Secondary Forest Detection in a Neotropical Dry Forest Landscape Using Landsat 7 ETM+ and IKONOS Imagery1

We integrate forest structure and remotely sensed data for four successional stages (pasture, early, intermediate, and late) of a tropical dry forest area located in the Sector Santa Rosa of the Guanacaste Conservation Area in northwestern Costa Rica. We used a combination of spectral vegetation indices derived from Landsat 7 ETM+ medium resolution and IKONOS high‐resolution imagery. The indices (using the red and near‐infrared bands) simple ratio and normalized difference vegetation index separated the successional stages well. Two other indices using mid‐infrared bands did not separate successional stages as well. In a comparison of the successional stages with chronological age, there was no separability in the spectral reflectance among different age classes. Successional stages, in contrast, showed distinct groups with minimal overlap. We also applied a simple validation in another dry forest located in the Palo Verde National Park in the province of Guanacaste, Costa Rica, with reasonably good results.     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

暖温带地区3种森林群落叶面积指数和林冠开阔度的季节动态

以辽东栎(Quercus liaotungensis)为主的落叶阔叶林、华北落叶松(Larix principis-rupprechtii)林和油松(Pinus tabulaeformis)林是暖温带林区具有代表性的森林群落类型。该研究应用国内外流行的半球图方法,通过对这3种森林群落叶面积指数和林冠开阔度的测定和综合比较,分析了叶面积指数和林冠开阔度的季节动态,揭示了暖温带地区不同类型森林群落叶面积指数和林冠开阔度的特征。研究结果表明,落叶阔叶林(优势种为辽东栎、棘皮桦(Betula dahurica)和五角枫(Acer mono))和华北落叶松林两种落叶森林群落的叶面积指数值均随生长季的到来而呈现增长的趋势,最大值出现在8月;林冠开阔度值随着生长季的到来而下降,最大值出现在11月。落叶阔叶林的叶面积指数和林冠开阔度的季节动态较之华北落叶松林明显。油松是常绿树种,其群落叶面积指数和林冠开阔度的变化程度均不明显,但林冠开阔度的变化趋势也是与叶面积指数的变化趋势相反。通过计算得出叶面积指数和林冠开阔度相关显著,并且呈现指数回归的关系。此研究结果为以遥感途径获取暖温带地区叶面积指数提供了地面校正依据,为研究该地区植被林冠的异质性及其造成的影响,以及进一步对该地区林分、景观和区域尺度上碳、水分和通量等方面的模拟提供了基础数据。     

基于高光谱遥感的小麦叶干重和叶面积指数监测

生物量和叶面积指数(LAI)是描述作物长势的重要参数, 叶干重和LAI的实时动态监测对小麦(Triticum aestivum)生长诊断和管理调控具有重要意义。为分析多种高光谱参数估算小麦叶干重和LAI的效果, 建立小麦叶干重和LAI的定量监测模型, 该研究连续3年采用不同小麦品种进行不同施氮水平的大田试验, 于小麦不同生育期采集田间冠层高光谱数据并测定叶片叶干重和LAI。试验结果显示, 小麦叶干重和LAI随施氮水平的提高而增加, 随生育进程呈单峰动态变化模式。小麦叶干重和LAI与光谱反射率间相关性较好的区域主要位于红光波段(590~710 nm, r<-0.60)和近红外波段(745~1 130 nm, r>0.69)。对于不同试验条件下的叶干重和LAI, 可以使用统一的光谱参数进行定量反演, 其中基于RVI (810, 560)、FD755、GM1、SARVI (MSS)和TC3等光谱参数的方程拟合效果较好。经不同年际独立试验数据的检验表明, 以参数RVI (810, 560)、GM1、SARVI (MSS)、PSSRb、(R_750-800/R_695-740) -1、VOG2和MSR705为变量建立的叶干重和LAI监测模型均给出较好的检验结果。因此, 利用关键特征光谱参数可以有效地评价小麦叶片生长状况, 尤其是光谱参数RVI (810, 560)、GM1和SARVI (MSS)可以对不同条件下小麦叶干重和LAI进行准确可靠的监测。     

