Remotely sensed landscape heterogeneity as a rapid tool for assessing local biodiversity value in a highly modified New Zealand landscape

The widespread conversion of natural habitats to agricultural land has created a need to integrate intensively managed landscapes into conservation management priorities. However, there are no clearly defined methods for assessing the conservation value of managed landscapes at the local scale. We used remotely sensed landscape heterogeneity as a rapid practical tool for the assessment of local biodiversity value within a predominantly agricultural landscape in Canterbury, New Zealand. Bird diversity was highly significantly correlated with landscape heterogeneity, distance from rivers and the Christchurch central business district, altitude and average annual household income, indicating that remotely sensed landscape heterogeneity is a good predictor of local biodiversity patterns. We discuss the advantages and limitations of using geographic information systems to determine local areas of high conservation value.     

Can we predict butterfly diversity along an elevation gradient from space?

An important challenge in ecology is to predict patterns of biodiversity across eco‐geographical gradients. This is particularly relevant in areas that are inaccessible, but are of high research and conservation value, such as mountains. Potentially, remotely‐sensed vegetation indices derived from satellite images can help in predicting species diversity in vast and remote areas via their relationship with two of the major factors that are known to affect biodiversity: productivity and spatial heterogeneity in productivity. Here, we examined whether the Normalized Difference Vegetation Index (NDVI) can be used effectively to predict changes in butterfly richness, range size rarity and beta diversity along an elevation gradient. We examined the relationship between butterfly diversity and both the mean NDVI within elevation belts (a surrogate of productivity) and the variability in NDVI within and among elevation belts (surrogates for spatial heterogeneity in productivity). We calculated NDVI at three spatial extents, using a high spatial resolution QuickBird satellite image. We obtained data on butterfly richness, rarity and beta diversity by field sampling 100 m quadrats and transects between 500 and 2200 m in Mt Hermon, Israel. We found that the variability in NDVI, as measured both within and among adjacent elevation belts, was strongly and significantly correlated with butterfly richness. Butterfly range size rarity was strongly correlated with the mean and the standard deviation of NDVI within belts. In our system it appears that it is spatial heterogeneity in productivity rather than productivity per se that explained butterfly richness. These results suggest that remotely‐sensed data can provide a useful tool for assessing spatial patterns of butterfly richness in inaccessible areas. The results further indicate the importance of considering spatial heterogeneity in productivity along elevation gradients, which has no lesser importance than productivity in shaping richness and rarity, especially at the local scale.     

Modelling avian biodiversity using raw,unclassified satellite imagery

Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

The relationship between the spectral diversity of satellite imagery,habitat heterogeneity,and plant species richness

Assessment of habitat heterogeneity and plant species richness at the landscape scale is often based on intensive and extensive fieldwork at great cost of time and money. We evaluated the use of satellite imagery as a quantitative measure of the relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness. A 16 km² portion of a military training area in Germany was systematically sampled by plant taxonomic experts on a grid of one hundred 1-ha plots. The diversity of disturbance types, resulting habitat heterogeneity, and plant species richness were determined for each plot. Using an IKONOS multispectral satellite image, we examined 168 metrics of spectral diversity as potential indicators of those independent variables. Across all potential relationships, a simple count of values per spectral band per plot, after compressing the data from the original 11-bit format with 2048 potential values per band into a maximum of 100 values per band, resulted in the most consistent predictor for various metrics of habitat heterogeneity and plant species richness. The count of values in the green band generally out-performed the other bands. The relationship between spectral diversity and plant species richness was stronger than for measures of habitat heterogeneity. Based on the results, we conclude that remotely sensed assessment of spectral diversity, when coupled with limited ground-truthing, can provide reasonable estimates of habitat heterogeneity and plant species richness across broad areas.     

