondary forests (Chraibi et al., 2021) and in an ecological network (Liccari et al., 2022).…”

Assessing Spatio-Temporal Change and Dynamics of Forest Ecosystem Succession Using Patch Analysis

“…As ecosystems confront rising anthropogenic impacts, it is critical that we figure out how to better monitor SFS. It may be determined using a variety of methodologies based on RS (Çakır et al, 2007b;Chraibi et al, 2021;Osińska-Skotak et al, 2021). RS and GIS have the ability to aid in the study of ecological processes at the landscape level in forest ecosystem (Sivrikaya, 2002;Akay et al, 2012).…”