高光谱植被指数与水稻叶面积指数的定量关系

基于不同水稻品种、施氮水平和不同生育期下的大田试验,确立了水稻叶面积指数（LAI）与冠层光谱特征参数的定量关系.结果表明：水稻叶面积指数与部分高光谱植被指数存在良好的相关性,其中原始光谱组成的2波段差值指数（DI）形式相关性最好,其次为比值（RI）和归一化（NI）植被指数.相关最好的原始光谱植被指数是由近红外波段组成的差值指数DI(854,760),相关最好的一阶导数光谱植被指数是红光和近红外光组成的导数差值指数DI(D₆₇₆, D₇₇₈),但总体上导数光谱指数不如原始光谱指数与LAI关系密切.独立试验数据检验结果表明,以差值指数DI(854,760)为变量建立的水稻LAI监测模型具有较好的表现,可用于水稻LAI的估测.     

Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest

Aims Understanding of the ecophysiological dynamics of forest canopy photosynthesis and its spatial and temporal scaling is crucial for revealing ecological response to climate change. Combined observations and analyses of plant ecophysiology and optical remote sensing would enable us to achieve these studies. In order to examine the utility of spectral vegetation indices (VIs) for assessing ecosystem-level photosynthesis, we investigated the relationships between canopy-scale photosynthetic productivity and canopy spectral reflectance over seasons for 5 years in a cool, temperate deciduous broadleaf forest at 'Takayama' super site in central Japan.Methods Daily photosynthetic capacity was assessed by in situ canopy leaf area index (LAI), (LAI × V cmax [single-leaf photosynthetic capacity]), and the daily maximum rate of gross primary production (GPP max) was estimated by an ecosystem carbon cycle model. We examined five VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red vegetation index (GRVI), chlorophyll index (CI) and canopy chlorophyll index (CCI), which were obtained by the in situ measurements of canopy spectral reflectance.Important findings Our in situ observation of leaf and canopy characteristics, which were analyzed by an ecosystem carbon cycling model, revealed that their phenological changes are responsible for seasonal and interannual variations in canopy photosynthesis. Significant correlations were found between the five VIs and canopy photosynthetic capacity over the seasons and years; four of the VIs showed hysteresis-type relationships and only CCI showed rather linear relationship. Among the VIs examined, we applied EVI–GPP max relationship to EVI data obtained by Moderate Resolution Imaging Spectroradiometer to estimate the temporal and spatial variation in GPP max over central Japan. Our findings would improve the accuracy of satellite-based estimate of forest photosynthetic productivity in fine spatial and temporal resolutions, which are necessary for detecting any response of terrestrial ecosystem to meteorological fluctuations.     

基于多角度植被指数的马尾松林LAI反演方法

CHRIS/PROBA是目前具有最高空间分辨率（17 m×17 m）的星载多角度高光谱数据,该款数据在反演植被垂直结构参数,如树高、叶面积指数（leaf area index,LAI）等方面具有重要的应用前景。基于四尺度几何光学模型得到马尾松（Pinus massoniana Lamb.）冠层的归一化差分植被指数（normalized difference vegetation index,NDVI）各向异性分布规律,利用CHRIS红光特征波段和近红外特征波段构建一种新型多角度植被指数（normalized hotspot-dark-spot difference vegetation index,NHDVI）,并将其应用于CHRIS数据对马尾松林的LAI遥感估算上。结果显示：（1）相比归一化差分植被指数（NDVI）与土壤调节植被指数（soil adjusted vegetation index,SAVI）而言,NHDVI能很好地融合光谱信息与角度信息,与地面实测LAI的决定系数达到0.7278;（2）利用NHDVI-LAI统计回归模型方法来反演LAI值,将得到的LAI值与地面实测值进行相关性分析,结果拟合优度达到0.8272,均方根误差RMSE为0.1232。与传统植被指数相比,包含角度信息的多角度植被指数对LAI的反演在精度上有较大提升,同时比基于辐射传输模型的反演方法更简易、实用。     

