Investigating the Relationship between Tree Species Diversity and Landsat-8 Spectral Heterogeneity across Multiple Phenological Stages

Forests cover about 30% of the Earth surface, they are among the most biodiverse terrestrial ecosystems and represent the bulk of many ecological processes and services. The assessment of biodiversity is an important and essential goal to achieve but it can results difficult, time consuming and expensive when based on field data. Remote sensing covers large areas and provides consistent quality and standardized data, which can be used to estimate species diversity. One method to estimate species diversity from remote sensing data is based on the Spectral Variation Hypothesis (SVH), which assumes that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of the considered area. SVH has been tested using different spectral heterogeneity (SH) indices and measures, recently the Rao's Q index has been proposed as a new spectral variation measure to be applied to remote sensing data. In this paper, we tested the SVH in an alpine coniferous forest to estimate tree species diversity. We evaluated the performance of the Rao's Q index and compared it with another widely used SH index, the Coefficient of Variation (CV), validating them against values of Shannon's H (used as species diversity index) derived from in-situ collected data. A NDVI time-series (for 2016 and 2017) derived from the Sentinel-2A and 2B and Landsat 8 OLI satellites has been used to test the effect of the spatial grain of both the sensors and to understand the seasonality of the SVH. The results showed that the SVH is season and sensor dependent. For both years and satellites, the relation between Rao's Q and field data reached the highest R2 between June and July, decreasing towards winter and spring similarly to the NDVI time-series. This relationship could be given because, when NDVI reaches its highest values, it is able to capture small variation in reflectance of different leaf traits typical of specific trees. The relation between field and spectral diversity reached a value of R2 = 0.70 (2017) and R2 = 0.48 (2016) for Sentinel-2 and of R2 = 0.42 (2017) and R2 = 0.47 (2016) for Landsat 8. CV showed the same NDVI temporal tendency. However, the relation between field-derived Shannon's H and CV was on average lower than that we found for Rao's Q. This research underlined the goodness of the Rao's Q index, the relevance of the NDVI in the study of the SVH and the importance of the multi-temporal approach.

Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests.

Grassland species diversity monitoring is essential to grassland resource protection and utilization. “Spectral variation hypothesis” (SVH) provides a remote sensing method for monitoring grassland species diversity at pixel scale by calculating spectral heterogeneity. However, the pixel spectrum is easily affected by soil and other background factors in natural grassland. Unmanned aerial vehicle (UAV)-based imaging spectroscopy provides the possibility of soil information removal by virtue of its high spatial and spectral resolution. In this study, UAV-imaging spectroscopy data with a spatial resolution of 0.2 m obtained in two sites of typical alpine steppe within the Sanjiangyuan National Nature Reserve were used to analyze the relationships between four spectral diversity metrics (coefficient of variation based on NDVI (CVNDVI), coefficient of variation based on multiple bands (CVMulti), minimum convex hull volume (CHV) and minimum convex hull area (CHA)) and two species diversity indices (species richness and the Shannon–Wiener index). Meanwhile, two soil removal methods (based on NDVI threshold and the linear spectral unmixing model) were used to investigate the impact of soil on species diversity estimation. The results showed that the Shannon–Wiener index had a better response to spectral diversity than species richness, and CVMulti showed the best correlation with the Shannon–Wiener index between the four spectral diversity metrics after removing soil information using the linear spectral unmixing model. It indicated that the estimation ability of spectral diversity to species diversity was significantly improved after removing the soil information. Our findings demonstrated the applicability of the spectral variation hypothesis in natural grassland, and illustrated the impact of soil on species diversity estimation.

Accurate monitoring of tree species diversity is crucial for understanding the dynamic changes in tree species diversity and its relationships with other services and functions in forest ecosystems. Traditional optical remote sensing data have been widely used for monitoring tree species diversity based on the spectral variation hypothesis (SVH). However, this method cannot capture the three-dimensional structural variations in complex species compositions under different stand conditions. In this study, we modeled tree species diversity in terms of spectral variation and stand structural complexity in a typical natural secondary forest in Northeast China by combining Sentinel-2 data and UAV-borne light detection and ranging (LiDAR) point cloud data. First, species diversity indices (including the Shannon index H' and Simpson index D1) were derived from 60 field-measured plots. Second, recursive feature elimination (RFE) was utilized for feature filtering of ten spectral bands and four vegetation indices extracted from Sentinel-2 data and Rao's Q index, as well as eleven features extracted from LiDAR point clouds reflecting the complexity of the stand structure. Subsequently, the random forest method was utilized to fit and predict the relationship between the remote sensing feature set and tree species diversity. The results showed that the use of multisource remote sensing feature set to estimate tree species diversity had the highest accuracy (R2 = 0.44, RMSE = 0.28 for H') compared to the use of only one data source. Moreover, when using a single remote sensing feature set, the estimation accuracy of the optical remote sensing feature set is higher than that of the LiDAR feature set for H' and D1, and the NIRv is the most influential spectral feature. This study clarified the value of spectral variation and productivity heterogeneity embodied in optical remote sensing features for monitoring tree species diversity, as well as evaluating the shortcomings and possibilities of using LiDAR point cloud features independently, and fully confirmed the positive significance of complementary effects between multisource remote sensing feature sets.

