Technical note: Use of digital surface model for hardwood rangeland monitoring

We built digital surface models (DSM) that contain 3D surface morphological information of the entire landscape using digital photogrammetry and aerial photographs. Changes in landscape components such as crown closure and tree height in hardwood rangeland were estimated using DSM. In comparison with manual interpretation results, errors of crown closure and tree hieght estimation using DSM were less than 0.7% and 1.5 m, respectively. This technique can be used for rangeland management, monitoring and ecological studies.The Journal of Range Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202

Simple Calibration of AVIRIS Data and LAI Mapping of Forest Plantation in Southern Argentina

During the 2001 EO-1 campaign in Argentina, two high spectral resolution image scenes of AVIRIS were acquired at two study sites in the Patagonia region of southern Argentina on 15 February 2001. A total of 70 LAI measurements were taken from different forest types in the same areas one month later, and some spectroradiometric measurements were also collected from the nearby highway and different forest stands in the areas. In this study, we compared the effectiveness of the three types of AVIRIS data used for estimating and mapping LAI. The three types of data correspond to AVIRIS original radiance (OR), corrected radiance (CR) and retrieved surface reflectance (SR). We first simulated the total at-sensor radiances using MODTRAN4, then used ground spectroradiometric measurements taken from different targets to improve the reflectances for each pixel on the image. The CR images were obtained by subtracting path radiance from the OR images. A 10-term LAI prediction model for each type of data was constructed to predict pixel-based LAI values. Finally, the pixel-based LAI value was sliced and mapped for all the three types of images. The results of mapping LAI using the three types of AVIRIS data (OR, CR and SR) indicate that mapping LAI by SR is the most realistic, followed by CR. The poorest result occurs when mapping LAI with OR data due to atmospheric effect. The SR data can lead to higher correlation with LAI in some bands and produce higher accuracy indices for the 10-term predictive model, although some indices from the test set for SR data have a somewhat lower correlation with LAI than those produced with OR data. Therefore, in general, it can be concluded that the retrieved surface reflectance data is more effective for mapping forest LAI compared to the other two types of data

Model-based conifer-crown surface reconstruction from highresolution aerial images. Photogrammetric Engineering and Remote Sensing 67

Abstract Tree crown parameters such as height, shape and crown closure are desirable in forest and ecological studies, but difficult to measure on the ground. The stereoscopic capability of high-resolution aerial images provides a way to crown surface reconstruction. However, existing digital photogrammetry packages designed to map terrain surfaces cannot accurately extract tree crown surfaces, particularly for conifer crowns with steep profiles in the vertical direction. In this paper, we integrate crown features derived from images to stereo matching, and develop a model-based approach to reconstruct crown surfaces for conifers. The idea is based on the fact that most conifer crowns are in a form of solid geometry. We model a conifer crown as a generalized ellipsoid; establish the optimal tree model using a geometric equation; and then apply the optimal tree model to guide a conventional pyramid image matching in crown surface reconstruction. The effectiveness of the proposed- approach is illustrated using an example of a redwood tree on 1:2,400 aerial photographs. 1

A comparison of photointerpretation and ground measurements of forest structure.

Traditional forest inventory methods are compared with photointerpreted results. The accuracy of photointerpretation for forest-type classification is assessed in test locations in northern California. If the accuracy of photointerpretation is not sufficiently high, then the traditional practice of comparing satellite classification to photointerpretation is not justified. If this hypothesis is true, it is speculated that spectral analysis of advanced digital satellite data (SPOT and TM) can be used in conjunction with ancillary ground data to produce forest classifications of the same or better accuracy than by traditional photointerpretation techniques. Results of the accuracy assessment of three levels of classification - species, size class, and density - are presented in tables

Vineyard identification in an oak woodland landscape with airborne digital camera imagery

Abstract. Using airborne multispectral digital camera imagery, we compared a number of feature combination techniques in image classification to distinguish vineyard from non-vineyard land-cover types in northern California. Image processing techniques were applied to raw images to generate feature images including grey level co-occurrence based texture measures, low pass and Laplacian filtering results, Gram-Schmidt orthogonalization, principal components, and normalized difference vegetation index (NDVI). We used the maximum likelihood classifier for image classification. Accuracy assessment is performed using digitized boundaries of the vineyard blocks. The most successful classification as determined by t-tests of the Kappa coefficients was achieved based on the use of a texture image of homogeneity obtained from the near infrared image band, NDVI and brightness generated through orthogonalization analysis. This method averaged an overall accuracy of 81 per cent for six frames of images tested. With post-classification morphological processing (clumping and sieving) the overall accuracy was significantly increased to 87 per cent (with a confidence level of 0.99). 1

EO‐1 Hyperion, ALI and Landsat 7 ETM+ Data Comparison for Estimating Forest Crown Closure and Leaf Area Index

In this study, mixed coniferous forest crown closure (CC) and leaf area index (LAI) were measured at the Blodgett Forest Research Station, University of California at Berkeley, USA. Data from EO‐1 Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) acquired on 9 October 2001, and from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) on 25 October 2001 were used for estimation of CC and LAI. A total of 38 forest CC and LAI measurements were used in this correlation analysis. The analysis procedure consists of (1) atmospheric correction to retrieve surface reflectance from Hyperion, ALI and ETM+ data, (2) a total of 38 patches, corresponding to ground CC and LAI measurement plots, extracted from data from the three sensors, and (3) calculating univariate/multivariate correlation coefficient (R 2) and root mean square error (RMSE) using CC and LAI measurements and retrieved surface reflectance data of the three sensors. The experimental results indicate: (1) higher individual band correlations with CC and LAI appear in visible and short wave infrared (SWIR) regions due to spectral absorption features (pigments in visible and water and other biochemicals in SWIR); (2) based on ALI individual band wavelengths, the R 2/RMSE produced with Hyperion bands are all better than those with ALI, except ALI band 1, due to atmospheric scattering of Hyperion bands in the visible region; (3) based on ETM+ individual band wavelengths, Hyperion is better than ALI, which is better than ETM+, especially for the NIR band group of Hyperion; (4) based on spectral region, Hyperion, again, is better than ALI which is better than ETM+, and optimal results appear in the visible region for ALI and in SWIR for Hyperion; and (5) if considering just six bands or six features (six principal components) in estimating CC and LAI, optimal results are obtained with six bands selected from the 167 Hyperion bands. In general, for estimation of forest CC and LAI in this study, the Hyperion sensor has outperformed the ALI and ETM+ sensors, whereas ALI is better than ETM+. The best spectral region for Hyperion is SWIR, but for ALI and ETM+, the visible region should be considered instead