土地覆盖
地理
土地信息系统
土地利用
空间数据基础设施
数据集成
服务(商务)
环境资源管理
地图学
计算机科学
数据科学
空间分析
遥感
数据库
土地管理
工程类
环境科学
土木工程
业务
营销
作者
Fabiana Zioti,Karine Reis Ferreira,Gilberto Ribeiro de Queiroz,Alana K. Neves,Felipe Carlos,Felipe Souza,Lorena Santos,Rolf Simões
出处
期刊:International journal of applied earth observation and geoinformation
日期:2021-12-18
卷期号:106: 102655-102655
被引量:12
标识
DOI:10.1016/j.jag.2021.102655
摘要
Information on land use and land cover (LULC) is essential to support governments in making decisions about the impact of human activities on the environment, planning the use of natural resources, conserving biodiversity, and monitoring climate change. Nowadays, different initiatives systematically produce information on LULC dynamics, on global, national, and regional scales. Examples of open and global LULC data products are Global Land-cover Classification with a Fine Classification System, Copernicus Global Land Service, and Global Land Cover by European Space Agency (ESA). At the national and regional level in Brazil, we can cite the data sets produced by PRODES, TerraClass, MapBiomas, and IBGE. Although these initiatives provide rich collections of open LULC maps, there is still a gap in tools that facilitate the integration of these data sets. The integrated analysis of these collections requires considerable effort by researchers who have to download, organize and harmonize them in their local computers, facing with different spatiotemporal resolutions and classification systems containing distinct class numbers, names and meanings. Besides that, these collections are distributed in different data formats through files or web services. To minimize these efforts, we propose a platform that allows users to access LULC collections from distinct sources, map their distinct classification systems, and retrieve LULC trajectories associated with spatial locations by integrating these collections. Besides the platform architecture description, this paper presents a case study that demonstrates its use in the integration and analysis.
科研通智能强力驱动
Strongly Powered by AbleSci AI