Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR

Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests.

The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated < 6.8% and < 18.6% error rates in the detection of invasive tree species at  7 m² and  2 m² minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them.     

Spectroscopic classification of tropical forest species using radiative transfer modeling

Leaf spectroscopy may be useful for tropical species discrimination, but few studies have provided an understanding of the spectral separability of species or how leaf spectroscopy scales to the canopy level relevant to mapping. Here we report on a study to classify humid tropical forest canopy species using field-measured leaf optical properties with leaf and canopy radiative transfer models. The experimental dataset included 188 canopy species collected in humid tropical forests of Hawaii. The leaf optical model PROSPECT-5 was used to simulate the leaf spectra of each species, which was used to train a classifier based on Linear Discriminant Analysis, and a canopy radiative transfer model 4SAIL2 to scale leaf measurements to the canopy level. The relationship linking classification accuracy at the leaf level to biodiversity showed an asymptotic trend reaching a maximum error of 47% when applied to the entire 188 species experimental dataset, and 56% when a simulated dataset showing amplified within-species spectral variability was used, suggesting uniqueness of the spectral signature for a significant proportion of species under study. The maximum error in canopy-level species classification was higher than leaf-level classification: 55% when canopy structure was held constant, and 64% with varying and unknown canopy structure. However, when classifying fewer species at a time, errors dropped considerably; for example, 20 species can be classified to 82-88% accuracy. These results highlight the potential of imaging spectroscopy to provide species discrimination in high-diversity, humid tropical forests.     

Canopy shadow in IKONOS satellite observations of tropical forests and savannas

The biological and structural complexity of tropical forests and savannas results in marked spatial variation in shadows inherent to remotely sensed measurements. While the biophysical and observational factors driving variations in apparent shadow are known, little quantitative information exists on the magnitude and variability of shadow in remotely sensed data acquired over tropical regions. Even less is known about shadow effects in multispectral observations from satellites (e.g., Landsat). The IKONOS satellite, with 1-m panchromatic and 4-m multispectral capabilities, provides an opportunity to observe tropical canopies and their shadows at spatial scales approaching the size of individual crowns and vegetation clusters.We used 44 IKONOS images from the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) data archive to quantify the spatial variation of canopy shadow fraction across a broad range of forests in the Brazilian Amazon and savannas in the Brazilian Cerrado. Forests had substantial apparent shadow fractions as viewed from the satellite vantage point. The global mean (±S.D.) shadow fraction was 0.25±0.12, and within-scene (e.g., forest stand) variability was similar to interscene (e.g., regional) variation. The distribution of shadow fractions for forest stands was skewed, with 30% of pixels having fractional shadow values above 0.30. Shadow fractions in savannas increased from 0.0±0.01 to 0.12±0.04 to 0.16±0.05 for areas with woody vegetation at low (<25% cover), medium (25-75%), and high (>75%) density, respectively.Landsat-like observations using both red (0.63-0.70 μm) and near-infrared (NIR) (0.76-0.85 μm) wavelength regions were highly sensitive to sub-pixel shadow fractions in tropical forests, accounting for ∼30-50% of the variance in red and NIR responses. A 10% increase in shadow fraction resulted in a 3% and 10% decrease in red and NIR channel response, respectively. The normalized difference vegetation index (NDVI) of tropical forests was weakly sensitive to changes in shadow fraction. For low-, medium-, and high-density savannas, a 10% increase in shadow fraction resulted in a 5-7% decrease in red-channel response. Shadows accounted for ∼15-50% of the overall variance in red-wavelength responses in the savanna image archive. Weak to no relationship occurred between shadow fraction and either NIR reflectance or the NDVI of savannas. Quantitative information on shadowing is needed to validate or constrain radiative transfer, spectral mixture, and land-surface models used to estimate material and energy exchanges between the tropical biosphere and atmosphere.     

Remote sensing of native and invasive species in Hawaiian forests

Detection and mapping of invasive species is an important component of conservation and management efforts in Hawai'i, but the spectral separability of native, introduced, and invasive species has not been established. We used high spatial resolution airborne imaging spectroscopy to analyze the canopy hyperspectral reflectance properties of 37 distinct species or phenotypes, 7 common native and 24 introduced tree species, the latter group containing 12 highly invasive species. Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) reflectance and derivative-reflectance signatures of Hawaiian native trees were generically unique from those of introduced trees. Nitrogen-fixing trees were also spectrally unique from other groups of non-fixing trees. There were subtle but significant differences in the spectral properties of highly invasive tree species in comparison to introduced species that do not proliferate across Hawaiian ecosystems. The observed differences in canopy spectral signatures were linked to relative differences in measured leaf pigment (chlorophyll, carotenoids), nutrient (N, P), and structural (specific leaf area; SLA) properties, as well as to canopy leaf area index. These leaf and canopy properties contributed variably to the spectral separability of the trees, with wavelength-specific reflectance and absorption features that overlapped, but which were unique from one another. A combination of canopy reflectance from 1125–2500 nm associated with leaf and canopy water content, along with pigment-related absorption features (reflectance derivatives) in the 400–700 nm range, was best for delineating native, introduced, and invasive species. There was no single spectral region that always defined the separability of the species groups, and thus the full-range (400–2500 nm) spectrum was highly advantageous in differentiating these groups. These results provide a basis for more detailed studies of invasive species in Hawai'i, along with more explicit treatment of the biochemical properties of the canopies and their prediction using imaging spectroscopy.     