Topography strongly affects atmospheric deposition and canopy exchange processes in different types of wet lowland rainforest,Southwest Costa Rica

Bulk precipitation and throughfall were collected in a wet lowland rainforest in SW Costa Rica on an event basis to allow modelling the contributions of dry deposition and canopy exchange to nutrient inputs and internal cycling of nutrients. Estimates based on bulk precipitation underestimated total atmospheric deposition to tropical rainforests by up to 10-fold ignoring the contributions of dry deposition. Canopy exchange contributed most of the aboveground inputs to the forest soil of Na⁺, about half for K⁺, 10% for P and Mg²⁺ and negligible for N, C and other elements. Tree species composition did not account for the differences found in net throughfall between forest sites, and vegetation structure (plant area index) had only a small effect on net throughfall. Forest regrowth affected net throughfall through reduced soil fertility and differences in leaf traits. Topography most significantly affected net throughfall via increased dry deposition at sites of higher elevation and via soil fertility and increased canopy exchange at down slope sites.     

Spatial and temporal variability of canopy structure in a tropical moist forest

Tree fall gaps are widely considered to play a prominent role in the maintenance of species diversity, while the spatiotemporal variability of canopy structure within closed forest stands is largely ignored. In this study we examined the vertical and horizontal components of canopy structure and its seasonal variability in a tropical wet semideciduous rainforest in Panama. Leaf area indices (LAI) were derived from measurements of diffuse radiation and empirically-based leaf angle distribution by mathematical inversion of a light interception model. Vertical distribution of LAI was non-homogeneous with 50% of the leaf area being concentrated in the uppermost 5 m of the canopy. In the wet season, when foliage is most abundant, the horizontal distribution of LAI in a 2100 m² plot ranged widely from 3 to 8, with a mean of 5.41. Changes in mean LAI between wet and dry seasons were small but highly significant. While ca 40% of the area was not affected by local changes in LAI, sizeable small scale changes in LAI did occur between wet and dry season in some locations. Local changes in LAI ranged from –2.3 to 2.4. These changes resulted in a 50% or more increase in light reaching the forest floor at 29% of the measuring locations, and a doubling or more at 13% of the location. Our results imply that structural heterogeneity by simple tree fall gaps do not adequately describe the dynamics of forest canopies.     

Seasonal leaf dynamics across a tree density gradient in a Brazilian savanna

Interactions between trees and grasses that influence leaf area index (LAI) have important consequences for savanna ecosystem processes through their controls on water, carbon, and energy fluxes as well as fire regimes. We measured LAI, of the groundlayer (herbaceous and woody plants <1-m tall) and shrub and tree layer (woody plants >1-m tall), in the Brazilian cerrado over a range of tree densities from open shrub savanna to closed woodland through the annual cycle. During the dry season, soil water potential was strongly and positively correlated with grass LAI, and less strongly with tree and shrub LAI. By the end of the dry season, LAI of grasses, groundlayer dicots and trees declined to 28, 60, and 68% of mean wet-season values, respectively. We compared the data to remotely sensed vegetation indices, finding that field measurements were more strongly correlated to the enhanced vegetation index (EVI, r ²=0.71) than to the normalized difference vegetation index (NDVI, r ²=0.49). Although the latter has been more widely used in quantifying leaf dynamics of tropical savannas, EVI appears better suited for this purpose. Our ground-based measurements demonstrate that groundlayer LAI declines with increasing tree density across sites, with savanna grasses being excluded at a tree LAI of approximately 3.3. LAI averaged 4.2 in nearby gallery (riparian) forest, so savanna grasses were absent, thereby greatly reducing fire risk and permitting survival of fire-sensitive forest tree species. Although edaphic conditions may partly explain the larger tree LAI of forests, relative to savanna, biological differences between savanna and forest tree species play an important role. Overall, forest tree species had 48% greater LAI than congeneric savanna trees under similar growing conditions. Savanna and forest species play distinct roles in the structure and dynamics of savanna–forest boundaries, contributing to the differences in fire regimes, microclimate, and nutrient cycling between savanna and forest ecosystems.     