Satellite remote sensing data is essential for large-scale, timely, and repeatable monitoring of forest species diversity. While various methods have been applied to satellite-based diversity estimation at regional scales, selecting suitable sensor and monitoring period remains challenging, especially in tropical forests. This study aims to identify the optimal time window, spatial resolution, and metrics for species diversity estimation in the Jianfengling tropical forest in southern China. We constructed stepwise linear regression models for estimating Richness, Simpson, and Shannon-Wiener indices using in-situ species diversity and heterogeneity metrics of spectra and structure. For analyzing phenology influence, we utilized six Sentinel-2 images acquired bimonthly from January to November. For evaluating scale dependency, we resampled the GF2 image to five spatial resolutions ranging from 0.8 to 10 m. The results indicated that the suitable phenological periods for species diversity estimation were at the beginning and end of the growing season, especially September performing the best for all diversity indices. Among four types of heterogeneity metrics, spectral information consistently explained most variance in species diversity indices across all periods. The optimal spatial resolution for estimating Richness and Shannon-Wiener index was 4–5 m, which corresponded to the average tree crown size. The texture features made a significant contribution compared to other metrics. Our study highlights that species diversity monitoring is highly dependent on the spatiotemporal scales of remote sensing data. It may offer practical guidance for selecting appropriate data and methods for species diversity monitoring in tropical forests.

QuestionWhich optical traits, retrieved from biophysical models applied to Sentinel‐2 images, enable an estimation of tree species diversity based on the Spectral Variation Hypothesis?LocationConiferous mountain forest in the eastern Italian Alps.MethodsWe analyzed the PROSPECT‐5 and Invertible Forest Reflectance Model (INFORM) biophysical parameters as retrieved from canopy reflectance data of different forest plots (using Sentinel‐2 images for the years 2017, 2018 and 2019) as optical trait indicators (OTIs). We successively tested the Spectral Variation Hypothesis (SVH) for each retrieved OTI using the Rao's Q as heterogeneity index, validating them against Shannon's H values calculated as a tree species diversity index derived from in‐situ collected data.ResultsWe demonstrated differences among OTIs in terms of how well their variations can be linked to species diversity. In particular the variations of brown pigments (Cbrown), carotenoids (Car) and chlorophyll content (Cab) can be considered the most relevant OTIs for the application of the SVH when using the Rao's Q as a proxy for tree species diversity in our study site.ConclusionsThis research underlined that the OTIs contribute differently in the SVH to estimate tree species diversity, highlighting significant positive correlations between tree species diversity and the spatial heterogeneity of the estimated pigment content (Cab, Car, Cbrown).

BackgroundThe homestead forests of Bangladesh occupy 0.27 million hectares (10% of the total forested area) and have potential to store carbon (C) and conserve biodiversity. Small scale forestry practices, however, are lacking reliable estimation of C stocks and tree species diversity. This may hinder successful implementation of REDD + and similar mechanisms as they concentrate on large-scale forests. This study aimed to estimate the above- and below-ground carbon stocks in homestead forests of Maheshkhali Island in Bangladesh and how tree species diversity and stand structural variation affect these C stocks. We randomly surveyed a total of 239 homestead forests in the hillside, beachside, and inland in 2019.ResultsTree biomass C stocks were 48–67% greater in the inland and hillside forests than in the beachside due to significantly greater stand density, basal area, tree diameter. In total we found 52 tree species, but most abundant species in the inland and hillside forests, Mangifera indica, Samanea saman, and Artocarpus heterophyllus stored the most C in tree biomass. Greater tree species richness and diversity index in the inland and hillside forests indicated greater above- and below-ground tree biomass C stocks. An increase in tree species richness and diversity index by one unit was found to increase the tree biomass C stock by 22 and 30 Mg C ha−1, respectively. The total soil C stock was also affected by tree species diversity, stand density, and their interaction with soil properties. Total soil C stocks were greatest (51 Mg ha−1) in the inland forests, having also the greatest stand density and tree species richness. C stock in soil surface was greatest in the hillside forests due to the greatest litterfall, but the average share of litterfall from the total biomass C was only 0.1%.ConclusionsHomestead forest ecosystems could store 96 Mg C ha−1 in total, which can contribute to climate change mitigation by generating C credits for small-scale homestead forests owners. Above- and below-ground tree biomass C stocks were found to correlate with tree species diversity, which may also contribute to biodiversity conservation in the REDD + in Bangladesh and countries alike.