Using satellite imagery to assess plant species richness: The role of multispectral systems

Question: The use of variations in the spectral responses of remotely sensed images was recently proposed as an indicator of plant species richness (Spectral Variation Hypothesis, SVH). In this paper we addressed the issue of the potential use of multispectral sensors by testing the hypothesis that only some of the bands recorded in a remotely sensed image contain information related to the variation in species richness. Location: Montepulciano Lake, central Italy. Methods: We assessed how data compression techniques, such as Principal Component Analysis (PCA), influence the relationship between spectral heterogeneity and species richness and evaluated which spectral interval is the most adequate for predicting species richness by means of linear regression analysis. Results: The original multispectral data set and the first two non-standardized principal components can both be used as predictors of plant species richness (R²∼ 0.48; p < 0.001), confirming that PCA is an effective tool for compressing multi-spectral data without loss of information. Using single spectral bands, the near infrared band explained 41% of variance in species richness (p < 0.01), while the visible wavelengths had much lower prediction powers. Conclusions: The potential of satellite data for estimating species richness is likely to be due to the near infrared bands, rather than to the visible bands, which share highly redundant information. Since optimal band selection for image processing is a crucial task and it will assume increasing importance with the growing availability of hyperspectral data, in this paper we suggest a ‘near infrared way’for assessing species richness directly from remotely sensed data.     

Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity?

Different approaches for the assessment of biodiversity by means of remote sensing were developed over the last decades. A new approach, based on the spectral variation hypothesis, proposes that the spectral heterogeneity of a remotely sensed image is correlated with landscape structure and complexity which also reflects habitat heterogeneity which itself is known to enhance species diversity. In this context, previous studies only applied species richness as a measure of diversity. The aim of this paper was to analyze the relationship of richness and abundance-based diversity measures with spectral variability and compare the results at two scales. At three different test sites in Central Namibia, measures of vascular plant diversity was sampled at two scales – 100 m² and 1000 m². Hyperspectral remote sensing data were collected for the study sites and spectral variability, was calculated at plot level. Ordinary least square regression was used to test the relationship between species richness and the abundance-based Shannon Index and spectral variability. We found that Shannon Index permanently achieved better results at all test sites especially at 1000 m², Even when all sites where pooled together, Shannon Index was still significantly related with spectral variability at 1000 m². We suggest incorporating abundance-based diversity measures in studies of relationships between ecological and spectral variability. The contribution made by the high spectral and spatial resolution of the hyperspectral sensor is discussed.     

Forest fragmentation and its impact on species diversity: an analysis using remote sensing and GIS

The process of forest fragmentation, a common phenomenon occurring in tropical forests, not only results into continuously forest getting fragmented but also brings about several physical and biological changes in the environment of forests. Consequently, there is a loss of biodiversity due to change in habitat conditions. These remnant fragments provide the last hope for biodiversity conservation. The present study deals with the impact of decreasing patch size of a fragmented forest on the diversity of the tropical dry deciduous forests in Vindhyan highlands, India. There is considerable change in the vegetation cover of this region owing to rapid industrialization and urbanization, which has also contributed to forest fragmentation. In the present study, remotely sensed data has been used to describe the changes brought about in vegetated areas over a period of 10 years as a result of fragmentation and its impact on biodiversity was assessed. Further, in order to assess the loss of species with respect to the reduction in patch size, species area curves for various change areas were analysed. It was observed that the rate of decrease in the number of species is faster in the case of negative change areas as compared to the positive change areas of the region. Various diversity indices also support this observation. Such an analysis would help in formulating appropriate conservation measures for the region.     