Impacts of Vegetation and Precipitation on Throughfall Heterogeneity in a Tropical Pre‐Montane Transitional Cloud Forest

Precipitation throughfall (TF) plays an important role in the water balance of tropical forests. This study used 164 gauges to quantify precipitation and TF variability in a tropical pre‐montane transitional cloud forest on the Caribbean slope of the Cordillera Tilarán, Costa Rica, to identify the ecological and meteorological drivers of this variability. Daily TF measurements were taken from 28 June to 17 July 2012 and 12 June to 16 July 2013, for a total of 39 precipitation events. The total mean TF was 87.9 percent and TF at individual gauges ranged from 22.7 percent to 245.7 percent. Leaf area index (LAI) was calculated above each gauge using hemispheric photography for a mean study‐site LAI of 7.7. There was no statistically significant relationship between LAI and TF. However, the amount of TF was positively correlated with precipitation intensity, while the variability of TF was negatively correlated with precipitation intensity. Our calculations indicate that at least 61 gauges are required to obtain mean TF estimates with less than 5 percent error. This study demonstrates that TF is highly spatially heterogeneous due to multiple compounding effects.     

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.     

华北落叶松人工林蒸散及产流对叶面积指数变化的响应

定量评价林地蒸散和产流等水文过程对冠层叶面积指数(LAI)的响应,对于深入认识森林植被的生态水文过程及其发生机制,实现半干旱区林水综合管理和区域可持续发展是非常必要的。应用集总式生态水文模型BROOK90,模拟分析了不同降水年型(丰水年、平水年、枯水年)下,位于半干旱区的宁夏六盘山叠叠沟小流域内华北落叶松(Larix principis-rupprechtii)人工林的水文过程对冠层LAI变化的响应关系。结果发现:林分总蒸散量、冠层截留量、蒸腾量与LAI都呈显著的正相关关系(R~20.99,P0.01),而土壤蒸发量、产流量则与LAI均呈显著的负相关关系(R~20.99,P0.01);在不同的降水年型下,各水文过程变量与LAI的关系都可以很好地用指数函数来表达,且都存在着一个LAI阈值。当LAI低于阈值时,各水文过程变量随LAI的变化幅度较大;但高于阈值时,各变量的变化十分缓慢并趋于稳定。在不同降水年型下,各变量LAI阈值之间存在着一定的差异。一般地,丰水年各变量的LAI阈值要大于枯水年,尤其是冠层截留和土壤蒸发。在丰水年,各水文过程变量随LAI增加而变化的速率要比在平水年、枯水年更快,说明在水分充足年份中各变量的波动更多取决于LAI变化,而在水分亏缺的年份中则可能更多地受到水分条件的限制。模拟结果表明,通过减少冠层LAI(如间伐)导致的林分的降低蒸散耗水和增加产流的作用是有限的,这是由于林分蒸散降低的幅度要比LAI降低的幅度小。例如,在平水年,当LAI从4.2变为2.0(减少幅度52.4%)时,林分年蒸散仅从357.2 mm减少至333.9 mm(减少幅度6.5%)。     

祁连山区青海云杉林冠层叶面积指数的反演方法

叶面积指数(Leaf area index, LAI)是陆地生态系统的一个十分重要的结构参数。随着空间精细化模型的发展和基于过程的分布式模拟技术的应用, 对LAI的区域估算显得越来越重要, 但目前尚缺乏有效的估算手段。该项研究以青海云杉(Picea crassifolia)林为研究对象, 利用LAI-2000冠层分析仪、鱼眼镜头法和经验公式法对林冠层LAI进行了测定, 观测值分别为1.03~3.70、0.48~2.26和2.27~8.20, 显然, 仪器测定值偏低。针对针叶的集聚效应导致仪器测定值偏低的现象, 利用跟踪辐射与冠层结构测量仪(TRAC)测定的青海云杉林聚集系数计算调整系数, 对鱼眼镜头法获取的LAI值进行订正。根据高分辨率的遥感数据反演青海云杉林的植被指数与LAI的关系, 最后获得了较合理的该地区林冠层LAI的空间分布图。     

