Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Somers, Ben; Asner, Gregory P.; Martin, Robert E.; Anderson, Christopher B.; Knapp, David; Wright, S. Joseph; Kerchove, Ruben Van De

doi:10.1016/j.rse.2015.04.016

Cited by 27 publications

(13 citation statements)

References 73 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Overall, this comparison provides compelling evidence that high‐fidelity imaging spectroscopy can be used to understand the spatial organization of biodiversity in hyperdiverse tropical forests. Our results show highly significant linear relationship between spectrally derived and plot‐based estimates of beta‐diversity consistent with previous studies that have used similar unsupervised approaches (Baldeck & Asner, ; Féret & Asner, ; Somers et al, ). Importantly, this strong relationship is preserved across plots using both 2‐ and 10‐cm‐diameter cut‐offs.…”

Section: Discussionsupporting

confidence: 91%

“…However, current satellite based multispectral sensors (e.g., Landsat) lack the spatial and spectral resolution required to sufficiently differentiate the high species-level diversity occurring within tropical forests (Rocchini et al, 2016;Rocchini, 2007aRocchini, , 2007b. Recent advances in high-fidelity, laser-guided imaging spectroscopy present a viable solution, and have been used successfully to estimate beta-diversity in Neotropical forests (Féret & Asner, 2014a, 2014bSomers et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities

et al. 2018

Self Cite

View full text Add to dashboard Cite

The forests of Amazonia are among the most biodiverse on Earth, yet accurately quantifying how species composition varies through space (i.e., beta‐diversity) remains a significant challenge. Here, we use high‐fidelity airborne imaging spectroscopy from the Carnegie Airborne Observatory to quantify a key component of beta‐diversity, the distance decay in species similarity through space, across three landscapes in Northern Peru. We then compared our derived distance decay relationships to theoretical expectations obtained from a Poisson Cluster Process, known to match well with empirical distance decay relationships at local scales. We used an unsupervised machine learning approach to estimate spatial turnover in species composition from the imaging spectroscopy data. We first validated this approach across two landscapes using an independent dataset of forest composition in 49 forest census plots (0.1–1.5 ha). We then applied our approach to three landscapes, which together represented terra firme clay forest, seasonally flooded forest and white‐sand forest. We finally used our approach to quantify landscape‐scale distance decay relationships and compared these with theoretical distance decay relationships derived from a Poisson Cluster Process. We found a significant correlation of similarity metrics between spectral data and forest plot data, suggesting that beta‐diversity within and among forest types can be accurately estimated from airborne spectroscopic data using our unsupervised approach. We also found that estimated distance decay in species similarity varied among forest types, with seasonally flooded forests showing stronger distance decay than white‐sand and terra firme forests. Finally, we demonstrated that distance decay relationships derived from the theoretical Poisson Cluster Process compare poorly with our empirical relationships. Synthesis. Our results demonstrate the efficacy of using high‐fidelity imaging spectroscopy to estimate beta‐diversity and continuous distance decay in lowland tropical forests. Furthermore, our findings suggest that distance decay relationships vary substantially among forest types, which has important implications for conserving these valuable ecosystems. Finally, we demonstrate that a theoretical Poisson Cluster Process poorly predicts distance decay in species similarity as conspecific aggregation occurs across a range of nested scales within larger landscapes.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, % cover was regarded as one important factor that potentially affects species richness predictions [44]. Second, species richness is a common indicator of community complexity [45], also due to different species generally varying in their morphological traits. These two conditions were separated into three classes based on values: 0-25, 26-40, and 40-100%, therefore representing low, moderate, and high % cover of the community and 1-3, 4-6, and 7-12 (species richness) as low, moderate, and high community complexity, respectively.…”

Section: The Methods For Identification Of the Best Indicesmentioning

confidence: 99%

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

et al. 2019

View full text Add to dashboard Cite

Numerous spectral indices have been developed to assess plant diversity. However, since they are developed in different areas and vegetation type, it is difficult to make a comprehensive comparison among these indices. The primary objective of this study was to explore the optimum spectral indices that can predict plant species richness across different communities in sandy grassland. We use 7339 spectral indices (7217 we developed and 122 that were extracted from literature) to predict plant richness using a two-year dataset of plant species and spectra information at 270 plots. For this analysis, we employed cluster analysis, correlation analysis, and stepwise linear regression. The spectral variability within the 420–480 nm and 760–900 nm ranges, the first derivative value at the sensitive bands, and the normalized difference at narrow spectral ranges correlated well with plant species richness. Within the 7339 indices that were investigated, the first-order derivative values at 606 and 583 nm, the reflectance combinations on red bands: (R802 − R465)/(R802 + R681) and (R750 − R550)/(R750 + R550) showed a stable performance in both the independent calibration and validation datasets (R2 > 0.27, p < 0.001, RMSE < 1.7). They can be regarded as the best spectral indices to estimate plant species richness in sandy grasslands. In addition to these spectral variation indices, the first derivative values or the normalized difference of the sensitive bands also reflect plant diversity. These results can help to improve the estimation of plant diversity using satellite-based airborne and hand-held hyperspectral sensors.

show abstract

“…Airborne field campaigns with various generations of the Airborne Visible/InfraRed imaging Spectrometer (AVIRIS) sensor, such as the AVIRIS Classic and the AVIRIS-NG, have provided good examples of the power of imaging spectrometry to detect vegetation function. Many of these campaigns have assessed plant function through photosynthesis, water status (Fuentes et al 2006;Serbin et al 2015;Somers et al 2015;Asner et al 2016a), or nutrient status (Asner and Vitousek 2005). The HyPlant airborne sensor (Rascher et al 2015), developed in preparation for the forthcoming FLEX satellite mission (Kraft et al 2013;Drusch et al 2017), with an expected launch in late 2022, provides very high spectral resolution radiance or reflectance suitable to detect chlorophyll fluorescence and foliar pigment content, both powerful indicators of photosynthetic activity (Rossini et al 2015;Simmer et al 2015;Wieneke et al 2016;Middleton et al 2017).…”

Section: Airborne Remote Sensingmentioning

confidence: 99%

Assessing Vegetation Function with Imaging Spectroscopy

et al. 2019

View full text Add to dashboard Cite

Healthy vegetation function supports diverse biological communities and ecosystem processes, and provides crops, forest products, forage, and countless other benefits. Vegetation function can be assessed by examining dynamic processes and by evaluating plant traits, which themselves are dynamic. Using both trait-based and process-based approaches, spectroscopy can assess vegetation function at multiple scales using a variety of sensors and platforms ranging from proximal to airborne and satellite measurements. Since spectroscopic data are defined by the instruments and platforms available, along with their corresponding spatial, temporal and spectral scales, and since these scales may not always match those of the function of interest, consideration of scale is a necessary focus. For a full understanding of vegetation processes, combined (multi-scale) sampling methods using empirical and theoretical approaches are required, along with improved informatics.

show abstract

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Cited by 27 publications

References 73 publications

Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities

Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

Assessing Vegetation Function with Imaging Spectroscopy

Contact Info

Product

Resources

About