Mapping beech, fagus sylvatica L., forest structure with airborne hyperspectral imagery

Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI(i,j) = (Ri - Rj)/(Ri + Rj), where Ri and Rj = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756-820 nm) and the water absorption feature centred at 1200 nm (1172-1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factor

Estimating fresh grass/herb biomass from HYMAP data using the rededge position

Remote sensing of grass/herb quantity is essential for rangeland management of livestock and wildlife. Spectral indices such as NDVI, determined from red and near infrared bands are affected by variable soil and atmospheric conditions and saturate in dense vegetation. Alternatively, the wavelength of maximum slope in the red-NIR transition, termed the red edge position (REP) has potential to mitigate these effects. But the utility of the REP using air-and spaceborne imagery is determined by the availability of narrow bands in the region of the red edge and the simplicity of the extraction method. Very recently, we proposed a simple technique for extracting the REP called the linear extrapolation method [Cho and Skidmore, Remote Sens. Environ., 101(2006)118.]. The purpose of this study was to evaluate the potential of the linear extrapolation method for estimating fresh grass/herb biomass and compare its performance with the four-point linear interpolation and three-point Lagrangian interpolation methods. The REPs were derived from atmospherically corrected HYMAP images collected over Majella National Park, Italy in July 2004. The predictive capabilities of various REP linear regression models were evaluated using leave-one-out cross validation and test set validation methods. For both validation methods, the linear extrapolation REP models produced higher correlations with grass/herb biomass and lower prediction errors compared with the linear interpolation and Lagrangian REP models. This study demonstrates the potential of REPs extracted by the linear extrapolation method using HYMAP data for estimating fresh grass/herb biomass

Effective Stress Nonlinear Model Parameters and Simulation of Stress-strain for Expansive Soil

An experimental investigation of the stress-strain and volume change behaviour of normally consolidated expansive (Barind) soil characteristics are studied using a computer controlled triaxial cell. This paper presents a procedure for estimating the effective nonlinear stress-strain parameters for Barind soils from the results of triaxial, consolidation and direct shear tests. In this study compacted soil is tested to different stress level. Since field tests are very costly, it is essential to develop suitable model parameters to predict the behaviour of Barind soils based on the triaxial, consolidation and direct shear tests. From the results of the drained triaxial compression tests, the Young’s modulus and modulus exponent, defined for their dependencies on shear stress level and strain rate were quantified. A procedure for estimating the bulk modulus number, the bulk modulus exponent and the unload-reload modulus number is also presented. Finally, a comparison is made between the predictions and experimental results using model constants and the predictions are found to be satisfactory

From tobacco to food production : consolidation, dissemination and policy advocacy (Bangladesh); final technical report, April 2009 – March 2011

Many farmers cultivate tobacco instead of food crops, lured by tobacco companies with false promises of higher price for tobacco leaves. Yet in spite of huge production, farmers do not get anticipated returns as market price is determined solely by the tobacco company. As part of the field experiment UBINIG (Policy Research for Development Alternative) which supported many farmers in three research areas, and while others took initiative on their own and in Chakaria, the government helped farmers to grow food rather than tobacco. The report covers in detail the complexity of switching from cash crop approach to food security

Estimation of green grass - herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration (n = 30) and test (n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m¿2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m¿2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m¿2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m¿2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m¿2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park