Exploring the influence of soil types underneath the canopy in winter wheat leaf area index remote estimating

Aims Remote sensing is an effective and nondestructive way to retrieve leaf area index (LAI) from plot, regional and global range. Soil background is one of the confounding factors limiting remotely estimating LAI. And soil type contains a large proportion of soil background information, which can influence the optical properties of vegetation canopy and soil. However, our knowledge on the effects stemmed from soil types underneath the canopy on LAI remote estimating have been in shortage. Thus, this study aims to explore the influences of soil types underneath the canopy on winter wheat LAI remote estimating. Methods We analyzed the sensitivity variation of eight spectral indices, named normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption ratio index 2 (MCARI₂), red edge inflection point (REIP), red edge amplitude (Dr), red edge area (SDr), red edge symmetry (RES), normalized difference spectral index (NDSI), to LAI in different soil types, and then we identified some spectral intervals or parameters that were insensitive to soil type variations underneath the canopy. We also compared the accuracy of two commonly used regression models, partial least squares regression (PLSR) and random forest regression (RFR), in estimating LAI for different soil types. We also explored the problems arising from applying the regression model developed in single soil type area to complex soil types area in retrieving LAI. Important findings This paper demonstrates the effects of soil types underneath the canopy on LAI retrieving. 1) The sensitivity of spectral indices to LAI is significantly different due to the soil type variation, but REIP has the least effects from soil type variation among the eight spectral indices. Meanwhile, the band selection algorithm of lambda-by-lambda not only chooses the most sensitive spectral interval for LAI, but also provides a feasible way to construct the spectral index that exhibits strong resistances to the effects of soil types underneath the canopy. 2) The accuracy of LAI estimation by regression models differs under soil type considered or not. So we suggest that in small scale researches, especially in a field scale, the ability of regression models in explaining variables is the priority consideration, while the PLSR is superior to RFR in this respect. Under the premise of unknown priori knowledge of land surfaces, the RFR is more suitable for retrieving LAI than PLSR, but land surface priori knowledge is still necessary. These findings provide the theoretical basis and methods for developing remotely sensing estimating LAI models adapted to various land surfaces. Further analysis is needed in applying the findings in more crop types, cultivars and growth stages.     

Drought effects on canopy properties and productivity in thinned and unthinned Turkey oak stands

ABSTRACT

Drought responses, leaf area index (LAI), leaf characteristics and light extinction coefficient (k) were analysed in thinned and unthinned Turkey oak (Quercus cerris L.) stands at two sites: Valsavignone, in the Apennines, with a mild climate, and Caselli, near the Tyrrhenian coast, with a longer and more accentuated dry period in the summer. Turkey oak showed a good adaptability to drought due to a series of modifications in leaf characteristics, canopy properties and biomass allocation such as leaf area reduction, increased leaf thickness, smaller number of leaves and, at stand level, lower LAI, leaf biomass and LWR values and higher light extinction coefficients. In spite of the better environmental conditions and the higher LAI values, productivity was lower in the wet site. The differences in Turkey oak canopy properties, light extinction coefficients, LAI and their relations with drought and productivity are discussed.     

Phyllostomid Bat Community Structure and Abundance in Two Contrasting Tropical Dry Forests1

Although tropical wet forests are generally more diverse than dry forests for many faunal groups, few studies have compared bat diversity among dry forests. I compared ground level phyllostomid bat community structure between two tropical dry forests with different precipitation regimes. Parque National Palo Verde in northwestern Costa Rica represents one of the wettest tropical dry forests (rainfall 1.5 m/yr), whereas the Chamela‐Cuixmala Biosphere Reserve on the Pacific coast of central Mexico represents one of the driest (750 mm/yr). Mist net sampling was conducted at the two study sites to compare changes in ground level phyllostomid bat community structure between regions and seasons. Palo Verde was more diverse than Chamela and phyllostomid species showed low similarity between sites (Classic Jaccard = 0.263). The distinct phyllostomid communities observed at these two dry forest sites demonstrates that variants of tropical dry forest can be sufficiently different in structure and composition to affect phyllostomid communities. At both dry forest sites, abundance of the two most common foraging guilds (frugivores and nectarivores) differed between seasons, with greatest numbers of individuals captured coinciding with highest chiropterophilic resource abundance.     

