Remote Sensing + Analysis

Finding new solutions from technology, modeling, and algorithms.

To support ecological planning, land use, and land management decisions, AES Geospatial has developed a specific expertise in remote sensing for vegetation analysis and other natural resource issues.

AES interprets remotely sensed data, often from multiple sensors and platforms, for applications in ecological restoration, forestry, agriculture, and industrial sectors of electricity, oil, and gas.

Geospatial specialists are experts in the collection and processing of high resolution images from airborne sensors and satellite data sources. With aerial imagery as a main source of datasets, AES frequently incorporates products from LiDAR sensors, NAIP images, Landsat images, and other datasets.

AES applies multiple digital image processing methods, algorithms, statistical modeling, and other data-mining tools to develop analytical products including detailed land cover types as well as an assortment of associated biophysical characteristics.

AES is also capable of integrating products from remote sensing technology to simulate environmental impacts, such as from climate change or different land management scenarios.  Our process-based ecosystem models provide simulation of ecosystem function and processes at both site and landscape scales.

By integrating a variety of strategies and technologies, in flexible combination, AES is able to produce deliverables to meet the diverse needs of a broad range of clients.

  • Regional + Watershed Scale Analysis
  • Remote Sensing Applications
  • Ecological Modeling
  • Data Collection, Management + Analysis
  • Education + Outreach

Approach

With the in-house capabilities of AES Flight Services, LLC, we are able to collect imagery at the right time, seasonally, for the best vegetation identification and analysis. The near-infrared band of the multi-spectral sensor is highly sensitive to chlorophyll and very useful in identifying vegetation, even to the species level.

Our digital images are processed to identify and delineate “objects” (such as ash trees or wild rice) using an object-oriented methodology. Digital objects are then classified and quantified by a series of rules and models that consider spectral characteristics of the objects and other factors such as texture and relationships with neighboring objects.

Applications are numerous, of course. As are combinations of methods and models to accomplish project goals. AES remote sensing scientists frequently collaborate with academic, institutional, non-profit and private industry partners to advance the leading edge of remote sensing in the context of natural resource conservation.


Remote Sensing + Analysis Portfolio

BLM Wildfire Study

BLM Wildfire Study

KC NRI II

KC NRI II

appliedecoRemote Sensing + Analysis