Currently available image fusion techniques applied to the merging of fine resolution panchromatic and multispectral images are still not able to minimize colour distortion and maximize spatial detail. In this study, a new fusion method, based on Bidimensional Empirical Mode Decomposition (BEMD), is proposed. Unlike other multiresolution analysis tools, such as the discrete wavelet transform (DWT), which normally examines only horizontal, vertical and diagonal orthonormal details at each decomposed scale, the BEMD produces a fully two-dimensional decomposition of the panchromatic and multispectral images, based purely on spatial relationships between the extrema of the image. These are decomposed into a certain level of Intrinsic Mode Functions (IMFs) and residual images with the same number of columns and rows as the original image. In consequence, by injecting all the IMF images from the panchromatic image into the residue of the corresponding multispectral image, the fusion image may be reconstructed. The fusion results are evaluated and compared with other popular methods in terms of both the visual examination and the quantitative assessment of the merged images. Preliminary results show that BEMD is optimal and provides a delicate balance between spectral information preservation and enhancement of spatial detail.

No abstract

Abstract. Qinghai Province is one of the important provinces on the Qinghai-Tibet Plateau in China. Its unique alpine meadow ecosystem makes it become the most concentrated areas of biodiversity in high altitudes in the world. Researching the vegetation coverage and changes of Qinghai province can reflect effectively and timely processing of changes and problems of ecological quality in the region. This research will give a long time series monitoring of the vegetation coverage of Qinghai province based on maximum value composite (MVC) and S-G filtering algorithm using MODIS data of the year of 2000–2012, then analyze the change using coefficient of variability(CV) and trend line analysis. According to research, during the past 13 years, more than half of Qinghai Province’s vegetation coverage is well, both the east and south have a high coverage, while the northwest is lower. The changing of vegetation coverage also has showed a steady and improving trend in 13 years. The largest area is slight improved area is about 29.08% of the total area, and the second largest area is significant improved area is about 21.09% of the total area. In this research can learn directly the vegetation coverage and changes of Qinghai province and provide reference and scientific basis for the protection and governance of ecological environment.

<p><strong>Abstract.</strong> At present, most of the researches on geometric change detection of vector data, they store the change detection results in the database, so they pay more attention to the accuracy of results, but not to the speed of processing. Nowadays, many applications require real-time change detection on vector data and rapid presentation of the result. Although the existing algorithms use spatial index technology to improve the processing speed, the processing time is still beyond the range that people can bear. In order to reduce processing time, this paper takes the vector surface feature set as the research object, trying to reduce the redundancy of the candidate set that seriously affects the efficiency of change detection. Based on the regular use of spatial index created with geometric Minimum Bounding Rectangle, this paper uses geometric shrinkage technique and precise query technique to reduce the size of the candidate set for detection, so as to achieve the goal of speeding up. Finally, using five years of farmland data and resident data from Ezhou City, Hubei Province, China, a change detection experiment was conducted. The experiment proved that the geometric shrinkage and precise query techniques can effectively improve the processing speed.</p>

The angular distribution of leaves is a key vegetation structural parameter for evaluating the reflection and transmission of solar radiation through vegetation canopies. Accurate extraction of Leaf Angle Distribution (LAD) is of great importance in estimating other vegetation structural parameters such as the canopy clumping and leaf area index. However, field measurement of LAD is timeconsuming, labour-intensive and subjective. In most studies, LAD is assumed to follow the spherical distribution assumption within canopy which may lead to considerable errors. To address this issue, we proposed a new approach for leaf segmentation and LAD measurement of individual broadleaf tree based on the TLS point cloud data. Based on the point density, point continuity and the distribution of intensity in the point cloud, this approach provides a fast and accurate leaf segmentation and LAD extraction strategy. Results of this TLS-based LAD method compared well with that extracted by the field measurement and the MDI-based method. This strategy shows its potential and applicability in accurate LAD measurement and LAI estimation.This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W13-957-2019 | © Authors 2019. CC BY 4.0 License.