Examining pine spectral separability using hyperspectral data from an airborne sensor: An extension of field‐based results

Three southern USA forestry species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), and shortleaf pine (Pinus echinata), were previously shown to be spectrally separable (83% accuracy) using data from a full‐range spectroradiometer (400–2500 nm) acquired above tree canopies. This study focused on whether these same species are also separable using hyperspectral data acquired using the airborne visible/infrared imaging spectrometer (AVIRIS). Stepwise discriminant techniques were used to reduce data dimensionality to a maximum of 10 spectral bands, followed by discriminant techniques to measure separability. Discriminatory variables were largely located in the visible and near‐infrared regions of the spectrum. Cross‐validation accuracies ranged from 65% (1 pixel radiance data) to as high as 85% (3×3 pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air‐ or space‐borne sensors.     

Comparing farmer‐based and satellite‐derived deforestation estimates in the Amazon basin using a hybrid classifier

The Amazon basin remains a major hotspot of tropical deforestation, presenting a clear need for timely, accurate and consistent data on forest cover change. We assessed the utility of a hybrid classification technique, iterative guided spectral class rejection (IGSCR), for accurately mapping Amazonian deforestation using annual imagery from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) from 1992 to 2002. The mean overall accuracy of the 11 annual classifications was 95% with a standard deviation of 1.4%, and z‐score analysis revealed that all classifications were significant at the 0.05 level. The IGSCR thus seems inherently suitable for monitoring forest cover in the Amazon. The resulting classifications were sufficiently accurate to assess preliminarily the magnitude and causes of discrepancies between farmer‐reported and satellite‐based estimates of deforestation at the household level using a sample of 220 farms in Rôndonia mapped in the field in 1992 and 2002. The field‐ and satellite‐derived estimates were significantly different only at the 0.10 level for the 220 farms studied, with the satellite‐derived deforestation estimates 8.9% higher than estimates derived from in situ survey methods. Some of this difference was due to a tendency of farmers to overestimate the amount of forest within their property in our survey. Given the objectivity and reduced expense of satellite‐based deforestation monitoring, we recommend that it be an integral part of household‐level analysis of the causes, patterns and processes of deforestation.     

Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south‐western USA

An accuracy assessment of the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous field (VCF) tree cover product using two independent ground‐based tree cover databases was conducted. Ground data included 1176 Forest Inventory and Analysis (FIA) plots for Arizona and 2778 Southwest Regional GAP (SWReGAP) plots for Utah and western Colorado. Overall rms. error was 24% for SWReGAP and 31% for FIA data. VCF bias was positive at low observed tree cover but systematically increased thereafter until at greater than 60% observed tree cover, VCF tree cover was 40% (SWReGAP) to 45% (FIA) too low. Errors are unlikely to be related to habitat fragmentation or variation in canopy height but may be influenced by scaling discontinuities between ground and satellite resolutions.     

Derivation of land surface temperatures from MODIS data using the general split‐window technique

Fast Atmospheric Signature Code (FASCODE), a line‐by‐line radiative transfer programme, was used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) data at wavelengths 11.03 and 12.02 µm to ascertain how accurately the land surface temperature (LST) can be inferred, by the split‐window technique (SWT), for a wide range of atmospheric and terrestrial conditions. The approach starts from the Ulivieri algorithm, originally applied to Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5. This algorithm proved to be very accurate compared to several others and takes into account the atmospheric effects, in particular the water vapour column (WVC) amount and a non‐unitary surface emissivity. Extended simulations allowed the determination of new coefficients of this algorithm appropriate to MODIS bands 31 and 32, using different atmospheric conditions. The algorithm was also improved by removing some of the hypothesis on which its original expression was based. This led to the addition of a new corrective term that took into account the interdependence between water vapour and non‐unitary emissivity values and their effects on the retrieved surface temperature. The LST products were validated within 1 K with in situ LSTs in 11 cases.     

Detection of sandy soil surfaces using ASTER‐derived reflectance,emissivity and elevation data: potential for the identification of land degradation

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reflectance and emissivity data were used to discriminate nonphotosynthetic vegetation (NPV) from exposed soils, to produce a topsoil texture image, and to relate sand fraction estimates with elevation data in an agricultural area of central Brazil. The results show that the combination of the shortwave infrared (SWIR) bands 5 and 6 (hydroxyl absorption band) and thermal infrared (TIR) bands 10 and 14 (quartz reststrahlen feature) discriminated dark red clayey soils and bright sandy soils from NPV (crop litter), respectively. The ratio of the bands 10 and 14 was correlated with laboratory measured total sand fraction. When applied to the image and associated with topography, a predominance of sandy soil surfaces at lower elevations and clayey soil surfaces at higher elevations was observed. Areas presenting the largest sand fraction values, identified from ASTER band 10/14 emissivity ratio, were coincident with land degradation processes.     

