Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones

In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity.     

Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

Both moderate and high spatial resolution imagery can be used to quantify abundance and distribution of urban vegetation for urban landscape management and to provide inputs to physical process models. Estimation of vegetation fraction from Landsat ETM+ and Quickbird allows for operational monitoring and reconnaissance at moderate resolution with calibration and vicarious validation at higher resolution. Establishing a linear correspondence between ETM-derived vegetation fraction and Quickbird-derived vegetation fraction facilitates the validation task by extending the spatial scale from 30 × 30 m to a more manageable 2.8 × 2.8 m. A comparative analysis indicates that urban reflectance can be accurately represented with a three component linear mixture model for both Landsat ETM+ and Quickbird imagery in the New York metro area. The strong linearity of the Substrate Vegetation Dark surface (SVD) mixture model provides consistent estimates of illuminated vegetation fraction that can be used to constrain physical process models that require biophysical inputs related to vegetation abundance. When Quickbird-derived 2.8 m estimates of vegetation fraction are integrated to 30 m scales and coregistered to Landsat-derived 30 m estimates, median estimates agree with the integrated fractions to within 5% for fractions > 0.2. The resulting Quickbird-ETM+ scatter distribution cannot be explained with estimate error alone but is consistent with a 3% to 6% estimation error combined with a 17 m subpixel registration ambiguity. The 3D endmember fraction space obtained from ETM+ imagery forms a ternary distribution of reflectance properties corresponding to distinct biophysical surface types. The SVD model is a reflectance analog to Ridd's V–I–S land cover model but acknowledges the fact that permeable and impermeable surfaces cannot generally be distinguished on the basis of broadband reflectance alone. We therefore propose that vegetation fraction be used as a proxy for permeable surface distribution to avoid the common erroneous assumption that all nonvegetated surfaces along the gray axis are completely impermeable. Comparison of mean vegetation fractions to street tree counts in New York City shows a consistent relationship between minimum fraction and tree count. However, moderate and high resolution areal estimates of vegetation fraction provide complementary information because they image all illuminated vegetation, including that not counted by the in situ street tree inventory.     

ESTIMATION OF JUVENILE SALMON HABITAT IN PACIFIC RIM RIVERS USING MULTISCALAR REMOTE SENSING AND GEOSPATIAL ANALYSIS

We conducted a regional classification and analysis of riverine floodplain physical features that represent key attributes of salmon rearing habitats. Riverine habitat classifications, including floodplain area and river channel complexity, were derived at moderate (30 m) spatial resolution using multispectral Landsat imagery and global terrain data (90 m) encompassing over 3 400 000 km² and most North Pacific Rim (NPR) salmon rivers. Similar classifications were derived using finer scale (i.e. ≤ 2.4‐m resolution) remote sensing data over a smaller set of 31 regionally representative flood plains. A suite of physical habitat metrics (e.g. channel sinuosity, nodes, floodplain width) were derived from each dataset and used to assess the congruence between similar habitat features at the different spatial scales and to evaluate the utility of moderate scale geospatial data for determining abundance of selected juvenile salmon habitats relative to fine scale remote sensing measurements. The resulting habitat metrics corresponded favorably (p < 0.0001) between the moderate scale and the fine scale floodplain classifications; a subset of these metrics (channel nodes and maximum floodplain width) also were strong indicators (R² > 0.5, p < 0.0001) of floodplain habitats defined from the finer scale analysis. These relationships were used to estimate the abundance and distribution of three critical shallow water floodplain habitats for juvenile salmon (parafluvial and orthofluvial springs, and shallow shore) across the entire NPR domain. The resulting database provides a potential tool to evaluate and prioritize salmon conservation efforts both within individual river systems and across major catchments on the basis of physical habitat distribution and abundance. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

