Justin Morgenroth

Tree Species Suitability to Bioswales and Impact on the Urban Water Budget.

Water movement between soil and the atmosphere is restricted by hardscapes in the urban environment. Some green infrastructure is intended to increase infiltration and storage of water, thus decreasing runoff and discharge of urban stormwater. Bioswales are a critical component of a water-sensitive urban design (or a low-impact urban design), and incorporation of trees into these green infrastructural components is believed to be a novel way to return stored water to the atmosphere via transpiration. This research was conducted in The Morton Arboretums main parking lot, which is one of the first and largest green infrastructure installations in the midwestern United States. The parking lot is constructed of permeable pavers and tree bioswales. Trees in bioswales were evaluated for growth and condition and for their effects on water cycling via transpiration. Our data indicate that trees in bioswales accounted for 46 to 72% of total water outputs via transpiration, thereby reducing runoff and discharge from the parking lot. By evaluating the stomatal conductance, diameter growth, and condition of a variety of tree species in these bioswales, we found that not all species are equally suited for bioswales and that not all are equivalent in their transpiration and growth rates, thereby contributing differentially to the functional capacity of bioswales. We conclude that species with high stomatal conductance and large mature form are likely to contribute best to bioswale function.

Developments in Landsat Land Cover Classification Methods: A Review

Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

Uptake and barriers to the use of geospatial technologies in forest management

BackgroundA survey was conducted to assess the uptake, and barriers to use, of geospatial tools and technologies amongst New Zealand’s plantation forestry sector.MethodsResponses were received from 17 companies representing 63% of New Zealand’s plantation forest by area. A wide range of company sizes were surveyed (net stocked areas ranged from 4,000 – 200,000 hectares), and 7 of the 17 have international operations.ResultsSurvey results suggest that freely available topography, climate, and soil datasets have limited utility, as forest management at the operational level requires higher resolution, remotely sensed data. The most common supplemental data are aerial photography or satellite imagery. High spatial resolution was more highly valued by respondents than spectral diversity (i.e. number of channels); only six companies regularly use imagery containing an infrared band. LiDAR data has been used regularly by only three New Zealand forestry companies, while another six have tried it, suggesting it is an emerging technology in New Zealand. The use of generic GIS software was common amongst all respondents (14 use the ESRI product ArcGIS, three use MapInfo produced by Pitney Bowes). The utility of ArcGIS, in particular, was enhanced by locally developed extensions designed to address specific operational tasks performed regularly by New Zealand’s forestry companies.ConclusionsWhile it is clear that geospatial data and tools are generally adopted by New Zealand’s forest industry, cost-related barriers prevent their widespread adoption. Interestingly, a lack of staff knowledge was also conceded an impediment to uptake, alluding to the importance of tertiary education in the geospatial sciences and continuing education for practitioners.

Automatic GNSS-enabled harvester data collection as a tool to evaluate factors affecting harvester productivity in a Eucalyptus spp. harvesting operation in Uruguay

Uruguay has adopted cut-to-length (CTL) machines in forest harvesting operations, especially in large scale, fast-growing plantations. The majority of modern CTL machines have on-board computers that capture individual tree data and can be coupled with global navigation satellite systems (GNSS). This provides the opportunity to collect data for research purposes and to improve operations. In this study, we retrieved data (StanForD stm and drf files) from a GNSS-enabled harvester working in CTL operations in Eucalyptus spp. plantations in Uruguay. With two thirds of this data we fitted a mixed effects model to evaluate harvester productivity as a function of stem diameter at breast height (DBH), species, shift (day/night), slope, and operator. A slope surface derived from a digital terrain model was overlaid with GNSS stem records. Slope values were assigned to each stem using the Spatial Analyst toolbox in ArcGIS. The reserved third of the data were used to validate the model. DBH was the most influential variable in harvester productivity, showing a positive correlation and a R2 value of 0.73 in the validation model. Operator and species also had significant effects. There was no significant slope effect, whereby the study area only had flat and mildly sloping terrain. Shift did not have a significant effect, indicating there was no drop in night shift productivity. The model developed constitutes the first published harvester productivity model in South America based on data automatically collected by harvesters. In addition, the forestry company may benefit from using the model for operator management.

