Chronosequence predictions are robust in a Neotropical secondary forest,but plots miss the mark

Tropical secondary forests (TSF) are a global carbon sink of 1.6 Pg C/year. However, TSF carbon uptake is estimated using chronosequence studies that assume differently aged forests can be used to predict change in aboveground biomass density (AGBD) over time. We tested this assumption using two airborne lidar datasets separated by 11.5 years over a Neotropical landscape. Using data from 1998, we predicted canopy height and AGBD within 1.1 and 10.3% of observations in 2009, with higher accuracy for forest height than AGBD and for older TSFs in comparison to younger ones. This result indicates that the space‐for‐time assumption is robust at the landscape‐scale. However, since lidar measurements of secondary tropical forest are rare, we used the 1998 lidar dataset to test how well plot‐based studies quantify the mean TSF height and biomass in a landscape. We found that the sample area required to produce estimates of height or AGBD close to the landscape mean is larger than the typical area sampled in secondary forest chronosequence studies. For example, estimating AGBD within 10% of the landscape mean requires more than thirty 0.1 ha plots per age class, and more total area for larger plots. We conclude that under‐sampling in ground‐based studies may introduce error into estimations of the TSF carbon sink, and that this error can be reduced by more extensive use of lidar measurements.     

分布区西缘油松种群的生长特征

 在青海省互助北山林场和贵德东山林场的油松林内分别设置6个和5个样方,对分布区西缘的天然油松种群的生长特征进行了生态学研究。结果表明,两地的油松种群均为复层异龄结构;树高与胸径的关系揭示东山的油松种群其生长能力仅为北山的一半;种群内个体的树高直径比与个体的大小呈负相关,表明个体生长对被压的生态适应;两地个体的直径生长过程也差异很大,即东山油松的直径生长随年龄的增长呈下降趋势,而北山呈上升趋势或变化很小。上述差异导致两地的生产力差异很大：北山可望获得200t／ha的生物量,而东山仅约为100t/ha,但两地的生物量密度差异不大,为7一11t／ha．m,利用生物量密度与总胸高断面积之间的线性关系,可以建立良好的推算种群地上部生物量的方法。本文还对天然油松的种群密度与平均个体重之间的关系作了讨论。     

Forest Structure and Biomass of a Tropical Seasonal Rain Forest in Xishuangbanna,Southwest China1

Xishuangbanna is a region located at the northern edge of tropical Asia. Biomass estimates of its tropical rain forest have not been published in English literature. We estimated forest biomass and its allocation patterns in five 0.185–1.0 ha plots in tropical seasonal rain forests of Xishuangbanna. Forest biomass ranged from 362.1 to 692.6 Mg/ha. Biomass of trees with diameter at 1.3 m breast height (DBH) ≥ 5 cm accounted for 98.2 percent of the rain forest biomass, followed by shrubs (0.9%), woody lianas (0.8%), and herbs (0.2%). Biomass allocation to different tree components was 68.4–70.0 percent to stems, 19.8–21.8 percent to roots, 7.4–10.6 percent to branches, and 0.7–1.3 percent to leaves. Biomass allocation to the tree sublayers was 55.3–62.2 percent to the A layer (upper layer), 30.6–37.1 percent to the B layer (middle), and 2.7–7.6 percent to the C layer (lower). Biomass of Pometia tomentosa, a dominant species, accounted for 19.7–21.1 percent of the total tree biomass. The average density of large trees (DBH ≥100 cm) was 9.4 stems/ha on two small plots and 3.5 stems/ha on two large plots, illustrating the potential to overestimate biomass on a landscape scale if only small plots are sampled. Biomass estimations are similar to typical tropical rain forests in Southeast Asia and the Neotropics.     