油菜绿色面积指数动态模拟模型

准确模拟绿色面积指数是作物生长模拟模型可靠预测作物生长和产量的关键。该研究的目的是以生理生态过程为基础,构建油菜(Brassica napus)叶面积指数和角果面积指数变化动态的模拟模型。油菜叶面积指数模型综合考虑了库或源限制下的叶面积增长模式,其中库限制下叶面积指数的增长呈指数方程,且受到温度、水分和氮素因子的影响;源限制下叶面积指数增长用比叶面积法来模拟。油菜角果面积指数由比角果面积和角果干物重来决定。比叶面积和比角果面积均为生理发育时间的函数。利用不同类型品种的播期试验及氮肥试验资料分别对模型进行了校正和检验,结果表明模型能较好地模拟不同条件下油菜叶面积指数和角果面积指数。     

Are cloud forest tree structure and environment related in the Venezuelan Andes?

Cloud forest vegetation structure and composition were studied in the Venezuelan Andes at three sites in Mérida State. Although the sites are within 10 to 30 km of each other, climatic, geologic and topographic differences are remarkable. The main purpose of the study was to determine the relationship of specific environmental variables to forest vegetation characteristics, including basal area, tree height, density and diversity, and leaf area index (LAI). At 51 plots, all trees' diameter at breast height >10 cm were recorded and identified. Although the environment at the three sites is distinctive, the tree species composition of the most abundant species was very similar. None of the measured environment variables were significantly correlated with the measured vegetation structure variables, except LAI, which was correlated with slope orientation; LAI showed higher values at south‐facing plots. Tree height was relatively uniform, while basal area was highly variable and reached very high values. Stem densities were in the range reported elsewhere in cloud forests. Multivariate analysis using structure or composition data shows segregation of the plots by site. Principal component analyses by site indicate a minor impact of environmental factors on forest variables. At each site, a particular group of species are correlated with the ordination axes. We conclude that species pools and forest dynamics add to the complexity of the structure of the studied cloud forests.     

Effect of prey availability on the abundance of White‐breasted Wood‐Wrens,insectivorous birds of tropical lowland forests

Some understory insectivorous birds manage to persist in tropical forest fragments despite significant habitat loss and forest fragmentation. Their persistence has been related to arthropod biomass. In addition, forest structure has been used as a proxy to estimate prey availability for understory birds and for calculating prey abundance. We used arthropod biomass and forest structural variables (leaf area index [LAI] and aerial leaf litter biomass) to explain the abundance of White‐breasted Wood‐Wrens (Henicorhina leucosticta), tropical understory insectivorous birds, in six forests in the Caribbean lowlands of Costa Rica. To estimate bird abundance, we performed point counts (100‐m radius) in two old‐growth forests, two second‐growth forests, and two selectively logged forests. Arthropod abundance was the best predictor of wood‐wren abundance (wi = 0.75). Wood‐wren abundance increased as the number of arthropods increased, and the estimated range of bird abundance obtained from the model varied from 0.51 (0.28 – 0.93 [95%CI]) to 3.70 (1.68 – 5.20 [95%CI]) within sites. LAI was positively correlated to prey abundance (P = 0.01), and explained part of the variation in wood‐wren abundance. In forests with high LAI, arthropods have more aerial leaf litter as potential habitat so more potential prey are available for wood‐wrens. Forests with a greater abundance of aerial leaf litter arthropods were more likely to sustain higher densities of wood‐wrens in a fragmented tropical landscape.