Classifying tropical deciduous vegetation: a comparison of multiple approaches in Popa Mountain Park,Myanmar

Although several studies have reported that rule-based methods are better than other image classification methods, no study has quantified their performance for tropical deciduous vegetation classification. We compared rule-based and maximum likelihood classification (MLC) approaches in classifying tropical deciduous vegetation in Popa Mountain Park, Myanmar. Classification was primarily based on Thematic Mapper (TM) bands of multi-season Landsat images, normalized difference vegetation indices (NDVIs), NDVI differences, mean NDVI and elevation (advanced spaceborne thermal emission and reflection radiometer digital elevation model (Aster DEM)). We used two main approaches for classification, a single-step approach in which all vegetation types were classified in one procedure, and a two-step approach in which forest and non-forest were discriminated first and then forest was classified into additional classes. Each of those approaches was conducted with and without elevation under the rule-based and MLC approaches, yielding eight separate methods. The two-step approaches generated more accurate results and all classifications improved markedly when elevation was included. The rule-based two-step with elevation approach produced the best overall accuracy and reliability.     

Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data

The ability of synthetic aperture radar (SAR) C-band microwave energy to penetrate within forest vegetation makes it possible to extract information on crown components, which in turn gives a better approximation of relative canopy density than optical data-derived canopy density. Many studies have been reported to estimate forest biomass from SAR data, but the scope of C-band SAR in characterizing forest canopy density has not been adequately understood with polarimetric techniques. Polarimetric classification is one of the most significant applications of polarimetric SAR in remote sensing. The objective of the present study was to evaluate the feasibility of different polarimetric SAR data decomposition methods in forest canopy density classification using C-band SAR data. Landsat (Land Satellite) 5 TM (Thematic Mapper) data of the same area has been used as optical data to compare the classification result. RADARSAT (Radar Satellite)-2 image with fine quad-pol obtained on 27 October 2011 over tropical dry forests of Madhav National Park, India, was used for the analysis of full polarimetric data. Six decomposition methods were selected based on incoherent decomposition for generating input images for classification, i.e. Huynen, Freeman and Durden, Yamaguchi, Cloude, Van zyl, and H/A/α. The performance of each decomposition output in relation to each land cover unit present in the study area was assessed using a support vector machine (SVM) classifier. Results show that Yamaguchi 4-component decomposition (overall accuracy 87.66% and kappa coefficient (κ) 0.86) gives better classification results, followed by Van Zyl decomposition (overall accuracy 87.20% and κ 0.85) and Freeman and Durden (overall accuracy 86.79% and κ 0.85) in forest canopy density classification. Both model-based decompositions (Freeman and Durden and Yamaguchi4) registered good classification accuracy. In eigenvector or eigenvalue decompositions, Van zyl registered the second highest accuracy among different decompositions. The experimental results obtained with polarimetric C-band SAR data over a tropical dry deciduous forest area imply that SAR data have significant potential for estimating canopy density in operational forestry. A better forest density classification result can be achieved within the forest mask (without other land cover classes). The limitations associated with optical data such as non-availability of cloud-free data and misclassification because of gregarious occurrence of bushy vegetation such as Lantana can be overcome by using C-band SAR data.     

Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote-sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images and different classification algorithms, maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA) and object-based classification (OBC), were explored. The results indicate that a combination of vegetation indices as extra bands into Landsat TM multi-spectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multi-spectral bands improved the overall classification accuracy (OCA) by 5.6% and the overall kappa coefficient (OKC) by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes that have complex stand structures and large patch sizes.     

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.     

Linear mixture model applied to Amazonian vegetation classification

Many research projects require accurate delineation of different secondary succession (SS) stages over large regions/subregions of the Amazon basin. However, the complexity of vegetation stand structure, abundant vegetation species, and the smooth transition between different SS stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier (MLC). Most of the time, classification distinguishes only between forest/non-forest. It has been difficult to accurately distinguish stages of SS. In this paper, a linear mixture model (LMM) approach is applied to classify successional and mature forests using Thematic Mapper (TM) imagery in the Rondônia region of the Brazilian Amazon. Three endmembers (i.e., shade, soil, and green vegetation or GV) were identified based on the image itself and a constrained least-squares solution was used to unmix the image. This study indicates that the LMM approach is a promising method for distinguishing successional and mature forests in the Amazon basin using TM data. It improved vegetation classification accuracy over that of the MLC. Initial, intermediate, and advanced successional and mature forests were classified with overall accuracy of 78.2% using a threshold method on the ratio of shade to GV fractions, a 7.4% increase over the MLC. The GV and shade fractions are sensitive to the change of vegetation stand structures and better capture biophysical structure information.     

Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia

This paper reports on a test of the ability to estimate above-ground biomass of tropical secondary forest from canopy spectral reflectance using satellite optical data. Landsat Thematic Mapper data were acquired concurrent with field surveys conducted in secondary forest fallows near Manaus, Brazil and Santa Cruz de la Sierra, Bolivia. Measurements of age and above-ground live biomass were made in 34 regrowth stands. Satellite data were converted to surface reflectances and compared with regrowth stand age, biomass and structural variables. Among the Brazilian stands, significant relationships were observed between middle-infrared reflectance and stand age, height, volume and biomass. The canopy reflectance-biomass relationship saturated at around 15.0 kg m^-2, or over 15 years of age (r > 0.80, p < 0.01). In the Bolivian study area, no significant relationship between canopy spectral reflectance and biomass was observed. These contrasting results are probably caused by a low Sun angle during the satellite measurements from Bolivia. However, regrowth structural and general compositional differences between the two study areas could explain the lack of a significant relationship in Bolivia. The results demonstrate a current potential for biomass estimation of secondary forests with satellite optical data in some, but not all, tropical regions. A discussion of the potential for regional extrapolation of spectral relationships and future satellite imagery is included.     

Quantifying empirical relations between planted species mixtures and canopy reflectance with PROTEST

Mapping plant species composition of mixed vegetation stands with remote sensing is a complicated task. Uncertainties may arise from similar spectral signatures of different plant species as well as from variable influences of prevailing plant states (e.g., growth stages, vigor, or stress levels). Despite these uncertainties, empirical approaches may often be able to take up the challenge. However, their performance is likely to be affected by the temporal variability of empirical relations between reflectance and plant species composition. To assess some aspects of this temporal variability, we performed a greenhouse study. Three mixed stands of grassland species were planted with defined spatial variation in species proportions. The canopy reflectance of these mixed stands was measured with a field spectrometer over a period of three months. Confounding external influences on plant states apart from maturation were minimized.The suitability of canopy reflectance and derivative reflectance to draw conclusions on differences in qualitative species mixtures between the stands was tested with a classification approach (Spectral Angle Mapper, SAM). Procrustean randomization test (PROTEST), which is to our knowledge new to the field of remote sensing, was applied in combination with Isometric Feature Mapping to quantify the spectral variation caused by within-stand spatial variation in species proportions. Model fits in both analyses increased with progressing plant development; further, utilization of derivative reflectance improved the model fits. Regardless of the within-stand variation, SAM enabled a successful discrimination of the three stands with an average overall accuracy of 85% (reflectance) and 92% (derivative reflectance). In PROTEST analysis, spatial variation in reflectance was successfully related to within-stand variation in species proportions. However, observed influences of variable growth stages and health states on these relations were considerable. The temporal variation of these relations (r = 0.27-0.73 for reflectance and 0.48-0.73 for derivative reflectance) was quantified for the first time under controlled conditions.     

Mapping the invasive species,Chinese tallow,with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress‐tupelo forest, senescing Chinese tallow with red leaves (‘red tallow’), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress‐tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non‐active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs.     

Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data

Invasive species usually colonize canopy gaps in tropical and subtropical forests, which results in a loss of native species. Therefore, an understanding of the location and distribution of canopy gaps will assist in predicting the occurrence of invasive species in such canopy gaps. We tested the utility of WorldView-2 (WV-2) with eight spectral bands at 2 m spatial resolution to delineate forest canopy gaps in a subtropical Dukuduku coastal forest in South Africa. We compared the four conventional visible-near-infrared bands with the eight-band WV-2 image. The eight-band WV-2 image yielded a higher overall accuracy of 86.90% (kappa coefficient = 0.82) than the resampled conventional four-band image that yielded an overall accuracy of 74.64% (kappa coefficient = 0.63) in pixel-based classification. We further compared the vegetation indices that were derived from four conventional bands with those derived from WV-2 bands. The enhanced vegetation index yielded the highest overall accuracy in the category of conventional indices (85.59% at kappa coefficient = 0.79), while the modified plant senescence reflectance index involving the red-edge band showed the highest overall accuracy (93.69%) in the category of indices derived from eight-band WV-2 imagery in object-based classification. Overall, the study shows that the unique high-resolution WV-2 data can improve the delineation of canopy gaps as compared to the conventional multispectral bands.     

Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images

ABSTRACT

Mapping of the distribution of individual seagrass species is essential for any attempts to manage seagrass ecosystems. It is therefore important to understand how the spectra of different seagrass species vary, in order to establish their unique absorption features and how these can be utilised for mapping by making use of remote-sensing images. This paper presents measurements of the reflectance spectra between 400 and 900 nm for nine tropical species of seagrass. Continuum removal and multispectral resampling procedures were applied to the spectra. Dendrogram analysis was carried out to identify species clustering as the basis for a mapping scheme. Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) approaches were employed for the classification of seagrass species using WorldView-2 images and measured spectra as the input endmember. Classification Tree Analysis (CTA) and an image segmentation approach using CTA (Object-Based Image Analysis – OBIA) were performed as a means of comparison. The results indicate that the absorption features and overall shape of the spectra for all seagrass species are relatively similar, and implied that the major differences are attributable to the absolute reflectance values. Consequently, SAM and SID produced results of low accuracy (<30%), whereas, CTA and OBIA delivered results exhibiting higher accuracy (60–92%). The use of a spectral-based classification algorithm was ineffective for the classification and mapping of seagrass species using multispectral images. The utilisation of absolute reflectance values was beneficial for the classification of seagrass species having similar spectral shape.     

Remote sensing of selective logging in Amazonia: Assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis

We combined a detailed field study of forest canopy damage with calibrated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) reflectance data and texture analysis to assess the sensitivity of basic broadband optical remote sensing to selective logging in Amazonia. Our field study encompassed measurements of ground damage and canopy gap fractions along a chronosequence of postharvest regrowth of 0.5–3.5 years. We found that canopy damage and regrowth rates varied according to the logging method used, either conventional logging or reduced impact logging. Areas used to stage felled trees prior to transport, log decks, had the largest gap fractions immediately following cutting. Log decks were quickly colonized by early successional plant species, resulting in significant gap fraction decreases within 1.5 years after site abandonment. Although log decks were the most obvious damage areas on the ground and in satellite imagery, they accounted for only 1–2% of the total harvested area of the blocks studied. Other forest damage features such as tree-fall gaps, skid trails, and roads were difficult to recognize in Landsat reflectance data or through textural analysis. These landscape features could be only crudely resolved in the most intensively logged forests and within about 0.5 years following harvest. We found that forest damage within any of the landscape strata (decks, roads, skids, tree falls) could not be resolved with Landsat reflectance or texture data when the canopy gap fraction was <50%. The basic Landsat ETM+ imagery lacks the resolution of forest structural features required for quantitative studies of logging damage. Landsat textural analyses may be useful for broad delineation of logged forests, but detailed ecological and biogeochemical studies will probably need to rely on other remote sensing approaches. Until spatial gradients of canopy damage and regrowth resulting from selective logging operations in tropical forests in the Amazon region are resolved, the impacts of this land use on a continental scale will remain poorly understood.     

Phenological differences in Tasseled Cap indices improve deciduous forest classification

Remote sensing needs to clarify the strengths of different methods so they can be consistently applied in forest management and ecology. Both the use of phenological information in satellite imagery and the use of vegetation indices have independently improved classifications of north temperate forests. Combining these sources of information in change detection has been effective for land cover classifications at the continental scale based on Advanced Very High Resolution Radiometer (AVHRR) imagery. Our objective is to test if using vegetation indices and change analysis of multiseasonal imagery can also improve the classification accuracy of deciduous forests at the landscape scale. We used Landsat Thematic Mapper (TM) scenes that corresponded to Populus spp. leaf-on and Quercus spp. leaf-off (May), peak summer (August), Acer spp. peak color (September), Acer spp. and Populus spp. leaf-off (October). Input data files derived from the imagery were: (1) TM Bands 3, 4, and 5 from all dates; (2) Normalized Difference Vegetation Index (NDVI) from all dates; (3) Tasseled Cap brightness, greenness, and wetness (BGW) from all dates; (4) difference in TM Bands 3, 4, and 5 from one date to the next; (5) difference in NDVI from one date to the next; and (6) difference in BGW from one date to the next. The overall kappa statistics (KHAT) for the aforementioned classifications of deciduous genera were 0.48, 0.36, 0.33, 0.38, 0.26, 0.43, respectively. The highest accuracies occurred from TM Bands 3, 4, and 5 (61.0% for deciduous genera, 67.8% for all classes) or from the difference in BGW (61.0% for deciduous genera, 67.8% for all classes). However, the difference in Tasseled Cap classification more accurately separated deciduous shrubs and harvested stands from closed canopy forest. Our results indicate that phenological change of forest is most accurately captured by combining image differencing and Tasseled Cap indices.     

Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).     

A TM-based hardwood-conifer mixture index for closed canopy forests in the Oregon Coast Range

The purpose of this study was to develop and evaluate a multi-spectral vegetation index for quantifying relative amounts of hardwood and conifer cover from Thematic Mapper (TM) imagery. We focused on closed canopy forests in the Oregon Coast Range, where hardwood, conifer, and mixed stand conditions are prevalent. An approach based on the Gramm-Schmidt orthogonalization process was used to derive three different hardwood-conifer mixture indices (HCMIs). Using correlation and regression analyses, the capacity of these indices to predict closed canopy hardwood percentage was compared with three other groups of spectral variables: (1) the untransformed TM reflectance bands, (2) the Tasseled Cap indices of brightness, greenness, and wetness, and (3) the first three principal components of closed canopy forest reflectance. Results show that while similar amounts of information were explained by HCMI, TM band, Tasseled Cap, and principal component models, only predictions derived from the HCMI1 and HCMI2 variables were unbiased with respect to topographic effects.     

Reflectance seasonality and its relation to the canopy leaf area index in an eastern Siberian larch forest: Multi-satellite data and radiative transfer analyses

Reliable monitoring of seasonality in the forest canopy leaf area index (LAI) in Siberian forests is required to advance the understanding of climate-forest interactions under global environmental change and to develop a forest phenology model within ecosystem modeling. Here, we compare multi-satellite (AVHRR, MODIS, and SPOT/VEGETATION) reflectance, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and LAI with aircraft-based spectral reflectance data and field-measured forest data acquired from April to June in 2000 in a larch forest near Yakutsk, Russia. Field data in a 30 × 30-m study site and aircraft data observed around the field site were used. Larch is a dominant forest type in eastern Siberia, but comparison studies that consider multi-satellite data, aircraft-based reflectance, and field-based measurement data are rarely conducted. Three-dimensional canopy radiative transfer calculations, which are based on Antyufeev and Marshak's [Antyufeev, V.S., & Marshak, A.L. (1990). Monte Carlo method and transport equation in plant canopies, Remote Sensing of Environment, 31, 183-191] Monte Carlo photon transport method combined with North's [North, P.R. (1996). Three-dimensional forest light interaction model using a Monte Carlo method, IEEE Transactions on Geoscience and Remote Sensing, 34(4), 946-956] geometric-optical hybrid forest canopy scene, helped elucidate the relationship between canopy reflectance and forest structural parameters, including several forest floor conditions. Aircraft-based spectral measurements and the spectral response functions of all satellite sensors confirmed that biases in reflectance seasonality caused by differences in spectral response functions among sensors were small. However, some reflectance biases occur among the near infrared (NIR) reflectance data from satellite products; these biases were potentially caused by absolute calibration errors or cloud/cloud shadow contamination. In addition, reflectance seasonality in AVHRR-based NIR data was very small compared to other datasets, which was partially due to the spring-to-summer increase in the amount of atmospheric water vapor. Radiative transfer simulations suggest that bi-directional reflectance effects were small for the study site and observation period; however, changes in tree density and forest floor conditions affect the absolute value of NIR reflectance, even if the canopy leaf area condition does not change. Reliable monitoring of canopy LAI is achieved by minimizing these effects through the use of NIR reflectance difference, i.e., the difference in reflectance on the observation day from the reflectance on a snow-free/pre-foliation day. This may yield useful and robust parameters for multi-satellite monitoring of the larch canopy LAI with less error from intersensor biases and forest structure/floor differences. Further validation with field data and combined use of other index (e.g. normalized difference water index, NDWI) data will enable an extension of these findings to all Siberian deciduous forests.     