Aboveground Biomass Accumulation in a Tropical Wet Forest in Nicaragua Following a Catastrophic Hurricane Disturbance1

Among their effects on forest structure and carbon dynamics, hurricanes frequently create large‐scale canopy gaps that promote secondary growth. To measure the accumulation of aboveground biomass (AGBM) in a hurricane damaged forest, we established permanent plots 4 mo after the landfall of Hurricane Joan on the Atlantic coast of Nicaragua in October 1988. We quantified AGBM accumulation in these plots by correlating diameter measurements to AGBM values using a published allometric regression equation for tropical wet forests. In the first measurement year following the storm, AGBM in hurricane‐affected plots was quite variable, ranging from 26 to 153 Mg/ha, with a mean of 78 (±15) Mg/ha. AGBM was substantially lower than in two control plots several kilometers outside the hurricane's path (331 ±15 Mg/ha). Biomass accumulation was slow (5.36 ± 0.74 Mg/ha/yr), relative to previous studies of forest regeneration following another hurricane (Hugo) and agricultural activity. We suggest that large‐scale, homogenous canopy damage caused by Hurricane Joan impeded the dispersal and establishment of pioneer trees and led to a secondary forest dominated by late successional species that resprouted and survived the disturbance. With the relatively slow rate of biomass accumulation, any tightening in disturbance interval could reduce the maximum capacity of the living biomass to store carbon.     

Monitoring individual tree‐based change with airborne lidar

Understanding the carbon flux of forests is critical for constraining the global carbon cycle and managing forests to mitigate climate change. Monitoring forest growth and mortality rates is critical to this effort, but has been limited in the past, with estimates relying primarily on field surveys. Advances in remote sensing enable the potential to monitor tree growth and mortality across landscapes. This work presents an approach to measure tree growth and loss using multidate lidar campaigns in a high‐biomass forest in California, USA. Individual tree crowns were delineated in 2008 and again in 2013 using a 3D crown segmentation algorithm, with derived heights and crown radii extracted and used to estimate individual tree aboveground biomass. Tree growth, loss, and aboveground biomass were analyzed with respect to tree height and crown radius. Both tree growth and loss rates decrease with increasing tree height, following the expectation that trees slow in growth rate as they age. Additionally, our aboveground biomass analysis suggests that, while the system is a net source of aboveground carbon, these carbon dynamics are governed by size class with the largest sources coming from the loss of a relatively small number of large individuals. This study demonstrates that monitoring individual tree‐based growth and loss can be conducted with multidate airborne lidar, but these methods remain relatively immature. Disparities between lidar acquisitions were particularly difficult to overcome and decreased the sample of trees analyzed for growth rate in this study to 21% of the full number of delineated crowns. However, this study illuminates the potential of airborne remote sensing for ecologically meaningful forest monitoring at an individual tree level. As methods continue to improve, airborne multidate lidar will enable a richer understanding of the drivers of tree growth, loss, and aboveground carbon flux.     

Detecting successional changes in tropical forest structure using GatorEye drone-borne lidar

Drone-based remote sensing is a promising new technology that combines the benefits of ground-based and satellite-derived forest monitoring by collecting fine-scale data over relatively large areas in a cost-effective manner. Here, we explore the potential of the GatorEye drone-lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables’ relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven second-growth and two old-growth). Stands were relatively homogenous in terms of canopy height and spatial heterogeneity, but not in their gap fraction. Neither species density nor tree community Shannon diversity index was significantly correlated with the canopy Shannon index. Canopy height, LAI, and AGB did not show a clear pattern as a function of forest age. However, gap fraction and spatial heterogeneity increased with forest age, whereas understory LAI decreased with forest age. Canopy height was strongly correlated with AGB. The heterogeneous mosaic created by successional forest patches across human-managed tropical landscapes can now be better characterized. Drone-lidar systems offer the opportunity to improve assessment of forest recovery and develop general mechanistic carbon sequestration models that can be rapidly deployed to specific sites, an essential step for monitoring progress within the UN Decade on Ecosystem Restoration.     

Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships

Aim Previous studies have developed strong, site‐specific relationships between canopy metrics from lidar (light detecting and ranging) remote sensing data and forest structural characteristics such as above‐ground biomass (AGBM), but the generality of these relationships is unknown. In this study, we examine the generality of relationships between lidar metrics and forest structural characteristics, including AGBM, from two study areas in Central America with different precipitation patterns. Location A series of tropical moist forest sites in Panama and a tropical wet forest in Costa Rica. Methods Canopy metrics (e.g. canopy height) were calculated from airborne lidar data. Basal area, mean stem diameter and AGBM were calculated from measurements taken as a part of ongoing forest dynamics studies in both areas. We examined the generality of relationship between lidar metrics and forest structure, and possible environmental effects (e.g. leaf phenology). Results We found that lidar metrics were strongly correlated (R²: 0.65–0.92) with mean stem diameter, basal area and AGBM in both regions. We also show that the relationships differed between these regions. Deciduousness of canopy trees in the tropical moist forest area accounted for the differences in predictive equations for stem diameter and basal area. The relationships between lidar metrics and AGBM, however, remained significantly different between the two study areas even after adjusting for leaf drop. We attribute this to significant differences in the underlying allometric relationships between stem diameter and AGBM in tropical wet and moist forests. Conclusions Important forest structural characteristics can be estimated reliably across a variety of conditions sampled in these closed‐canopy tropical forests. Environmental factors such as drought deciduousness have an important influence on these relationships. Future efforts should continue to examine climatic factors that may influence the generality of the relationships between lidar metrics and forest structural characteristics and assess more rigorously the generality of field‐derived allometric relationships.     

Mapping land cover and forest density in Zagros forests of Khuzestan province in Iran: A study based on Sentinel-2, Google Earth and field data

Zagros forests in western Iran have widely been destroyed because of various reasons. This study was performed to provide the land cover and forest density maps in Zagros forests of Khuzestan province using Sentinel-2, Google Earth and field data. The forest boundary in Khuzestan province was digitized in Google Earth. Sentinel-2 satellite images were provided for the study area. One 1:25000 index sheet of Iranian Mapping Organization (IMO) was selected as pilot area in the province. Sentinel-2 image of the pilot area was classified using different supervised classification algorithms to select the best algorithm for land cover mapping in Khuzestan province. In addition, to evaluate the accuracy of Google Earth data, field sampling was performed using random plots in different land covers. Field data of forest plots were applied to investigate tree canopy cover percent (forest density), as well. Classification of Sentinel-2 image in Zagros area of Khuzestan province was done using the best algorithm and the land cover was obtained. The forest density map was also obtained using a linear regression model between tree canopy cover percent (obtained from field plots) and normalized difference vegetation index (NDVI) (obtained from NDVI map). Finally, the accuracy of land cover map was assessed by some square plots on Google Earth. Results demonstrated that support vector machine (SVM) algorithm had the highest accuracy for land cover mapping. Results also showed that Google Earth images had a good accuracy in the Zagros forests of Khuzestan province. Results demonstrated that NDVI has been a good predicator to estimate tree canopy cover in the study area. Based on results, an area of 443,091.22 ha is covered by Zagros forests in Khuzestan province. Results of accuracy assessment of the land cover map showed the good accuracy of this map in Khuzestan province (overall accuracy: 91% and kappa index: 0.83). For optimum management of Zagros forests, it is suggested that the land cover and forest density mapping will be performed using SVM algorithm, NDVI, and Sentinel-2 satellite images in Zagros forests of Khuzestan province in the certain periods.     

Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis Program,Landsat, and LANDFIRE

The concentrations of CO₂ and other greenhouse gases in the atmosphere have been increasing and greatly affecting global climate and socio-economic systems. Actively growing forests are generally considered to be a major carbon sink, but forest wildfires lead to large releases of biomass carbon into the atmosphere. Aboveground forest biomass carbon (AFBC), an important ecological indicator, and fire-induced carbon emissions at regional scales are highly relevant to forest sustainable management and climate change. It is challenging to accurately estimate the spatial distribution of AFBC across large areas because of the spatial heterogeneity of forest cover types and canopy structure. In this study, Forest Inventory and Analysis (FIA) data, Landsat, and Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) data were integrated in a regression tree model for estimating AFBC at a 30-m resolution in the Utah High Plateaus. AFBC were calculated from 225 FIA field plots and used as the dependent variable in the model. Of these plots, 10% were held out for model evaluation with stratified random sampling, and the other 90% were used as training data to develop the regression tree model. Independent variable layers included Landsat imagery and the derived spectral indicators, digital elevation model (DEM) data and derivatives, biophysical gradient data, existing vegetation cover type and vegetation structure. The cross-validation correlation coefficient (r value) was 0.81 for the training model. Independent validation using withheld plot data was similar with r value of 0.82. This validated regression tree model was applied to map AFBC in the Utah High Plateaus and then combined with burn severity information to estimate loss of AFBC in the Longston fire of Zion National Park in 2001. The final dataset represented 24 forest cover types for a 4 million ha forested area. We estimated a total of 353 Tg AFBC with an average of 87 MgC/ha in the Utah High Plateaus. We also estimated that 8054 Mg AFBC were released from 2.24 km² burned forest area in the Longston fire. These results demonstrate that an AFBC spatial map and estimated biomass carbon consumption can readily be generated using existing database. The methodology provides a consistent, practical, and inexpensive way for estimating AFBC at 30-m resolution over large areas throughout the United States.     

