Application of close-range aerial infrared thermography to detect landfill gas emissions: a case study

Monitoring waste disposal sites is important to check that the produced biogas, potentially explosive, is properly collected by the biogas extraction system of the landfill site and to evaluate the residual biogas flow escaping from upper surface of the landfill. As the biogas migrates to the surface, the soil through which it flows is expected to reach a higher temperature than the surrounding environment; thus, measuring the thermal footprint of the landfill soil surface could allow the detection of biogas leakages and spots suitable for the gas extraction. Close-range aerial infrared thermography is an innovative approach able to identify thermal anomalies with a good resolution over a large region of the landfill surface. A simple procedure to deduce the biogas flow rate emerging from the soil into the atmosphere, based on infrared thermography measurements, is presented. The approach has been applied to a case study concerning a large landfill located in Genoa (Italy). Aerial infrared photographs taken during different days and seasons showed the presence of thermal anomalies over regions along the peripheral boundary of the landfill still not interested in biogas extraction

Unmanned aerial vehicle to estimate nitrogen status of turfgrasses

Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits. The objectives of the trial were i) to compare the spectral reflectance of 3 turfgrasses acquired via UAV and by a ground-based instrument; ii) to test the sensitivity of the 2 data acquisition sources in detecting induced variation in N levels. N application gradients from 0 to 250 kg ha-1 were created on 3 different turfgrass species: Cynodon dactylon x transvaalensis (Cdxt) Patriot, Zoysia matrella (Zm) Zeon and Paspalum vaginatum (Pv) Salam. Proximity and remote-sensed reflectance measurements were acquired using a GreenSeeker handheld crop sensor and a UAV with onboard a multispectral sensor, to determine Normalized Difference Vegetation Index (NDVI). Proximity-sensed NDVI is highly correlated with data acquired from UAV with r values ranging from 0.83 (Zm) to 0.97 (Cdxt). Relating NDVI-UAV with clippings N, the highest r is for Cdxt (0.95). The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%. UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management. For relatively small green areas, a hand-held crop sensor can be a less expensive and more practical option

Early season weed mapping in rice crops using multi-spectral UAV data

In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral ortho-mosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into â weed/no weed classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an alarmist approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA= 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 x 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth

Monitoring of biogas emissions from an urban landfill by means of close-range aerial infrared thermography

Monitoring waste disposal sites is important to check that the produced biogas, potentially explosive and environmentally significant, is properly collected by the biogas extraction system of the landfill site and to evaluate the residual biogas flow escaping from the upper surface of the landfill. As the biogas migrates to the surface, the soil through which it flows is expected to reach a higher temperature than the surrounding environment; thus, measuring the thermal footprint of the landfill soil surface could allow the detection of biogas leakages and spots suitable for gas extraction. Infrared thermography is an innovative diagnostic tool able to detect thermal anomalies on the landfill surface. If the infrared camera is installed on an unmanned aerial vehicle, thermal anomalies can be identified with a good resolution over a large region. A simple procedure to deduce the biogas flow rate emerging from the soil into the atmosphere, based on infrared thermography measurements, is presented. The approach has been applied to a case study concerning a large landfill located in Genoa, northern Italy. Aerial infrared photographs taken during different days and seasons showed the presence of thermal anomalies over regions along the peripheral boundary of the landfill still not interested in biogas extraction

Relationships between the clippings nitrogen content of <i>Cynodon dactylon x transvaalensis</i> and NDVI data obtained with a ground-based spectral reflectance instrument (Trimble’s GreenSeeker sensor) and an unmanned aerial vehicle (UAV).

<p>Values represented the 4 replications.</p

Relationships between the clippings nitrogen content of <i>Paspalum vaginatum</i> and NDVI data obtained with a ground-based spectral reflectance instrument (Trimble’s GreenSeeker sensor) and an unmanned aerial vehicle (UAV).

<p>Values represented the 4 replications.</p

The RGB image of the turfgrass fields acquired by Tetracam ADCMicro mounted on the unmanned aerial vehicle.

<p>The RGB image of the turfgrass fields acquired by Tetracam ADCMicro mounted on the unmanned aerial vehicle.</p

Relationships between the clippings nitrogen content and NDVI data measured with the UAV on <i>Cynodon dactylon x transvaalensis</i>, <i>Paspalum vaginatum</i> and <i>Zoysia matrella</i>.

<p>In each species values are means of 4 replications.</p

The UAV during flight operations (July 6, 2015; Pisa, Italy; 43°40’N, 10° 19’E, 6 m. a.s.l.) and a detail of the vehicle DJI S1000 Octocopter.

<p>The UAV during flight operations (July 6, 2015; Pisa, Italy; 43°40’N, 10° 19’E, 6 m. a.s.l.) and a detail of the vehicle DJI S1000 Octocopter.</p

Pearson product-moment correlation coefficients (r) among the nitrogen applied, clippings nitrogen content, color intensity, temperature, NDVI measured with a ground-based instrument (Trimble’s GreenSeeker sensor) and NDVI measured with an unmanned aerial vehicle (UAV) on a) <i>Cynodon dactylon x transvaalensis</i>; b) <i>Paspalum vaginatum</i>; c) <i>Zoysia matrella</i>.

<p>In each species correlation coefficients are calculated across all entries.</p