Remote Sensing Inversion of Soil Zinc Pollution in Gejiu Mining Area of Yunnan

As a product of the development of modern industry and mining industry,heavy metal Zn pollution has gradually invaded the daily production and life of human beings,which is harmful to our physical and mental health.In dealing with large-scale soil environmental monitoring.The traditional heavy metal monitoring method is time-consuming and laborious.Due to its characteristics of high speed,high speed and high efficiency,remote sensing technology has become an important tool for environmental monitoring in the new era.This study takes Yunnan Gejiu mining area as a typical area,collecting sample in field soil and measurement of soil sample spectra and Zn content.Then the band transform method based on the multiplicative transformation was proposed to enhance product conversion relationship between Zn elements and spectral sensitive bands,using the established prediction model and optimal Zn content based on ASTER images to carry out pollution mapping.Research shows that：①the maximum correlation band of Zn elements is the B515 band,close to the absorption peak of sphalerite and smithsonite zinc containing minerals,is an important band of zinc element inversion of soil;②the spectral multiplicative transformation can highlight the sensitive bands of Zn elements,and retain the most sensitive information of the original soil;③in the hypersecretion inversion model of soil zinc content in the study area,the precision of the model established by partial least squares(R=0.90)is the highest(R=0.70);④The inversion results based on ASTER images show that there is a significant correlation between soil Zn pollution and mining activities(Verification accuracy of map R=0.694).The results of this study can provide the basis and technical support for remote sensing quantitative inversion of heavy metal content and large-scale environmental pollution monitoring.     

Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling

We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.     

Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape

Mapping requires a meaningful generalization of information. For vegetation maps, classification is frequently used to generalize the species composition of (semi-)natural plant assemblages. As an alternative to classification, ordination methods aim to extract major floristic gradients describing the prevailing compositional variation in a floristic data set as metric variables. This ability has been used previously to derive gradient maps of homogeneous landscapes that show plant species composition in continuous fields. In the present study, gradient mapping was used in a more heterogeneous landscape with intricate environmental gradients and higher variation in vegetation physiognomy. Since established ordination methods may have difficulties to cope with the highly variable plant species composition, we tested the novel method Isometric Feature Mapping (Isomap) against conventional methods (Detrended Correspondence Analysis and Nonmetric Multidimensional Scaling). The resulting floristic gradients were related to hyperspectral imagery (HyMap) using partial least squares regression (PLSR) and subsequently mapped. Prediction uncertainties are provided as additional map. Isomap was able to preserve 74% of the original variation inherent to the floristic data set in a three-dimensional solution. This was considerably more than the established techniques achieved. The PLSR models for the floristic gradients extracted with Isomap showed model fits ranging from R² = 0.59 to R² = 0.73 in calibration and from R² = 0.55 to R² = 0.69 in tenfold cross-validation. The resulting gradient map provides detailed information on compositional vegetation patterns.     

Jackknife and bootstrap methods in the identification of dynamic models

A new criterion based on a Jackknife or a Bootstrap statistic is proposed for identifying non-parsimonious dynamic models (FIR, ARX). It is applicable for selecting the number of components in latent variable regression methods or the constraining parameter in regularized least squares regression methods. These meta parameters are used to overcome ill-conditioning caused by model over-parameterization, when fitted using prediction error or least squares methods. In all cases studied, using PLS for parameter estimation, the proposed criterion led to the selection of better models, in the mean square error sense, than when selected via cross-validation. The methodology also provides approximate confidence intervals for the model parameters and the step and impulse response of the system.     

可见光—近红外光谱估算三江源区不同土壤全氮含量

近年来可见光—近红外反射光谱已被广泛应用于估算土壤全氮含量,为大范围区域土壤全氮含量获取提供了一种快速、有效的方法。基于实验室测定的三江源区146个表层土壤(0~30cm)样品的反射光谱数据(350~2 500nm)与全氮含量数据;利用偏最小二乘回归(PLSR)和反向传播神经网络(BPNN)两种模型方法与光谱反射率(REF)及其4种数学预处理变换相结合,分别建立分土壤类型样本和总体样本全氮估算模型;评估利用可见光—近红外光谱技术预测三江源区土壤全氮含量的能力。结果表明:BPNN模型的R2cal、R2val及验证RPD的平均值分别为0.87、0.81与2.28;而PLSR模型则相应为0.75、0.72和1.95;表明BPNN模型预测能力整体上要优于PLSR模型。BPNN与光谱各种形式的结合均具有良好、或接近良好预测全氮的能力;而PLSR与REF、倒数对数(Log(1/R))及波段深度(BD)的结合仅少部分具有良好估算能力、大部分则为粗略估算能力,一阶微分(FDR)和二阶微分(SDR)估算精度均较低,尤其是SDR(R20.5,RPD=1.10~1.27)均不具备估算能力。总体样本所建模型稳定性好于分土壤类型,分土壤类型建模差异性明显;此外,总体来看,BPNN模型比PLSR建模精度高、模型稳定性好,但PLSR模型可操作性强于BPNN模型。     

近红外光谱技术快速测定当归中藁本内酯含量

基于近红外光谱技术,运用偏最小二乘回归(PLSR)方法实现当归中藁本内酯含量的快速、无损检测.采用高效液相色谱(HPLC)法测定当归中藁本内酯含量,一阶导数结合正交信号校正对原始光谱进行预处理,建立当归近红外光谱和藁本内酯含量之间的最小二乘回归定量分析模型.结果表明:模型在校正集上的均方根误差(RMSEE)、交叉验证均方根误差(RMSECV)和决定系数R2分别为0.199 9,0.3489和0.9932,在预测集上的预测均方根误差(RMSEP)和决定系数R2分别为0.23和0.9941.方法具有简单、快速、不破坏样品等特点,可用于当归中藁本内酯含量的快速检测.