Reflectance of floodplain waterbodies using EO‐1 Hyperion data from high and receding flood periods of the Amazon River

The potential of Hyperion images acquired on September 2001 (receding flood period of the Amazon River) and June 2005 (high flood) was investigated for reflectance characterization of selected Amazon floodplain waterbodies using a linear spectral mixture model. The results show the ability of Hyperion to measure adequately the major variation in water reflectance spectral features in response to the annual flood pulse of the Amazon River. Mixture model fraction values were correlated with measured inorganic suspended solids (ISS) but not with chlorophyll (Chl) in the high flood period. Inspection of the fractions across the two images revealed variation in water composition. Small changes in ISS‐ and Chl‐bearing water fractions between the images indicated relatively stable spectral conditions for low (Tapajós River and Lake Juruparipucu) and high (Amazon River) turbidity waterbodies. Large changes indicated reflectance variation in some lakes when the water receded due to algal blooms (Lake Curumu) and sediment resuspension in shallow regions (Lake Aritapera). Although not all water constituents were modelled adequately for quantification purposes, spectral mixture modelling is still an interesting approach for spectral–temporal reflectance characterization of Amazonian floodplains with hyperspectral data.     

The impact of assimilation of MODIS data for the prediction of a tropical low‐pressure system over India using a mesoscale model

A low‐pressure system formed over the Bay of Bengal, India on 15 October 2003 and crossed the east coast of India during the late hours of 17 October 2003. The system, which provided copious rainfall over the Bay of Bengal and stations on the east coast, is investigated in this study using the fifth Generation Mesoscale Model (MM5). Three sets of numerical experiments are designed in this study. While the first set utilizes National Center for Environmental Prediction—Aviation (NCEP‐AVN) analysis (for the initial conditions and lateral boundary conditions) only in the MM5 simulation, the second set utilizes the vertical profiles of temperature and humidity obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) (as well as a few radiosonde station data) to provide an improved analysis. The third set used the vertical profiles of temperature and humidity from MODIS alone to provide an improved analysis. The results of the three sets of simulation are compared with one another as well as with the analysis and observations. It is found that the predicted sea level pressure of the MM5 simulation which utilized the improved analysis: reproduces the large‐scale structure of the low‐pressure system as manifested in the NCEP‐AVN analysis; provides a stronger and deeper low‐pressure system as seen from the sea level pressure field; and shows a larger northward extent of the associated precipitation pattern as compared with the simulation with just the analysis. The results of the third experiment (impact of vertical profiles of temperature and humidity using MODIS alone) compare well with the results of the second experiment except that in the former, the associated cyclonic circulation in the lower troposphere appears weaker. The results of this study, although restricted to a single case study, demonstrate that inclusion of MODIS derived vertical temperature and humidity profiles together with radiosonde data caused a favourable impact on the simulated structure of the low‐pressure system.     

Mapping mineralogical alteration using principal‐component analysis and matched filter processing in the Takab area,north‐west Iran,from ASTER data

The Takab area, located in north‐west Iran, is an important gold mineralized region with a long history of gold mining. The gold is associated with toxic metals/metalloids. In this study, Advanced Space Borne Thermal Emission and Reflection Radiometer data are evaluated for mapping gold and base‐metal mineralization through alteration mapping. Two different methods are used for argillic and silicic alteration mapping: selective principal‐component analysis and matched filter processing (MF). Running a selective principal‐component analysis using the main spectral characteristics of key alteration minerals enhanced the altered areas in PC2. MF using spectral library and laboratory spectra of the study area samples gave similar results. However, MF, using the image reference spectra from principal component (PC) images, produced the best results and indicated the advantage of using image spectra rather than library spectra in spectral mapping techniques. It seems that argillic alteration is more effective than silicic alteration for exploration purposes. It is suggested that alteration mapping can also be used to delineate areas contaminated by potentially toxic metals.     

Investigating chemistry students’ learning about the relationship between the temperature and the pressure of a gas using a microcomputer‐based laboratory (MBL): A word of caution

Abstract

Learning chemistry with understanding is a challenge. From a social constructivist position, we view students’ chemistry learning from experiments as involving the shared negotiation of meaning that uses experimental data to confirm or challenge their existing scientific theories. This study focuses on the practices related to the use of a microcomputer‐based laboratory (MBL) in a high school chemistry course in which students were studying gases and kinetic theory. Given the widely accepted view that the use of such technology is a ‘cure‐all’ for educational problems, student learning using this technology might be considered disappointing. We find that little or no higher‐order thinking was employed as students engaged in using the MBL and that some alternative conceptions were still evident. It is necessary to consider the students’ and the teacher's use of such technology in the experimental context if the promise of its use is to be realized.     

Comment on the article on ‘Within‐field wheat yield prediction from IKONOS data,a new matrix approach’ by E. A. Enclona,P. S. Thenkabail,D. Celis and J. Diekmann,International Journal of Remote Sensing, 25, 377–388 (2004)

This article comments on a method of within‐field yield prediction from IKONOS data published in this Journal (International Journal of Remote Sensing, 25, 377–388). The authors propose what they call a new matrix approach, which solves a system of linear equations relating actual yield to expected yield in four classes defined based on spectral characteristics and the relative areas for these classes as measured by remote sensing. The authors use five observations to estimate four unknown parameters, and they propose that generally p+1 observations could be used to estimate p parameters. This article shows that this approach will lead to very unreliable predictions of class yield. The problem is shown to be a standard multiple regression problem. It is argued that sample size must be considerably larger than the number of classes in order to obtain reliable yield estimates for the classes. Due to the very nature of the regressor variables, the relative areas of the classes, multicollinearity is expected to be a severe problem. Therefore, it may be useful to reduce the number of regressor variables using standard model selection procedures.