Estimate Tree Species Research Articles

Mapping tree species diversity in a typical natural secondary forest by combining multispectral and LiDAR data

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.

Estimating tree species composition from airborne laser scanning data using point-based deep learning models

Airborne laser scanning (ALS) data is increasingly being used for accurate estimations of forest inventory attributes, such as tree height and volume. However, determining tree species information, an important attribute required by inventories for a range of forest management applications, is challenging to extract from ALS data. This is due to complex species assemblages and vertical structures in certain forest environments, and a lack of spectral information for most ALS sensors. Our objective was to estimate tree species compositions in a Canadian boreal forest environment using ALS data and point-based deep learning techniques. To accomplish this, we utilised existing polygonal forest resource information to develop a large comprehensive dataset of tree species compositions across a 630,000 ha forest management area. We then used an adapted PointAugment generative adversarial network (GAN) coupled with the dynamic graph convolutional neural network to estimate tree species proportions. This innovative deep learning approach resulted in an overall R2adj of 0.61 for all tree species proportions. A weighted F1 score of 63% was observed when classifying the leading species of a plot, 66% for leading genus, and 85% when distinguishing between coniferous and deciduous dominated plots. Furthermore, the PointAugment GAN successfully generated augmented point clouds of forest plots which can alleviate issues associated with limited training samples for use in deep learning. This approach demonstrates the capability of point-based deep learning techniques to accurately estimate tree species compositions from ALS point clouds within an expansive forested region, characterized by a complex management history and a diversity of tree species. The source code along with the pretrained models are available at https://github.com/Brent-Murray/point-dl/tree/main/Pytorch/models/PointAugment.

Corrigendum: Remote sensing methods for estimating tree species of forests in the Volyn region, Ukraine

Corrigendum: Remote sensing methods for estimating tree species of forests in the Volyn region, Ukraine

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Forest ecosystems depend on species of tree variety. Remote sensing for obtaining large-scale spatial distribution information of tree species diversity is a geoscience research hotspot to overcome the limitations of conventional tree species diversity survey approaches. Airborne LiDAR or synergy with airborne optical imagery has been used to model and estimate tree species diversity for specific forest communities, with many revealing results. However, the data collection for such research is costly, the breadth of monitoring findings is limited, and obtaining information on the geographical pattern is challenging. To this end, we propose a method for mapping forest tree species diversity by synergy satellite optical remote sensing and satellite-based LiDAR based on the spectral heterogeneity hypothesis and structural variation hypothesis to improve the accuracy of the remote sensing monitoring of forest tree species diversity while considering data cost. The method integrates horizontal spectral variation from GF-1/PMS image data with vertical structural variation from ICESat-2 spot data to estimate the species diversity of trees. The findings reveal that synergistic horizontal spectral variation and vertical structural variation overall increase tree species diversity prediction accuracy compared to a single remote sensing variation model. The synergistic approach improved Shannon and Simpson indices prediction accuracy by 0.06 and 0.04, respectively, compared to the single horizontal spectral variation model. The synergistic model, single vertical structural variation model, and single horizontal spectral variation model were the best prediction models for Shannon, Simpson, and richness indices, with R2 of 0.58, 0.62, and 0.64, respectively. This research indicates the potential of synergistic satellite-based LiDAR and optical remote sensing in large-scale forest tree species diversity mapping.

Modeling tree species richness patterns and their environmental drivers across Hyrcanian mountain forests

Perception of the way tree species richness distribution varies along the elevational gradient is necessary for applying conservation strategies in favor of biodiversity hotspots. Hyrcanian Mountain Forests of Iran are among the most biodiverse forests in the world and are inscribed in the United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage List. Here, the Bayesian Additive Regression Tree (BART) and Stack-Species Distribution Model (S-SDMs) were used to understand tree diversity patterns of the Hyrcanian Mountain Forests. For all the tree species, we implemented the BART model with nine environmental variables, including six bioclimatic and three topographic variables, to create a habitat suitability map. We used the area under the curve (AUC), True skill statistic (TSS), type I error, and type II error to evaluate the BART model for each tree species. All models were then stacked to estimate tree species richness. Important variables affecting the distribution of tree species in northern Iran were different for each species but, in general, topographic variables such as slope and altitude played a very important role. BART model mapped all the 29 tree species distribution effectively with AUC 0.83–0.98, TSS 0.51–0.92; and low type I and type II errors. A very important point was the high ability of this model to predict species with low presence data. The achievement of this study was identifying the biodiversity hotspots based on the tree distribution pattern along the elevational gradient. We mapped regional richness at 1 km spatial resolution and it was the first effort to predict macroecological patterns in the Hyrcanian forests of Iran. The hotspots were mainly scattered from sea level and areas with 1000 m elevation, which were close to residential areas. In conclusion, these results help forest manager to plan new conservation strategies based on tree richness hotspots.