Calculating landscape diversity with information-theory based indices: A GRASS GIS solution

The assessment of species diversity in relatively large areas has always been a challenging task for ecologists, mainly because of the intrinsic difficulty to judge the completeness of species lists and to undertake sufficient and appropriate sampling. Since the variability of remotely sensed signal is expected to be related to landscape diversity, it could be used as a good proxy of diversity at species level.It has been demonstrated that the relation between species and landscape diversity measured from remotely sensed data or land use maps varies with scale. However, Free and Open Source tools (allowing an access to the source code) for assessing landscape diversity at different spatial scales are still lacking today. In this paper, we aim at: i) providing a theoretical background of the mostly used diversity indices stemmed from information theory that are commonly applied to quantify landscape diversity from remotely sensed data and ii) proposing a free and robust Open Source tool (r.diversity) with its source code for calculating diversity indices (and allowing an easy potential implementation of new metrics by multiple contributors globally) at different spatial scales from remotely-sensed imagery or land use maps, running under the widely used Open Source program GRASS GIS.r.diversity can be a valuable tool for calculating landscape diversity in an Open Source space given the availability of multiple indices at multiple spatial scales with the possibility to create new indices directly reusing the code.We expect that the subject of this paper will stimulate discussions on the opportunities offered by Free and Open Source Software to calculate landscape diversity indices.     

High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail

While high resolution satellite remote sensing has been hailed as a very useful source of data for biodiversity assessment and monitoring, applications have been more developed in temperate areas. The biodiverse tropics offer a challenge of an altogether different magnitude for hyperspatial and hyperspectral remote sensing. This paper examines issues related to hyperspatial and hyperspectral remotely sensed imagery, which constitutes one of the most potentially powerful yet underutilized sources of for tropical research on biodiversity. Hyperspatial data with their increased pixel resolution are possibly best suited at facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity, particularly when spatial resolution becomes too fine and pixels are smaller than the size of the object (e.g., tree canopy) being identified. Hyperspectral data on the other hand, with their high spectral resolution, can be used to record information pertaining to a range of critical plant properties related to species identity, and can be very effective used for discriminating tree species in tropical forests, despite the greater complexity of such environments. There remains a glaring gap in the easy availability of hyperspectral and hyperspatial satellite data in the tropics due to reasons of cost, data coverage, and security restrictions. Stimulating discussion on the applications of this powerful, but underutilized tool by ecologists, is the first step in promoting a more extensive use of such data for ecological studies in tropical biodiversity rich areas.     

Patterns of bird diversity and habitat use in mixed vineyard-matorral landscapes of Central Chile

The Mediterranean climate region of central Chile is rich in biodiversity and contains highly productive agricultural lands, which creates challenges for the preservation of natural habitats and native biodiversity. Ecological data and studies for the region are also limited, making informed conservation in agricultural landscapes difficult. The increasing availability of remotely sensed data provide opportunities to relate species occurrences to measures of landscape heterogeneity even when field measures of habitat structure are lacking. When working with such remotely sensed data, it’s important to select appropriate measures of heterogeneity, including common metrics of landscape composition as well as frequently overlooked shape metrics. In this contribution we combine bird surveys with multispectral satellite imagery to develop boosted regression tree models of avian species richness, and of habitat use for 15 species across a mixed vineyard-matorral landscape in central Chile. We found a range of associations between individual species and land cover types, with the majority of species occurring most frequently in remnant habitats and ecotones rather than the interiors of large vineyard blocks. Models identified both metrics of landscape composition and patch shape as being important predictors of species occurrence, suggesting that shape metrics can complement more commonly used metrics of landscape composition. Vineyards that include corridors or islands of remnant habitat among vine blocks may increase the amount of area available to many species, although some species may still require large tracts of intact natural habitat to persist.     

Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing

Aim Conservation activities have increasingly focused on issues at the level of the landscape but are constrained by limited data and knowledge relating to biodiversity at this scale. Satellite remote sensing has considerable, but under‐exploited, potential as a source of information on biodiversity at the landscape level. Remote sensing has generally been used to assess biodiversity indirectly, using approaches that often fail to fully exploit the information content of the imagery and typically only with regard to the species richness component of biodiversity. The aim of this paper was to assess the potential of remote sensing as a source of information on the richness, evenness and composition of tree species in a tropical rain forest. Location The test site was a c. 225 km² region centred on the Danum Valley Field Centre, Borneo. This test site contained regions of undisturbed and differentially logged rain forest. Methods Data on tree biodiversity had been acquired for fifty‐two sample plots by standard field survey methods and were used to derive summary indices of biodiversity for seedlings, saplings and mature trees. Differences between logged and unlogged sites were evaluated by comparison of the indices and species accumulation curves. A Landsat Thematic Mapper (TM) image of the site acquired close to the date of the field survey was obtained and rigorously pre‐processed. Feedforward neural networks were used to derive predictions of biodiversity indices from the imagery. A Kohonen self organizing map neural network was used to ordinate the field data to derive classes of forest defined by relative similarity in species composition. The separability of the defined classes in the Landsat TM image was evaluated with a discriminant analysis. Results Analyses of the field data revealed considerable variation in the biodiversity of seedlings, saplings and trees at the site, associated, in part, with differences in logging activities. This variation in biodiversity was manifest in the remotely sensed data. The analyses indicated an ability to (1) predict biodiversity indices, with the highest correlation between predicted and actual index observed for evenness described by Shannon entropy (r = 0.546), but especially to (2) classify nine forest classes defined on the basis of similarity in tree species composition (accuracy 95.8%). Main conclusions Logging activities impacted on biodiversity and the resulting variation in biodiversity was reflected in the remotely sensed imagery. Using methods that exploit more fully the information content of the imagery than those used in other previous studies, a richer representation of biodiversity may be derived. This representation includes estimates of key summary indices of biodiversity, notably richness and evenness, as well as information on species composition. The results indicate that remotely sensed data may be used as a source of information on biodiversity at the landscape scale that may be used to inform conservation science and management.     

Measuring Rao's Q diversity index from remote sensing: An open source solution

Measuring biodiversity is a key issue in ecology to guarantee effective indicators of ecosystem health at different spatial and time scales. However, estimating biodiversity from field observations might present difficulties related to costs and time needed. Moreover, a continuous data update for biodiversity monitoring purposes might be prohibitive. From this point of view, remote sensing represents a powerful tool since it allows to cover wide areas in a relatively low amount of time. One of the most common indicators of biodiversity is Shannon's entropy H′, which is strictly related to environmental heterogeneity, and thus to species diversity. However, Shannon's entropy might show drawbacks once applied to remote sensing data, since it considers relative abundances but it does not explicitly account for distances among pixels’ numerical values. In this paper we propose the use of Rao's Q applied to remotely sensed data, providing a straightforward R-package function to calculate it in 2D systems. We will introduce the theoretical rationale behind Rao's index and then provide applied examples based on the proposed R function.     

Remote sensing variables as predictors of habitat suitability of the viscacha rat (<Emphasis Type="Italic">Octomys mimax</Emphasis>), a rock-dwelling mammal living in a desert environment

Identifying high-quality habitats across large areas is a central goal in biodiversity conservation. Remotely sensed data provide the opportunity to study different habitat characteristics (e.g., landscape topography, soil, vegetation cover, climatic factors) that are difficult to identify at high spatial and temporal resolution on the basis of field studies. Our goal was to evaluate the applicability of remotely sensed information as a potential tool for modeling habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling species that lives in a desert ecosystem. We fitted models considering raw indices (i.e., green indices, Brightness Index (BI) and temperature) and their derived texture measures on locations used by and available for the viscacha rat. The habitat preferences identified in our models are consistent with results of field studies of landscape use by the viscacha rat. Rocky habitats were well differentiated by the second-order contrast of BI, instead of BI only, making an important contribution to the global model by capturing the heterogeneity of the substratum. Furthermore, rocky habitats are able to maintain more vegetation than much of the surrounding desert; hence, their availability might be estimated using SATVI (Soil Adjusted Total Vegetation Index) and its derived texture measures: second-order contrast and entropy. This is the first study that evaluates the usefulness of remotely sensed data for predicting and mapping habitat suitability for a small-bodied rock dwelling species in a desert environment. Our results may contribute to conservation efforts focused on these habitat specialist species by using good predictors of habitat quality.     

