CIFOR-ICRAF berfokus pada tantangan-tantangan dan peluang lokal dalam memberikan solusi global untuk hutan, bentang alam, masyarakat, dan Bumi kita

Kami menyediakan bukti-bukti serta solusi untuk mentransformasikan bagaimana lahan dimanfaatkan dan makanan diproduksi: melindungi dan memperbaiki ekosistem, merespons iklim global, malnutrisi, keanekaragaman hayati dan krisis disertifikasi. Ringkasnya, kami berupaya untuk mendukung kehidupan yang lebih baik.

CIFOR-ICRAF menerbitkan lebih dari 750 publikasi setiap tahunnya mengenai agroforestri, hutan dan perubahan iklim, restorasi bentang alam, pemenuhan hak-hak, kebijakan hutan dan masih banyak lagi – juga tersedia dalam berbagai bahasa..

CIFOR-ICRAF berfokus pada tantangan-tantangan dan peluang lokal dalam memberikan solusi global untuk hutan, bentang alam, masyarakat, dan Bumi kita

Kami menyediakan bukti-bukti serta solusi untuk mentransformasikan bagaimana lahan dimanfaatkan dan makanan diproduksi: melindungi dan memperbaiki ekosistem, merespons iklim global, malnutrisi, keanekaragaman hayati dan krisis disertifikasi. Ringkasnya, kami berupaya untuk mendukung kehidupan yang lebih baik.

CIFOR–ICRAF publishes over 750 publications every year on agroforestry, forests and climate change, landscape restoration, rights, forest policy and much more – in multiple languages.

CIFOR–ICRAF addresses local challenges and opportunities while providing solutions to global problems for forests, landscapes, people and the planet.

We deliver actionable evidence and solutions to transform how land is used and how food is produced: conserving and restoring ecosystems, responding to the global climate, malnutrition, biodiversity and desertification crises. In short, improving people’s lives.

Parallel Generation of Very High Resolution Digital Elevation Models: High-Performance Computing for Big Spatial Data Analysis

Ekspor kutipan

Very high resolution digital elevation models (DEM) provide the opportunity to represent the micro-level detail of topographic surfaces, thus increasing the accuracy of the applications that are depending on the topographic data. The analyses of micro-level topographic surfaces are particularly important for a series of geospatially related engineering applications. However, the generation of very high resolution DEM using, for example, LiDAR data is often extremely computationally demanding because of the large volume of data involved. Thus, we use a high-performance and parallel computing approach to resolve this big data-related computational challenge facing the generation of very high resolution DEMs from LiDAR data. This parallel computing approach allows us to generate a fine-resolution DEM from LiDAR data efficiently. We applied this parallel computing approach to derive the DEM in our study area, a bottomland hardwood wetland located in the USDA Forest Service Santee Experimental Forest. Our study demonstrated the feasibility and acceleration performance of the parallel interpolation approach for tackling the big data challenge associated with the generation of very high resolution DEM.
Download:

DOI:
https://doi.org/10.1007/978-981-10-8476-8_2
Skor altmetrik:
Jumlah Kutipan Dimensi:

Publikasi terkait