The use of dual-wavelength airborne laser scanning for estimating tree species composition and species-specific stem volumes in a boreal forest

The estimation of species composition and species-specific stem volumes are critical components of many forest inventories. The use of airborne laser scanning with multiple spectral channels may prove instrumental for the cost-efficient retrieval of these forest variables. In this study, we scanned a boreal forest using two channels: 532 nm (green) and 1064 nm (near infrared). The data was used in a two-step methodology to (1) classify species, and (2) estimate species-specific stem volume at the level of individual tree crowns. The classification of pines, spruces and broadleaves involved linear discriminant analysis (LDA) and resulted in an overall accuracy of 91.1 % at the level of individual trees. For the estimation of stem volume, we employed species-specific k-nearest neighbors models and evaluated the performance at the plot level for 256 field plots located in central Sweden. This resulted in root-mean-square errors (RMSE) of 36 m3/ha (16 %) for total volume, 40 m3/ha (27 %) for pine volume, 32 m3/ha (48 %) for spruce volume, and 13 m3/ha (87 %) for broadleaf volume. We also simulated the use of a monospectral near infrared (NIR) scanner by excluding features based on the green channel. This resulted in lower overall accuracy for the species classification (86.8 %) and an RMSE of 41 m3/ha (18 %) for the estimation of total stem volume. The largest difference when only the NIR channel was used was the difficulty to accurately identify broadleaves and estimate broadleaf stem volume. When excluding the green channel, RMSE for broadleaved volume increased from 13 to 26 m3/ha. The study thus demonstrates the added benefit of the green channel for the estimation of both species composition and species-specific stem volumes. In addition, we investigated how tree height influences the results where shorter trees were found to be more difficult to classify correctly.

Remote sensing methods for estimating tree species of forests in the Volyn region, Ukraine

Forest classification is needed to solve a wide range of environmental issues related to of forest classes and succession processes, the extent of afforestation and deforestation and global environmental change. These applications require a very accurate mapping and monitoring of forest types. This article investigates the combination of modern open geographic information systems and remote sensing data in forest management tasks for a specific part of the Ukrainian state area. Based on the existing afforestation plans, the results of the unsupervised classification of Sentinel-2 images and the selection of forest species fragments with closed crowns as training data for supervised classification, classifiers of forest species of the study object were developed with and without taking into account age groups. A supervised classification of research objects is realized and the accuracy of the obtained results is evaluated. It is established that the accuracy of determining forest species on the basis of the proposed method is 90.3 and 91.4%, taking into account age groups and without taking them into account, respectively. Thus, it is found that the modeling of the age groups does not improve the classification result for the test area.

Modelling Species Richness and Functional Diversity in Tropical Dry Forests Using Multispectral Remotely Sensed and Topographic Data

Efforts to assess and understand changes in plant diversity and ecosystem functioning focus on the analysis of taxonomic diversity. However, the resilience of ecosystems depends not only on species richness but also on the functions (responses and effects) of species within communities and ecosystems. Therefore, a functional approach is required to estimate functional diversity through functional traits and to model its changes in space and time. This study aims to: (i) assess the accuracy of estimates of species richness and tree functional richness obtained from field data and Sentinel-2 imagery in tropical dry forests of the Yucatan Peninsula; (ii) map and analyze the relationships between these two variables. We calculated species richness and functional richness (from six functional traits) of trees from 87 plots of the National Forest Inventory in a semi-deciduous tropical forest and 107 in a semi-evergreen tropical forest. Species richness and functional richness were mapped using reflectance values, vegetation indices, and texture measurements from Sentinel-2 imagery as explanatory variables. Validation of the models to map these two variables yielded a coefficient of determination (R2) of 0.43 and 0.50, and a mean squared relative error of 25.4% and 48.8%, for tree species richness and functional richness, respectively. For both response variables, the most important explanatory variables were Sentinel-2 texture measurements and spectral bands. Tree species richness and functional richness were positively correlated in both forest types. Bivariate maps showed that 44.9% and 26.5% of the forests studied had high species richness and functional richness values. Our findings highlight the importance of integrating field data and remotely sensed variables for estimating tree species richness and functional richness. In addition, the combination of species richness and functional richness maps presented here is potentially valuable for planning, conservation, and restoration strategies by identifying areas that maximize ecosystem service provisioning, carbon storage, and biodiversity conservation.