Modeling forest songbird species richness using LiDAR-derived measures of forest structure

Conservation of biodiversity requires information at many spatial scales in order to detect and preserve habitat for many species, often simultaneously. Vegetation structure information is particularly important for avian habitat models and has largely been unavailable for large areas at the desired resolution. Airborne LiDAR, with its combination of relatively broad coverage and fine resolution provides existing new opportunities to map vegetation structure and hence avian habitat. Our goal was to model the richness of forest songbirds using forest structure information obtained from LiDAR data. In deciduous forests of southern Wisconsin, USA, we used discrete-return airborne LiDAR to derive forest structure metrics related to the height and density of vegetation returns, as well as composite variables that captured major forest structural elements. We conducted point counts to determine total forest songbird richness and the richness of foraging, nesting, and forest edge-related habitat guilds. A suite of 35 LiDAR variables were used to model bird species richness using best-subsets regression and we used hierarchical partitioning analysis to quantify the explanatory power of each variable in the multivariate models. Songbird species richness was correlated most strongly with LiDAR variables related to canopy and midstory height and midstory density (R² = 0.204, p < 0.001). Richness of species that nest in the midstory was best explained by canopy height variables (R² = 0.197, p < 0.001). Species that forage on the ground responded to mean canopy height and the height of the lower canopy (R² = 0.149, p < 0.005) while aerial foragers had higher richness where the canopy was tall and dense and the midstory more sparse (R² = 0.216, p < 0.001). Richness of edge-preferring species was greater where there were fewer vegetation returns but higher density in the understory (R² = 0.153, p < 0.005). Forest interior specialists responded positively to a tall canopy, developed midstory, and a higher proportion of vegetation returns (R² = 0.195, p < 0.001). LiDAR forest structure metrics explained between 15 and 20% of the variability in richness within deciduous forest songbird communities. This variability was associated with vertical structure alone and shows how LiDAR can provide a source of complementary predictive data that can be incorporated in models of wildlife habitat associations across broad geographical extents.     

Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance

PROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (C_w), and leaf dry matter content (C_m). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.     

Modelling land cover changes in the tropical dry forest surrounding the Chamela-Cuixmala biosphere reserve,Mexico

Land-use change is one of the main threats to biodiversity conservation in tropical landscapes and natural protected areas are particularly threatened because of the transformation occurring at their neighbouring environments. We analysed the ‘land-cover’ change (LCC) occurring in the areas surrounding the Chamela-Cuixmala Biosphere Reserve (CCBR) in the Pacific coast of Jalisco, Mexico, during the last 40 years (split in two periods, 1970–1993/94 and 1993/94–2011/12). Supervised classifications were generated by applying a multi-temporal approach on Landsat Thematic Mapper (1993/94) and Satellite Pour l’Observation de la Terre SPOT (2011/12) imagery. The base line (t₀) consisted of an historical 1:50,000 scale map of land-use/land-cover (LULC) made in 1970. In addition to LCC predictions for the next four decades, this study generated a variety of indicators identifying the location and intensity of LCC occurring in the CCBR’s surrounding landscapes. The main vegetation formations, tropical dry forest (TDF), and semi-deciduous tropical forest (SDTF), decreased in area 25% and 50%, respectively, during the 1970–1993/94 period. However, annual rates of change (ARC) showed a deacceleration in the reduction of main vegetation formations, for the period 1993/94–2011/12. This study generated indicators identifying the location and intensity of ‘land-cover’ changes occurring in the CCBR’s surrounding landscapes. Potential transition models made evident the CCBR’s key role as a reservoir of the region’s biodiversity associated to main vegetation types such as the tropical deciduous forest and the semi-deciduous forest.     

Using vegetation reflectance variability for species level classification of hyperspectral data

A study was conducted to investigate whether reflectance data from vegetation in a tropical forest canopy could be used for species level discrimination. Reflectance spectra of 11 species were analysed at the scale of the leaf, branch, tree and species. To enhance separation of species-of-interest spectra from the other spectra in the data, the variation in reflectance values for the species-of-interest were used to create a characteristic spectral shape. With a simple algorithm, the resultant shape-space was used as a data filter that correctly discriminated against 94% of the non-species-of-interest trees.