遥感用于森林生物多样性监测的进展

随着物种和栖息地的丧失,全球范围的生物多样性保护已经成为迫切的需要。航空航天技术的迅猛发展使遥感成为能提供跨越不同时空尺度监测陆地生态系统生物多样性的重要工具,这方面的研究在欧美等国已经有了小范围的开展,在国内刚刚起步。国外关于生物多样性遥感探测的方法基本有3种:1.利用遥感数据直接对物种或生境制图,进而估算生物多样性;2 .建立遥感数据的光谱反射率与地面观测物种多样性的关系模型;3.与野外调查数据结合直接在遥感数据上进行生物多样性指数制图。研究表明,物种直接制图法只能应用于较小的范围;生境制图的方法,应用广泛,技术相对成熟,研究范围局限于几百公里的范畴,但不能获取生境内部的多样性信息。光谱模型技术目前正处于探索阶段,对于植被复杂、生物多样性高的地域,具有较大的应用潜力。在遥感数据上直接进行生物多样性制图在加拿大已经得到了应用。     

Remote detection of invasive plants: a review of spectral,textural and phenological approaches

Remote sensing image analysis is increasingly being used as a tool for mapping invasive plant species. Resulting distribution maps can be used to target management of early infestations and to model future invasion risk. Remote identification of invasive plants based on differences in spectral signatures is the most common approach, typically using hyperspectral data. But several studies have found that textural and phenological differences are also effective approaches for identifying invasive plants. I review examples of remote detection of invasive plants based on spectral, textural and phenological analysis and highlight circumstances where the different approaches are likely to be most effective. I also review sources and availability of remotely sensed data that could be used for mapping and suggest field data collection approaches that would support the analysis of remotely sensed data. Remote mapping of biological invasions remains a relatively specialized research topic, but the distinct cover, morphology and/or seasonality of many invaded versus native ecosystems suggests that more species could be detected remotely. Remote sensing can sometimes support early detection and rapid response directly, however, accurately detecting small, nascent populations is a challenge. However, even maps of heavily infested areas can provide a valuable tool for risk assessment by increasing knowledge about temporal and spatial patterns and predictors of invasion.     

The Effects of Biogeography and Biotic Interactions on Lemur Community Assembly

Geographic patterns of biodiversity result from broad-scale biogeographic and present-day ecological processes. The aim of this study was to investigate the relative importance of biogeographic history and ecology driving patterns of diversity in modern primate communities in Madagascar. I collected data on endemic lemur species co-occurrence from range maps and survey literature for 100 communities in protected areas. I quantified and compared taxonomic, phylogenetic, and functional dimensions of intra- and intersite diversity. I tested environmental and geographic predictors of diversity and endemism. I calculated deforestation rates within protected areas between the years 2000 and 2014, and tested if diversity is related to forest cover and loss. I found the phylogenetic structure of lemur communities could be explained primarily by remotely sensed plant productivity, supporting the hypothesis that there was ecological differentiation among ecoregions, while functional-trait disparity was not strongly related to environment. Taxonomic and phylogenetic diversity also increased with increasing topographic heterogeneity. Beta diversity was explained by both differences in ecology among localities and potential river barriers. Approximately 3000 km² were deforested in protected areas since the year 2000, threatening the most diverse communities (up to 31%/park). The strong positive association of plant productivity and topographic heterogeneity with lemur diversity indicates that high productivity, rugged landscapes support greater diversity. Both ecology and river barriers influenced lemur community ecology and biogeography. These results underscore the need for focused conservation efforts to slow the loss of irreplaceable evolutionary and ecological diversity.     

Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping

Information on the spatial distribution and composition of biological communities is essential in designing effective strategies for biodiversity conservation and management. Reliable maps of species richness across the landscape can be useful tools for these purposes. Acquiring such information through traditional survey techniques is costly and logistically difficult. The kriging interpolation method has been widely used as an alternative to predict spatial distributions of species richness, as long as the data are spatially dependent. However, even when this requirement is met, researchers often have few sampled sites in relation to the area to be mapped. Remote sensing provides an inexpensive means to derive complete spatial coverage for large areas and can be extremely useful for estimating biodiversity. The aim of this study was to combine remotely sensed data with kriging estimates (hybrid procedures) to evaluate the possibility of improving the accuracy of tree species richness maps. We did this through the comparison of the predictive performance of three hybrid geostatistical procedures, based on tree species density recorded in 141 sampling quadrats: co-kriging (COK), kriging with external drift (KED), and regression kriging (RK). Reflectance values of spectral bands, computed NDVI and texture measurements of Landsat 7 TM imagery were used as ancillary variables in all methods. The R² values of the models increased from 0.35 for ordinary kriging to 0.41 for COK, and from 0.39 for simple regression estimates to 0.52 and 0.53 when using simple KED and RK, respectively. The R² values of the models also increased from 0.60 for multiple regression estimates to 0.62 and 0.66 when using multiple KED and RK, respectively. Overall, our results demonstrate that these procedures are capable of greatly improving estimation accuracy, with multivariate RK being clearly superior, because it produces the most accurate predictions, and because of its flexibility in modeling multivariate relationships between tree richness and remotely sensed data. We conclude that this is a valuable tool for guiding future efforts aimed at conservation and management of highly diverse tropical forests.     

Demonstration of a satellite-based index to monitor habitat at continental-scales

An important initial step in the conservation and sustainable management of the Earth's biodiversity is to implement systems to both identify and subsequently monitor components of biological diversity, along with developing a better understanding of the processes that significantly threaten their conservation or sustainable use. Key factors in both species diversity and richness are related to environmental heterogeneity which is driven by temporal and spatial variation in the biological, physical, and chemical features of the environment. These environmental characteristics are manifest through the condition and change in vegetation productivity (considered as an integrated response of vegetation to climate and soil conditions). Earth observation is uniquely capable of synoptically covering large areas of the planet in a repeatable, and cost effective manner, and is a well-established technology for detecting terrestrial vegetation productivity. A recently developed dynamic habitat index (DHI), based on satellite observations of the fraction of radiation absorbed by the canopy (fPAR), has been shown to effectively cluster remotely sensed observations into a range of habitat regimes which in turn have been related to breeding bird surveys in the Canadian Province of Ontario and across the conterminous United States. With evidence that the index is well correlated with species diversity, we consider, in this subsequent paper, whether such an index is a suitable candidate as a continental index to characterize and subsequently monitor habitat conditions. To do so, we first utilise available fPAR data available from 2000 to 2005 over North America, and apply the index. Using information on continental terrestrial ecozones and their ecological distinctiveness, we then compare and contrast the index and utilize trajectory analysis to assess what changes have occurred in the index over the 6-year time period and possible implications for continental biodiversity. The potential application of the index is the discussed.     

A spectral analysis of stem bark for boreal and temperate tree species

The woody material of forest canopies has a significant effect on the total forest reflectance and on the interpretation of remotely sensed data, yet research on the spectral properties of bark has been limited. We developed a novel measurement setup for acquiring stem bark reflectance spectra in field conditions, using a mobile hyperspectral camera. The setup was used for stem bark reflectance measurements of ten boreal and temperate tree species in the visible (VIS) to near‐infrared (NIR) (400–1000 nm) wavelength region. Twenty trees of each species were measured, constituting a total of 200 hyperspectral reflectance images. The mean bark spectra of species were similar in the VIS region, and the interspecific variation was largest in the NIR region. The intraspecific variation of bark spectra was high for all studied species from the VIS to the NIR region. The spectral similarity of our study species did not correspond to the general phylogenetic lineages. The hyperspectral reflectance images revealed that the distributions of per‐pixel reflectance values within images were species‐specific. The spectral library collected in this study contributes toward building a comprehensive understanding of the spectral diversity of forests needed not only in remote sensing applications but also in, for example, biodiversity or land surface modeling studies.