Comparison of Basal Area and Trees Abundance for Estimating Tree Diversity in Beech Forests (Case Study: Guilan, Masal, Northern Iran)

Measuring tree species diversity is necessary to manage forest resources sustainably and to better understand the economic consequences to changes in species diversity due to management. This research aimed at comparing the basal area and trees abundance for evaluating tree species diversity in oriental beech (Fagus orientalis Lipsky) forests (Guilan, Masal, Northern Iran). For this study, compartment 515 was selected, and then data were collected through the random-systematic method with a grid dimension of 150×150 m in an area of 50 ha, and lozenge shape with the sizes of 400; 800; 1,000; 1,200; 1,600; 2,000; 2,500; and 5,000 m2 was established. In total, 160 sampling plots were taken. In each plot, the diameter at breast height (DBH≥7.5 cm) of all trees was measured. Diversity and evenness indices were estimated applying different variables (the basal area and the trees abundance) separately. The results elucidated that the error percentage of Mac Arthur’s N1, Hill’s N2 indices, and Simpson's evenness, was lower using basal area compare to the trees abundance. The result of Camargo, Nee, and Smith-Wilson evenness indices revealed that the error percentage by using the trees abundance was lower than the amount of basal area. We recommend using the basal area for estimating tree species diversity.

Tree Species Classification Using High-Resolution Satellite Imagery and Weakly Supervised Learning

Knowing vegetation type in an area is crucial for several applications, including ecology, land use management, and infrastructure risk assessment. In combination with recent advancements in image processing, remote sensing technology has been used to perform fast vegetation type estimation and reduce the need for intensive and time-consuming field-based surveys. This paper proposes a weakly supervised method based on deep learning to estimate tree species relying on multi-spectral high-resolution satellite images. We tested the approach against noisy labels, which often occur in real-world datasets. We validate our approach for a study area in Norway and in Italy using images taken in different periods of the year. Our method significantly enhances the quality of the available forestry inventory dataset.

Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method

Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m × 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this “mini raster cell method” features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58°27’, E 13°39’). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots.

Which optical traits enable an estimation of tree species diversity based on the Spectral Variation Hypothesis?

AbstractQuestionWhich 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).

Patterns of tree species richness in Southwest China

As a region known for its high species richness, southwest China plays an important role in preserving global biodiversity and ensuring ecological security in the Yangtze, Mekong, and Salween river basins. However, relatively few studies focus on the response of tree species richness to climate change in this part of China. This study determined the main tree species in southwest China using the Vegetation Map of China and the Flora of China. From simulations of 1970 to 2000 and three forecasts of future benign, moderate, and extreme climate warming anticipated during 2061 to 2080, this study used a maximum entropy model (MaxEnt) to simulate main tree species richness in southwest China. Regions with a peak species richness at intermediate elevations were typically dominated by complex mountainous terrain, such as in the Hengduan Mountains. Likewise, regions with the smallest richness were low-elevation areas, including the Sichuan Basin, and the high-elevation Sichuan-Tibet region. Annual precipitation, minimum temperature of the coldest month, temperature seasonality, and elevation were the most critical factors in estimating tree species richness in southwest China. During future 2061 to 2080 climate scenarios, tree species tended to migrate towards higher elevations as mean temperatures increased. For climate change scenarios RCP2.6-2070 (benign) and RCP4.5-2070 (moderate), the main tree species richness in the study area changed little. During the RCP8.5-2070 extreme scenario, tree species richness decreased. This study provides useful guidance to plan and implement measures to conserve biodiversity.

Community and neighbourhood tree species richness effects on fungal species in leaf litter

Abstract Investigating the effects of individual tree species on fungal species in leaf litter allows a mechanistic understanding of how tree diversity affects the diversity and composition of fungal species at the community level. We collected freshly-fallen leaves of eight focal tree species at four tree species richness levels in a large-scale subtropical forest diversity experiment to estimate tree species richness effects on fungal species diversity and community composition at two spatial scales: at the local tree neighbourhood and at the tree community level. The identity of focal tree species affected both the diversity and composition of the fungal community in freshly-fallen leaves, particularly structuring the composition of both the pathogenic and non-pathogenic fungal community. Furthermore, we found that the effects of community tree species richness on fungal OTU composition were tree species-specific. Besides community tree species richness, the neighbour tree community had significant effects on the structure of the entire fungal community and of functional groups in freshly-fallen leaves. These findings highlight that the response of fungal species assemblages to tree species richness depends on fungal-associated tree species identity, and suggest that heterospecificity of local tree neighbours is an important driver of tree richness effects on litter fungal community.

