A Framework for integrating Lidar, Satellite and Weather Observations to Support Improvements in Residential irrigation

Abstract:

Balancing residential irrigation and energy needs with the dynamic energy markets is becoming increasingly important to energy and water users, producers and the energy and water infrastructure. This called for the development of a decision support system for irrigation management to facilitate customer participation in water and energy demand management incentive programs. This work presents a framework that allows to estimate accurate information on lawns’ actual water needs. The methodology provides the computing and data processing systems required to support automated, near real-time integration of observations from satellite and surface sensor networks, and generates data and information in formats that are convenient for residential customers, water managers, and other end users.

The framework starts from an automated method based on 1m Lidar data, and 0.5m high-resolution imagery to automatically map urban lawn cover. The derived lawns in local areas are then grouped based on similarities in soil properties, topography, and other relevant site factors to account for variables that could affect turf water needs. Irrigation needs are derived using appropriate values of turf parameters derived by automatic processing cloud-free Sentinel-2A data in combination with daily weather information and NOAA weather forecast.

Using past water consumption data for six cities in CT (USA), target properties are identified by discretionary water use [difference between water use during irrigation season and non-irrigation season] and peaking factor [ratio between the same variables]. Based on this assessment, several properties are then processed as potential pilot locations, and are processed to extrapolate the potential peak reduction that could be seen if this method had been implemented during summer 2019.