Patterns and Correlates of Tropical Dry Forest Structure and Composition in a Highly Replicated Chronosequence in Yucatan,Mexico

Research on tropical dry forest (TDF) succession i0s needed for effective conservation and management of this threatened and understudied ecosystem. We used a highly replicated chronosequence within a 37,242‐ha TDF landscape to investigate successional patterns by plant size class and to evaluate the influence of stand age, topographic position, soil properties and spatial autocorrelation on forest structure and composition. We used a SPOT5 satellite image to obtain a land‐cover thematic map, and sampled woody vegetation (adults: >5 cm diam; saplings: 1–5 cm) and soil properties in 168 plots distributed among four vegetation classes: VC1 (3–8‐yr‐old forest), VC2 (9–15‐yr‐old forest), VC3 (>15‐yr‐old forest on flat areas), VC4 (>15‐yr‐old forest on hills). Stem density decreased with stand age and was lowest in VC3, while height, basal area and species density increased with age and were higher in older than in younger forests. Topographic position also influenced forest structure and composition. Basal area and height were largely determined by stand age, whereas stem and species density, and composition were influenced mostly by soil variables associated with fertility, and by spatial autocorrelation. Adults and saplings showed contrasting patterns and correlates of community structure, but similar patterns and correlates of composition, possibly due to the prevalence of coppicing. Our results show that our sampling approach can overcome several limitations of chronosequence studies, and provide insights in the patterns and drivers of succession, as well as guidelines for forest management and conservation. Abstract in Spanish is available at http://www.blackwell‐synergy.com/loi/btp .     

Effects of peat swamp logging and agricultural expansion on species richness of native mammals in Peninsular Malaysia

The biodiversity inhabiting tropical peat swamp forests in Southeast Asia is currently threatened by commercial logging and agricultural expansion. The occurrence of mammals in such forests is often poorly known and the factors influencing their occurrence in these ecosystems have rarely been quantified. We aim to determine the key habitat and landscape drivers of mammal species richness in fragmented peat swamp reserves. We conducted camera trap surveys in the North Selangor Peat Swamp Forest (NSPSF), the last remaining area of peat swamp forest on the west coast of Peninsular Malaysia. We also measured vegetation structure and landscape metrics to investigate the relationship between these factors and mammal richness. We recorded a total of 16 mammal species from 45 sampling sites using camera traps located in peat swamp forest reserves. Mammal species richness increased with the abundance of large trees and distance away from roads. Species richness decreased significantly with canopy cover and height, the abundance of fallen trees, the abundance of forest palms and saplings, distance away from rivers, and a measure of landscape compositional heterogeneity. Our findings underscore the high conservation value of logged peat swamp forests and the urgent need to halt further deforestation. We recommend: (1) protecting riparian habitat; (2) avoiding further forest conversion particularly areas supporting large trees into oil palm plantations; and (3) limiting road development within and around the NSPSF.     

Pervasive canopy dynamics produce short-term stability in a tropical rain forest landscape

A fundamental property of all forest landscapes is the size frequency distribution of canopy gap disturbances. But characterizing forest structure and changes at large spatial scales has been challenging and most of our understanding is from permanent inventory plots. Here we report the first application of light detection and ranging remote sensing to measurements of canopy disturbance and regeneration in an old-growth tropical rain forest landscape. Pervasive local height changes figure prominently in the dynamics of this forest. Although most canopy gaps recruited to higher positions during 8.5 years, size frequency distributions were similar at two points in time and well-predicted by power-laws. At larger spatial scales (hundreds of ha), height increases and decreases occurred with similar frequency and changes to canopy height that were analysed using a height transition matrix suggest that the distribution of canopy height at the beginning of the study was close to the projected steady-state equilibrium under the recent disturbance regime. Taken together, these findings show how widespread local height changes can produce short-term stability in a tropical rain forest landscape.     