Use of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa

We use remotely sensed data to estimate species diversity in Blouberg Nature Reserve (BNR) in the Limpopo province, South Africa to understand the state of biodiversity since communities’ involvement in conservation initiatives. To achieve this objective, Landsat series data collected before and after community involvement in biodiversity conservation were used in conjunction with selected diversity indices i.e., Shannon-Wiener Index (H’) and Simpson Index (D). Thirty 15 m × 15 m field plots were selected and all trees within each plot were identified, with the help of Botanists. Further, we applied regression analysis to determine the relationship between satellite derived tree species diversity and field observations. The results of the study demonstrated a significant (p < 0.5) variation in tree species diversity between 1990 and 2019. The highest relationship was obtained between H’ and the combined remotely sensed spectral data and as well as Indices (VIs) when compared to other derived satellite data. Further, the results showed positive significant relationship (p < 0.05) between the combined remotely sensed data and observed H’ index with r2 = 0.36 and r2 = 0.34 for the period before and after involving local communities in biodiversity conservation, respectively. Thus, the findings of the study indicate that the ecological condition of the reserve was slightly affected by the involvement of local communities in biodiversity conservation, for instance, volunteering in bush-encroachment eradication and decision-making. Overall, findings of the study underscore the relevance of remotely sensed data in assessing the ecological condition of protected areas and this information can help in decision-making.

Relating modelled habitat suitability for Abies balsamea to on-the-ground species structural characteristics in naturally growing forests

Abstract Modelling tree habitat suitability (HS) is a common practice to assess tree species distribution across a broad range of spatial scales. However, it is seldom used to test the extent that modelled HS-scores (probability of species occurrence) can represent on-the-ground measures of species-structural characteristics. In this study, we compare four parametric and non-parametric models generated with the R-package, sdm, to assess the potential for these models to estimate tree species distribution of balsam fir [Abies balsamea (L.) Mill.] in naturally-growing forests across an extensive landscape. Central to this development are inventory plot data of species presence-absence and four abiotic factors linked to plant growth and distribution. The study’s abiotic factors include: (1) photosynthetically active radiation; (2) growing degree-days; (3) relative extractable soil water content; and (4) near-surface wind speed, all expressed spatially at 30-m resolution. To gauge whether modelled HS can explain structural characteristics in balsam fir-dominated stands, field-based estimates of site index (SI) and cumulative aboveground biomass (ABG) were compared against independently-derived HS-scores. The results showed that: (i) random forest was the most successful at representing species distribution of balsam fir among the four methods considered; (ii) overall growing conditions for balsam fir was observed to be most favourable on north-facing slopes, particularly in the northwest part of the target landscape, where near-surface air temperatures are cooler, soils are moderately wetter, and wind speeds are lower; (iii) tree-based calculations of SI were partially characterised by patterns in modelled HS-scores, due to scale differences (i.e., from individual tree to 30-m grid cells) and an inadequate number of sample trees; and (iv) patterns of cumulative AGB were more accurately represented by species HS. Modelled HS-scores, as potential indicators of tree species habitat preference, AGB, and species distribution, can offer key ecological information essential to inform forest management and conservation planning at the landscape level.

Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis

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.

The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration

Ambitious pledges to restore over 400 million hectares of degraded lands by 2030 have been made by several countries within the Global Partnership for Forest Landscape Restoration (FLR). Monitoring restoration outcomes at this scale requires cost-effective methods to quantify not only forest cover, but also forest structure and the diversity of useful species. Here we obtain and analyze structural attributes of forest canopies undergoing restoration in the Atlantic Forest of Brazil using a portable ground lidar remote sensing device as a proxy for airborne laser scanners. We assess the ability of these attributes to distinguish forest cover types, to estimate aboveground dry woody biomass (AGB) and to estimate tree species diversity (Shannon index and richness). A set of six canopy structure attributes were able to classify five cover types with an overall accuracy of 75%, increasing to 87% when combining two secondary forest classes. Canopy height and the unprecedented “leaf area height volume” (a cumulative product of canopy height and vegetation density) were good predictors of AGB. An index based on the height and evenness of the leaf area density profile was weakly related to the Shannon Index of tree species diversity and showed no relationship to species richness or to change in species composition. These findings illustrate the potential and limitations of lidar remote sensing for monitoring compliance of FLR goals of landscape multifunctionality, beyond a simple assessment of forest cover gain and loss.