This study was part of an interdisciplinary research project on soil carbon and phytomass dynamics of boreal and arctic permafrost landscapes. The 45 ha study area was a catchment located in the forest tundra in northern Siberia, approximately 100 km north of the Arctic Circle.The objective of this study was to estimate aboveground carbon (AGC) and assess and model its spatial variability. We combined multi-spectral high resolution remote sensing imagery and sample based field inventory data by means of the k-nearest neighbor (k-NN) technique and linear regression.Field data was collected by stratified systematic sampling in August 2006 with a total sample size of n = 31 circular nested sample plots of 154 m² for trees and shrubs and 1 m² for ground vegetation. Destructive biomass samples were taken on a sub-sample for fresh weight and moisture content. Species-specific allometric biomass models were constructed to predict dry biomass from diameter at breast height (dbh) for trees and from elliptic projection areas for shrubs.Quickbird data (standard imagery product), acquired shortly before the field campaign and archived ASTER data (Level-1B product) of 2001 were geo-referenced, converted to calibrated radiances at sensor and used as carrier data. Spectral information of the pixels which were located in the inventory plots were extracted and analyzed as reference set. Stepwise multiple linear regression was applied to identify suitable predictors from the set of variables of the original satellite bands, vegetation indices and texture metrics. To produce thematic carbon maps, carbon values were predicted for all pixels of the investigated satellite scenes. For this prediction, we compared the kNN distance-weighted classifier and multiple linear regression with respect to their predictions.The estimated mean value of aboveground carbon from stratified sampling in the field is 15.3 t/ha (standard error SE = 1.50 t/ha, SE% = 9.8%). Zonal prediction from the k-NN method for the Quickbird image as carrier is 14.7 t/ha with a root mean square error RMSE = 6.42 t/ha, RMSE_r = 44%) resulting from leave-one-out cross-validation. The k-NN-approach allows mapping and analysis of the spatial variability of AGC. The results show high spatial variability with AGC predictions ranging from 4.3 t/ha to 28.8 t/ha, reflecting the highly heterogeneous conditions in those permafrost-influenced landscapes. The means and totals of linear regression and k-NN predictions revealed only small differences but some regional distinctions were recognized in the maps.     

Spectral‐ALS data fusion for different roughness parameterizations of forested floodplains

Natural river floodplains and adjacent wetlands grow typically a diverse and heterogeneous combination of herbs, shrubs and trees, which play an essential role in determining the total flow resistance. Hydrodynamic effects of trees in forested floodplains can provide the majority of flow resistance during flood events. Nevertheless, ground‐based techniques to acquire vegetation parameters are expensive and difficult to apply over long reaches. This paper presents a novel method of automated roughness parameterization of riparian woody vegetation by fusion of Quickbird multi‐spectral image with airborne laser scanning (ALS) data. The data fusion approach includes: individual tree detection and estimation of vegetation metrics from light detection and ranging (LiDAR) data, assessment of predictive models for the vegetation parameters and spatial mapping of the vegetation parameters for the forest plants in the riparian corridor. The proposed method focuses on estimation of plant density (d), crown diameters (D_C), tree height (h), stem diameter (D_S), crown base height (cbh) and leaf area index (LAI). The procedure is tested along a 14‐km reach of the Sieve River (Tuscany, Italy) characterized by high woody plant density. Due to the complex study area, the data fusion approach explains with variable reliability the local vegetation properties (R²(D_C) = 0.14, R²(h) = 0.84, R²(D_S) = 0.25, R²(cbh) = 0.66). The generated structural parameter maps represent spatially explicit data layers that can be used as inputs to hydrodynamic models used to analyse flow resistance effects in different submergence conditions of vegetation. A simple flow resistance model was applied over a test area comparing the results of the proposed method and a traditional ground‐based approach. The modelling results showed that the new method is able to provide accurate output data to describe the interaction between water levels and bio‐mechanical characteristics of vegetation. The proposed methodology provides a fast, repeatable and accurate way of obtaining floodplain roughness, which enables regular updating of vegetation characteristics. Copyright © 2010 John Wiley & Sons, Ltd.     

Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)

Understanding, monitoring and modelling attributes of seagrass biodiversity, such as species composition, richness, abundance, spatial patterns, and disturbance dynamics, requires spatial information. This work assessed the accuracy of commonly available airborne hyper-spectral and satellite multi-spectral image data sets for mapping seagrass species composition, horizontal horizontal-projected foliage cover and above-ground dry-weight biomass. The work was carried out on the Eastern Banks in Moreton Bay, Australia, an area of shallow and clear coastal waters, containing a range of seagrass species, cover and biomass levels. Two types of satellite image data were used: Quickbird-2 multi-spectral and Landsat-5 Thematic Mapper multi-spectral. Airborne hyper-spectral image data were acquired from a CASI-2 sensor using a pixel size of 4.0 m. The mapping was constrained to depths shallower than 3.0 m, based on past modelling of the separability of seagrass reflectance signatures at increasing water depths. Our results demonstrated that mapping of seagrass cover, species and biomass to high accuracy levels (> 80%) was not possible across all image types. For each parameter mapped, airborne hyper-spectral data produced the highest overall accuracies (46%), followed by Quickbird-2 and then Landsat-5 Thematic Mapper. The low accuracy levels were attributed to the mapping methods and difficulties in matching locations on image and field data sets. Accurate mapping of seagrass cover, species composition and biomass, using simple approaches, requires further work using high-spatial resolution (< 5 m) and/or hyper-spectral image data. Further work is required to determine if and how the seagrass maps produced in this work are suitable for measuring attributes of seagrass biodiversity, and using these data for modelling floral and fauna biodiversity properties of seagrass environments, and for scaling-up seagrass ecosystem models.     

Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications

Urban vegetation cover is a critical component in urban systems modeling and recent advances in remote sensing technologies can provide detailed estimates of vegetation characteristics. In the present study we classify urban vegetation characteristics, including species and condition, using an approach based on spectral unmixing and statistically developed decision trees. This technique involves modeling the location and separability of vegetation characteristics within the spectral mixing space derived from high spatial resolution Quickbird imagery for the City of Vancouver, Canada. Abundance images, field based land cover observations and shadow estimates derived from a LiDAR (Light Detection and Ranging) surface model are applied to develop decision tree classifications to extract several urban vegetation characteristics. Our results indicate that along the vegetation-dark mixing line, tree and vegetated ground cover classes can be accurately separated (80% and 94% of variance explained respectively) and more detailed vegetation characteristics including manicured and mixed grasses and deciduous and evergreen trees can be extracted as second order hierarchical categories with variance explained ranging between 67% and 100%. Our results also suggest that the leaf-off condition of deciduous trees produce pixels with higher dark fractions resulting from branches and soils dominating the reflectance values. This research has important implications for understanding fine scale biophysical and social processes within urban environments.     

城市地区ETM和QUICKBIRD影像NDVI值比较研究

通过比较ETM和Quickbird两个数据的归一化插值植被指数,来判断它们在反映植被覆盖度方面的效果。结果表明,在尺度较小并且地物景观比较复杂的城市地区,高分辨率的Quickbird影像能够更好地观测到小范围地区的NDVI值。对于城市地区,由于绿地面积相对较小,因此最好利用高分辨率的Quickbird数据,而对于大尺度或植被景观比较单一地区,二者的差异不明显。由于ETM影像的成本相对较低而且波谱范围更加广泛,故在大尺度地区使用TM影像监测植被变化更加合适。     

一种改进的基于ICA特征空间的高空间分辨率影像的目标识别

为解决高空间分辨率影像目标的识别问题,一种好的方式是将充分考虑高阶累积量的独立分量分析方法引入高空间分辨率影像进行特征提取,但由于基于传统独立成分分析方法提取的特征空间不能最优区分不同类别的样本。为此,提出一种改进的基于独立成分分析的目标识别方法（Multi-ICA）。该方法为每个类别的样本构造单独的特征空间,通过投影到特征空间,得到表征该类别样本特征的特征向量集合。Multi-ICA方法提取的特征空间是基于某类样本图像的共性特征建立的,同一类别样本间的欧式距离要小于不同类别样本之间的欧式距离。因此,可以将待识别样本分类到具有最小欧式距离的特征空间所对应的类别上。现以北京地区的高分辨率卫星Quickbird影像为例,进行了Multi-ICA、传统ICA方法、主成分分析（PCA）方法,以及Multi-PCA方法的目标识别对比实验。结果表明,提出Multi-ICA算法的识别率有明显的提高,并且在一定程度上缓解了由于样本数量增加导致样本特征向量维数增加的问题。     

中高空间分辨率多光谱遥感数据在坦桑尼亚鲁夸地区找矿预测中的应用

坦桑尼亚中西部的鲁夸地区是重要的铁铜金多金属成矿区,矿产资源丰富,找矿前景较好。文章以Manyoro和Sangu两个地区为研究区,利用中高空间分辨率的多光谱数据,结合已知地质资料的分析,进行了遥感岩性地层和构造解译,并基于岩矿光谱特征,提取出了铁染和羟基蚀变信息,通过成矿地质条件和找矿预测研究,圈定了5处遥感找矿靶区,为坦桑尼亚中西部鲁夸地区进一步开展矿产资源调查工作提供了基础资料和科学依据。