Dynamics of a human‐modified tropical peat swamp forest revealed by repeat lidar surveys

Tropical peat swamp forests (PSFs) are globally important carbon stores under threat. In Southeast Asia, 35% of peatlands had been drained and converted to plantations by 2010, and much of the remaining forest had been logged, contributing significantly to global carbon emissions. Yet, tropical forests have the capacity to regain biomass quickly and forests on drained peatlands may grow faster in response to soil aeration, so the net effect of humans on forest biomass remains poorly understood. In this study, two lidar surveys (made in 2011 and 2014) are compared to map forest biomass dynamics across 96 km² of PSF in Kalimantan, Indonesia. The peatland is now legally protected for conservation, but large expanses were logged under concessions until 1998 and illegal logging continues in accessible portions. It was hypothesized that historically logged areas would be recovering biomass while recently logged areas would be losing biomass. We found that historically logged forests were recovering biomass near old canals and railways used by the concessions. Lidar detected substantial illegal logging activity—579 km of logging canals were located beneath the canopy. Some patches close to these canals have been logged in the 2011–2104 period (i.e. substantial biomass loss) but, on aggregate, these illegally logged regions were also recovering. Unexpectedly, rapid growth was also observed in intact forest that had not been logged and was over a kilometre from the nearest known canal, perhaps in response to greater aeration of surface peat. Comparing these results with flux measurements taken at other nearby sites, we find that carbon sequestration in above‐ground biomass may have offset roughly half the carbon efflux from peat oxidation. This study demonstrates the power of repeat lidar survey to map fine‐scale forest dynamics in remote areas, revealing previously unrecognized impacts of anthropogenic global change.     

Disturbance history mediates climate change effects on subtropical forest biomass and dynamics

The responses of forest communities to interacting anthropogenic disturbances like climate change and logging are poorly known. Subtropical forests have been heavily modified by humans and their response to climate change is poorly understood. We investigated the 9‐year change observed in a mixed conifer‐hardwood Atlantic forest mosaic that included both mature and selectively logged forest patches in subtropical South America. We used demographic monitoring data within 10 1 ha plots that were subjected to distinct management histories (plots logged until 1955, until 1987, and unlogged) to test the hypothesis that climate change affected forest structure and dynamics differentially depending on past disturbances. We determined the functional group of all species based on life‐history affinities as well as many functional traits like leaf size, specific leaf area, wood density, total height, stem slenderness, and seed size data for the 66 most abundant species. Analysis of climate data revealed that minimum temperatures and rainfall have been increasing in the last few decades of the 20th century. Floristic composition differed mainly with logging history categories, with only minor change over the nine annual census intervals. Aboveground biomass increased in all plots, but increases were higher in mature unlogged forests, which showed signs of forest growth associated with increased CO₂, temperature, and rainfall/treefall gap disturbance at the same time. Logged forests showed arrested succession as indicated by reduced abundances of Pioneers and biomass‐accumulators like Large Seeded Pioneers and Araucaria, as well as reduced functional diversity. Management actions aimed at creating regeneration opportunities for long‐lived pioneers are needed to restore community functional diversity, and ecosystem services such as increased aboveground biomass accumulation. We conclude that the effects of climate drivers on the dynamics of Brazilian mixed Atlantic forests vary with land‐use legacies, and can differ importantly from the ones prevalent in better known tropical forests.     

Forest structure and biomass in post-agricultural forests: Lessons learned with new spatial tools