Estimating tree species diversity in the savannah using NDVI and woody canopy cover

Remote sensing applications in biodiversity research often rely on the establishment of relationships between spectral information from the image and tree species diversity measured in the field. Most studies have used normalized difference vegetation index (NDVI) to estimate tree species diversity on the basis that it is sensitive to primary productivity which defines spatial variation in plant diversity. The NDVI signal is influenced by photosynthetically active vegetation which, in the savannah, includes woody canopy foliage and grasses. The question is whether the relationship between NDVI and tree species diversity in the savanna depends on the woody cover percentage. This study explored the relationship between woody canopy cover (WCC) and tree species diversity in the savannah woodland of southern Africa and also investigated whether there is a significant interaction between seasonal NDVI and WCC in the factorial model when estimating tree species diversity. To fulfil our aim, we followed stratified random sampling approach and surveyed tree species in 68 plots of 90m×90m across the study area. Within each plot, all trees with diameter at breast height of >10cm were sampled and Shannon index − a common measure of species diversity which considers both species richness and abundance − was used to quantify tree species diversity. We then extracted WCC in each plot from existing fractional woody cover product produced from Synthetic Aperture Radar (SAR) data. Factorial regression model was used to determine the interaction effect between NDVI and WCC when estimating tree species diversity. Results from regression analysis showed that (i) WCC has a highly significant relationship with tree species diversity (r2=0.21; p<0.01), (ii) the interaction between the NDVI and WCC is not significant, however, the factorial model significantly reduced the error of prediction (RMSE=0.47, p<0.05) compared to NDVI (RMSE=0.49) or WCC (RMSE=0.49) model during the senescence period. The result justifies our assertion that combining NDVI with WCC will be optimal for biodiversity estimation during the senescence period.

Remote sensing of species diversity using Landsat 8 spectral variables

The application of remote sensing in biodiversity estimation has largely relied on the Normalized Difference Vegetation Index (NDVI). The NDVI exploits spectral information from red and near infrared bands of Landsat images and it does not consider canopy background conditions hence it is affected by soil brightness which lowers its sensitivity to vegetation. As such NDVI may be insufficient in explaining tree species diversity. Meanwhile, the Landsat program also collects essential spectral information in the shortwave infrared (SWIR) region which is related to plant properties. The study was intended to: (i) explore the utility of spectral information across Landsat-8 spectrum using the Principal Component Analysis (PCA) and estimate alpha diversity (α-diversity) in the savannah woodland in southern Africa, and (ii) define the species diversity index (Shannon (H′), Simpson (D2) and species richness (S) – defined as number of species in a community) that best relates to spectral variability on the Landsat-8 Operational Land Imager dataset. We designed 90 m × 90 m field plots (n=71) and identified all trees with a diameter at breast height (DbH) above 10 cm. H′, D2 and S were used to quantify tree species diversity within each plot and the corresponding spectral information on all Landsat-8 bands were extracted from each field plot. A stepwise linear regression was applied to determine the relationship between species diversity indices (H′, D2 and S) and Principal Components (PCs), vegetation indices and Gray Level Co-occurrence Matrix (GLCM) texture layers with calibration (n=46) and test (n=23) datasets. The results of regression analysis showed that the Simple Ratio Index derivative had a higher relationship with H′, D2 and S (r2=0.36; r2=0.41; r2=0.24 respectively) compared to NDVI, EVI, SAVI or their derivatives. Moreover the Landsat-8 derived PCs also had a higher relationship with H′ and D2 (r2 of 0.36 and 0.35 respectively) than the frequently used NDVI, and this was attributed to the utilization of the entire spectral content of Landsat-8 data. Our results indicate that: (i) the measurement scales of vegetation indices impact their sensitivity to vegetation characteristics and their ability to explain tree species diversity; (ii) principal components enhance the utility of Landsat-8 spectral data for estimating tree species diversity and (iii) species diversity indices that consider both species richness and abundance (H′ and D2) relates better with Landsat-8 spectral variables.