Question Although digital photography is an efficient and objective means of extracting green fractional vegetation cover (FVC), it lacks automation and classification accuracy. How can green FVC be extracted from digital images in an accurate and automated method? Methods Several colour spaces were compared on the basis of a separability index, and CIE L*a*b* was shown to be optimal for the tested colour spaces. Thus, all image processing was performed in CIE L*a*b* colour space. Gaussian models were used to fit the green vegetation and background distributions of the a* component. Three strategies (T0, T1 and T2 thresholding method) were tested to select the optimal thresholds for segmenting the image into green vegetation and non‐green vegetation. The a* components of the images were then segmented and the green FVC extracted. Results The FVC extracted using T0, T1, and T2 thresholding methods were evaluated with simulated images, and cross‐validated with FVC extracted with supervised classification methods. The results show that FVC extracted with T0, T1 and T2 thresholding methods are similar to those estimated with supervised classification methods. The mean errors associated with the FVC values provided in our approach and supervised classification are less than 0.035. In a test with simulated data, our method performed better than the supervised classification method. Conclusions Methods presented in this paper were demonstrated to be feasible and applicable for automatically and accurately extracting FVC of several green vegetation types with varying background and shadow conditions. However, our algorithm design assumes a Gaussian distribution for both vegetated and non‐vegetated portions of a digital image; moreover, the impact of view angle on the FVC extraction from digital images must also be considered.

Ground-based leaf area and leaf direction measurements are crucial for remote sensing validation of leaf area index (LAI) and leaf angle distribution (LAD) products. The existing methods, such as use of the expensive instruments and/or time-consuming and labour-intensive field operations, have led to scientists investigating advanced methods. For this reason an image-based method for measuring the leaf characteristics of corn is presented to avoid the limitations mentioned above. With application of photogrammetric techniques, a three-dimensional corn model is reconstructed from captured images, and accurate leaf areas and leaf directions are measured on the basis of the 3D model. The accuracy of the proposed method is assessed by comparing the results from the 3D model with that measured by tape in the field. The experimental results show that the proposed method is feasible and effective. The proposed method can save time and labour.

Reputation is a socially mediated form of knowledge. In social psychology it has been studied with reference to different social actors (individuals, brands, cities, etc). However, the social-psychological conceptualization of city reputation lacks a consensual definition. This research aims to operationally define city reputation, via the construction and validation of the City Reputation Indicators (CRIs). The first and preliminary study (N = 62) defines the underlying dimensions via six focus groups held in two Italian cities that differ in terms of their political/administrative and sociodemographic features. The second study (N = 362) tests the properties of CRIs via a survey administered in Rome. Results show a first validation of the 12 CRIs' scales, which represent a basic tool for the assessment of people's perception of a city. Theoretical and applied implications are discussed, as well as development perspectives for improvements.

No abstract