International Journal of Digital Earth 

About: International Journal of Digital Earth is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Computer science & Digital Earth. It has an ISSN identifier of 1753-8947. Over the lifetime, 1059 publications have been published receiving 23656 citations.

Crowdsourcing geographic information for disaster response: a research frontier

Abstract: Geographic data and tools are essential in all aspects of emergency management: preparedness, response, recovery, and mitigation. Geographic information created by amateur citizens, often known as volunteered geographic information, has recently provided an interesting alternative to traditional authoritative information from mapping agencies and corporations, and several recent papers have provided the beginnings of a literature on the more fundamental issues raised by this new source. Data quality is a major concern, since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data. During emergencies time is the essence, and the risks associated with volunteered information are often outweighed by the benefits of its use. An example is discussed using the four wildfires that impacted the Santa Barbara area in 2007–2009, and lessons are drawn.

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Abstract: We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs (RMSE =86% in 2000 and 119% in 2005), but showed improved accuracy in agricultural areas and increased discrimination of small forest patches Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE=168% for MODIS-based vs 174% for Landsat-based estimates), but RMSE of Landsat estimates was 33 percentage points lower than that of the MODIS data in an agricultural region The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal

Big Data and cloud computing: innovation opportunities and challenges

Abstract: Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Abstract: Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This...

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Abstract: Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and pho...