Ground truth measurement of trees using terrestrial laser for satellite remote sensing

Forest monitoring for environmental policy requires accurate and efficient ground-truthing techniques in the field. In this paper, a portable terrestrial laser scanner (TLS) is utilized to estimate leaf area index (LAI) of mixed forest stands in Christchurch, New Zealand. Our method converted laser XYZ coordinates to orthographic coordinates to create fish-eye images, from which LAI was estimated. The results were highly correlated with LAI estimates from three traditional techniques: radiation obtained by AccuPAR (R2 = 0.81), Landsat TM (R2 = 0.79), and fish-eye lens photography (R2 = 0.91). This novel technique is a simple and efficient way to collect and analyze LAI and provides good ground truth data for satellite remote sensing.

Estimating conductive sapwood area in diffuse and ring porous trees with electronic resistance tomography

Accurately estimating sapwood area is essential for modelling whole-tree or stand-scale transpiration from point-flow sap-flux observations. In this study, we tested the validity of electrical resistance tomography (ERT) to locate the sapwood-heartwood (SW/HW) interface for two ring porous (Quercus nigra L. and Quercus virginiana Mill.) and one diffuse porous (Acer rubrum L.) species. Estimates derived from the ERT analyses were compared with the SW/HW interface measured following dye perfusion testing. The ERT results revealed spatial variation in electrical resistance, with higher resistivity in the inner part of the cross sections. Regression analyses showed that ERT was able to accurately account for 97% and 80% of the variation in sapwood area (calculated as R2) for Q. virginiana (n = 19) and Q. nigra (n = 7), respectively, and 56% of the variation in the diffuse porous species (n = 8). Root mean square error (RMSE) values for sapwood areas of the ring porous species were 11.12 cm2 (19%) and 25.98 cm2 (33%) for Q. virginiana and Q. nigra, respectively. Sapwood area estimates for diffuse wood carried greater error (RMSE = 33.52 cm2 (131%)). Model bias for all sapwood area estimates was negative, suggesting that ERT had a tendency to overestimate sapwood areas. Electrical resistance tomography proved to be a significant predictor of sapwood area in the three investigated species, although it was more reliable for ring porous wood. In addition to the results, a comprehensive code sequence for use with R statistical software is provided, so that other investigators may follow the same method.

Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier

Abstract The application of Landsat satellite imagery in land cover classification is affected by atmospheric and topographic errors, which have led to the development of different correction methods. In this study, moderate resolution atmospheric transmission (MODTRAN) and dark object subtraction (DOS) atmospheric corrections, and cosine topographic correction were evaluated individually and combined in a heterogeneous landscape in Zambia. These pre-processing methods were tested using a combination of object-based image analysis (OBIA) and Random Forests (RF) non-parametric classifier (hereafter referred to as OBIA-RF). This assessment aimed at understanding the combined effects of different pre-processing methods and the OBIA-RF classification method on the accuracy of Landsat operational land (OLI-8) imagery with different spatial resolutions. Here, we used pansharpened and standard Landsat OLI-8 images with 15 and 30 m spatial resolutions, respectively. The results showed that non pre-processed images reached a classification accuracy of 68% for pansharpened and 66% for standard Landsat OLI-8. Classification accuracy improved to 93% (pansharpened) and 86% (standard) when combined MODTRAN and cosine topographic correction pre-processing were applied. The results highlight the importance of pansharpening, as well as atmospheric and topographic corrections for Landsat OLI-8 imagery, when used as input in OBIA classification with the RF classifier.

Evaluation of modelling approaches in predicting forest volume and stand age for small-scale plantation forests in New Zealand with RapidEye and LiDAR

Abstract In New Zealand, 30% of plantation forests are small-scale ( 2  ha −1 , stand volume with an RMSE of 94.93 m 3  ha −1 and age with an RMSE of 2.17 years.

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m−2), 50% (0.259 points m−2), and 75% (0.129 points m−2) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m−2). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m−2). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.