This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m × 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH ≥ 5 cm), tree height, and each plot’s GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon–Wiener (H′), Simpson diversity index (D2), species richness (S), and species evenness (J′). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI—normalized difference vegetation index, EVI—enhanced vegetation index, SRI—simple ratio index, SAVI—soil adjusted vegetation index, and NDRE—normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H′) due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study’s findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.

In this study, plant species diversity in two urban parks of Kio and Shariati in Khorramabad County were compared. A number of 30 sample plots were sampled from each park using systematic random sampling technique and in each sample plot characteristics of plant species, number of species, diameter at breast height of all trees, height of all trees and shrubs as well as large and small diameters of crown were measured. Simpson's diversity and evenness indices as well as Shannon Hill, and Brillouin diversity indices were used to assess plant diversity. Species richness was calculated using the number of species in each sample plot. Species diversity indices were calculated using the software Metadology Ecologycal and the t test was used to assess the significance of differences of these two indexes between the Kio and Shariati Parks. The results show that Kio Park had the higher average Shannon (1.514), Simpson (0.695), Hill (2.854) and Briloin (1.061) diversity indices indicating higher species diversity than Shariati Park. Also this park also has the higher Simpson evenness index (0.792) and the species richness (3.633), which indicates a better distribution and abundance of species in the park. The difference between averages of diversity indices at confidence level of 1% and the difference between averages of the evenness index at 5% were significant.

In order to have a more comprehensive understanding of the structural change and succession process of plant community in Haikou City, and predict the composition of its future change trend. A total of 235 randomly selected urban forest plots were studied in a built-up area of Haikou City. Structural characteristics and their corresponding plant diversity indexes were determined with a correlation analysis. Results indicated that (1) The presence of 283 species of common plants in the built-up area belonging to 218 genera of 72 families with 114 native species and 169 exotic species. (2) The dominant plants included Cocos nucifera, Pterocarpus indicus, Roystonea regia, and Ficus benjamina. (3) The diameter presented a distribution pattern of small, and the overall tree height was low with an average height of 6.8 m. (4) There was a significant correlation between species diversity and functional diversity indexes (P < 0.05). (5) Also, the phylogenetic diversity index (PD) was significantly correlated (P < 0.05) with species diversity indexes in the tree layer and herb layer. The phylogenetic species variability index (PSV) was not correlated with most species diversity indexes and functional diversity indexes, but the phylogenetic species variability evenness (PSE) index was significantly correlated with most of them (P < 0.05). Haikou City having a large improvement potential in forest development, should avoid the homogeneity phenomenon where native species have been adopted in urban forest development; to ensure comprehensiveness and accuracy, Haikou should consider multi-level and targeted diversity indexes and functional trait parameters when evaluating urban plant diversity.

Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery

Biodiversity monitoring in the Niger delta has become pertinent in view of the incessant spillages from oil production activities and the socio-economic impact of these spillages on the inhabitants who depend on the resources for their livelihood. Conventional methods of post-impact assessments are expensive, time consuming, and cause damage to the environment, as they often require the removal of affected samples/specimens for laboratory analysis. Remote sensing offers the opportunity to track biodiversity changes from space while using the spectral variability hypothesis (SVH). The SVH proposes that the species diversity of a sampled area is linearly correlated with the variability of spectral reflectance of the area. Several authors have tested the SVH on various land cover types and spatial scales; however, the present study evaluated the validity of the SVH against the backdrop of oil pollution impact on biodiversity while using vascular plant species as surrogates. Species richness and diversity indices were computed from vegetation data collected from polluted and non-polluted transects. Spectral metrics that were derived from Sentinel 2 bands and broadband vegetation indices (BVIs) using various algorithms, including averages, spread, dimension reduction, and so on, were assessed for their ability to estimate vascular plants species richness and diversity. The results showed significant differences in vegetation characteristics of polluted and control transects (H = 76.05, p-value = &lt;0.05 for abundance and H = 170.03, p-value &lt; 0.05 for richness). Spectral diversity metrics correlated negatively with species data on polluted transects and positively on control transects. The metrics computed using Sentinel 2A bands and vegetation indices proved to be sensitive to changes in vegetation characteristics following oil pollution. The most robust relationship was observed between the metrics and indices on control transects, whereas the weakest relationships were observed on polluted transects. Index-wise, the Simpson’s diversity index regressed better with spectral metrics (R2 &gt; 0.5), whereas the Chao-1 richness index regressed the least (R2 &lt; 0.5). The strength of the relationship resulted in successfully estimating species richness and diversity values of investigated transects, thereby enhancing biodiversity monitoring over time and space.

Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data

As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large-scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at a fine scale in an ecological network (EN) embedded in a complex landscape has been tested using two new and promising methodological approaches: the first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao’s Quadratic heterogeneity measure (Rao’s Q). Both approaches are available thanks to two brand-new R packages: “biodivMapR” and “rasterdiv”. Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and monitor over time floristic diversity maintained in an EN selected as an example and located in northeast Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones, reaching a value of R2 = 0.36 and R2 = 0.43, respectively. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. Finally, the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion.

Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems