The 环保创新中心 (CIC) was established in 2013 to use cutting-edge technology to empower data-driven conservation and restoration. Just as the use of technology changed the corporate world and made it more efficient, technology can do the same for the conservation movement. Through national and international partnerships, the CIC makes this data accessible for restoration professionals to practice precision conservation, yielding greater impact with fewer resources. 读到 新闻报道 CIC的成员.

Precision conservation—projects at the right place, 正确的比例, 尺寸合适, 合适的时机, and making sure they are working—is going to redefine how landscape conservation is approached. Instead of sweeping acquisitions or all-encompassing legislation, we can use the latest high-resolution datasets to conduct advanced geospatial analysis that allows us to better target and implement on-the-ground a...

The 环保创新中心 is the technology shop of the 十大赌博正规老平台 that offers assistance related to geographic in格式ion systems (GIS) and similar mapping technologies. CIC staff are trained and qualified to produce high-resolution datasets, 进行复杂的地理空间分析, and develop user-friendly desktop and web applications. These products and services act as decision-support tools that increase an organization’s capacity to plan, 优先考虑, and implement conservation and restoration work.

The CIC works with organizations of all types and sizes, 包括土地信托, 社区流域组织, 水土保持区, 私营环保公司, 和地方, 状态, 以及联邦机构. 不管我们和谁合作, we emphasize the importance of understanding each organization’s unique challenges and designing customized solutions. We strive to help our partners integrate geospatial data and analyses into their work to effectively overcome these challenges. 除了开发新产品, 我们也进行实践培训, 举办研讨会, 并创建定制的用户指南.

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The CIC provides a variety of data generation services to help your organization generate, 收集, or transform the in格式ion you need to be successful.
Imagery 分析 translates raw aerial and satellite imagery into usable data that can categorize the landscape and help make decisions about where and how to work.

The 环保创新中心 helps partners understand what imagery is available, determine what is best suited for your project, and can provide a full suite of image analysis services that help you get the in格式ion you need to make informed management decisions at all scales, from watershed wide to project specific, 包括:

•   Supervised, Unsupervised, and Object Based Land Cover Classifications

•   High Resolution Land Cover Generation

•   Satellite and aerial image interpretation and feature extraction

•   Change Detection and time-series analysis


•   Data Acquisition, cleaning, and mosaicing

•   Geo-referencing and correcting scanned images and maps

Lidar elevation data can be leveraged to understand the landscape in three dimensions, leading to a greater understanding of how a system works

Managers need to be able to model the landscape with a high degree of precision to understand how small changes in project design or location can create dramatic differences in a project’s effectiveness. Starting with hundreds of millions of points, LIDAR can be processed into a variety of high-resolution products that provide this detail including :

•   “Bare Earth” Digital Elevation Models (DEMs) and “First Return” Digital Surface Models (DSMs)

•   Normalized Digital Surface Models (nDSMs) depicting canopy and building heights

•   Hydrologically conditioned DEMs for hydrologic modeling

•   Flow Direction, Flow Accumulation, and Concentrated Flow Path Mapping

•   Slope, aspect, and other surface modeling

Data cleaning and processing can enable an organization to base decisions about future projects on the most current in格式ion.

遗留数据, 收集在电子表格中, 访问数据库, 甚至在纸上, can hold critical insights for project planning and design, but only if they can be integrated into today’s systems. 中投公司已经开发出了清洁方法, 格式, and integrate a variety of data sources so that it works for your process 包括:

•   Formatting, standardizing, and remapping fields between datasets

•   QA/QC’ing data to ensure consistency, quality, and accuracy of datasets

•   Mapping spatial data that currently exists only in tabular form