Questions: With calls for afforestation to sequester carbon due to climate change, agricultural land will be converted to forests in the near future. Little is known about how the ecosystem services of reforested landscapes with an agricultural land-use history will differ from reference forests. Our objectives were to (i) test the hypothesis that forests with a history of agricultural land use can provide the same carbon storage and biomass ecosystem services as adjacent reference forests, given some recovery time; (ii) explore whether there is a lag in the recovery of forest community composition due to prior agricultural land use; and (iii) demonstrate how remote-sensing methods can improve our understanding of land-use legacies at large spatial scales. Location: Finger Lakes National Forest, NY, USA. Methods: Using historic air photos, landscape-scale lidar, and field surveys, we compared differences in biomass storage, forest structure, and vegetation communities between reference forests and post-agricultural forests at different stages of regeneration in the Finger Lakes National Forest, New York, USA. We also used lidar to create a spatial model of biomass across the landscape to analyze the spatial distribution of biomass across our study area. Results: We found biomass and forest structure in post-agricultural forests generally recovered to levels typical of reference forests within 50 years of abandonment. Conversely, we found the composition of woody and herbaceous communities still varied between reference and post-agricultural forests after 50 years of abandonment. Conclusions: Collectively our results indicate afforestation efforts can be effective for carbon sequestration at early stages of forest succession. Our spatial model of biomass indicated that biomass levels can be low in forests with extensive edge. Further research is needed to understand how contemporary landscape structure interacts with legacy effects of agriculture to affect biomass and other ecosystem services.     

Error propagation and scaling for tropical forest biomass estimates

The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. To make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimates, yet this uncertainty is rarely evaluated carefully. Here, we quantify four types of uncertainty that could lead to statistical error in AGB estimates: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. In previous studies, these sources of error were reported but rarely integrated into a consistent framework. We estimate all four terms in a 50 hectare (ha, where 1 ha = 10(4) m2) plot on Barro Colorado Island, Panama, and in a network of 1 ha plots scattered across central Panama. We find that the most important source of error is currently related to the choice of the allometric model. More work should be devoted to improving the predictive power of allometric models for biomass.     

Soil is the main predictor of secondary rain forest estimated aboveground biomass across a Neotropical landscape

We studied the relative effects of landscape configuration, environmental variables, forest age, and spatial variables on estimated aboveground biomass (AGB) in Costa Rican secondary rain forests patches. We measured trees ≥5 cm dbh in 24, 0.25 ha plots and estimated AGB for trees 5–24.9 cm dbh and for trees >25 cm dbh using two allometric equations based on multispecies models using tree dbh and wood‐specific gravity. AGB averaged 87.3 Mg/ha for the 24 plots (not including remnant trees) and 123.4 Mg/ha including remnant trees (20 plots). There was no effect of forest age on AGB. Variation partitioning analysis showed that soils, climate, landscape configuration, and space together explained 61% of tree AGB variance. When controlling for the effects of the other three variables, only soils remained significant. Soil properties, specifically K and Cu, had the strongest independent effect on AGB (variation partitioning, R² = 0.17, p = 0.0310), indicating that in this landscape, AGB variation in secondary forest patches is influenced by soil chemical properties. Elucidating the relative influence of soils in AGB variation is critical for understanding changes associated with land cover modification across Neotropical landscapes, as it could have important consequences for land use planning since secondary forests are considered carbon sinks. Abstract in Spanish is available with online material.     

Light heterogeneity affects understory plant species richness in temperate forests supporting the heterogeneity–diversity hypothesis

One of the most important drivers for the coexistence of plant species is the resource heterogeneity of a certain environment, and several studies in different ecosystems have supported this resource heterogeneity–diversity hypothesis. However, to date, only a few studies have measured heterogeneity of light and soil resources below forest canopies to investigate their influence on understory plant species richness. Here, we aim to determine (1) the influence of forest stand structural complexity on the heterogeneity of light and soil resources below the forest canopy and (2) whether heterogeneity of resources increases understory plant species richness. Measures of stand structural complexity were obtained through inventories and remote sensing techniques in 135 1‐ha study plots of temperate forests, established along a gradient of forest structural complexity. We measured light intensity and soil chemical properties on six 25 m² subplots on each of these 135 plots and surveyed understory vegetation. We calculated the coefficient of variation of light and soil parameters to obtain measures of resource heterogeneity and determined understory plant species richness at plot level. Spatial heterogeneity of light and of soil pH increased with higher stand structural complexity, although heterogeneity of soil pH did not increase in conditions of generally high levels of light availability. Increasing light heterogeneity was also associated with increasing understory plant species richness. However, light heterogeneity had no such effects in conditions where soil resource heterogeneity (variation in soil C:N ratios) was low. Our results support the resource heterogeneity–diversity hypothesis for temperate forest understory at the stand scale. Our results also highlight the importance of interaction effects between the heterogeneity of both light and soil resources in determining plant species richness.