Michael G. Andreu

A community-based urban forest inventory using online mapping services and consumer-grade digital images

Abstract Community involvement in gathering and submitting spatially referenced data via web mapping applications has recently been gaining momentum. Urban forest inventory data analyzed by programs such as the i-Tree ECO inventory method is a good candidate for such an approach. In this research, we tested the feasibility of using spatially referenced data gathered and submitted by non-professional individuals through a web application to augment urban forest inventory data. We examined the use of close range photogrammetry solutions of images taken by consumer-grade cameras to extract quantitative metric information such as crown diameter, tree heights and trunk diameters. Several tests were performed to evaluate the accuracy of the photogrammetric solutions and to examine their use in addition to existing aerial image data to supplement or partially substitute for standard i-Tree ECO field measurements. Digital images of three sample sites were acquired using different consumer-grade cameras. Several photogrammetric solutions were performed using the acquired image sets. Each model was carried out using a relative orientation process followed by baseline model scaling. Several distances obtained through this solution were compared to the corresponding distances obtained through direct measurements in order to assess the quality of the model scaling approach. Measured i-Tree ECO field plot inventory data, online aerial image measurements and photogrammetric observations were compared. The results demonstrate the potential for using aerial image digitizing in addition to ground images to assist in participatory urban forest inventory efforts.

The Socioeconomic Context of Carbon Sequestration in Agroforestry: A Case Study from Homegardens of Kerala, India

While the biological and ecological role of agroforestry (AF) on climate change mitigation has received considerable research attention lately, the role of socio-psychological factors in this context has been left largely unexplored. Socio-psychological variables such as culture, demography, economy, and social values play important roles in farmers’ decision making with the land management, which in turn influence the ability of AF systems to sequester carbon (C). This chapter presents a case study from Thrissur, Kerala, India, which examined how different socio-psychological factors influence soil C sequestration through land management decisions in tropical homegardens (HGs), a popular agroforestry system in the tropics. This study used the Theory of Planned Behavior (TPB) as the theoretical framework to understand homegarden owners’ perceptions on the adoption of five land management practices (i.e., tillage, tree planting, plant residue incorporation, manure usage, and fertilizer applications), which are known to impact C sequestration. Data collected using focus group and household interviews were analyzed by regression statistics. Results indicated that farmers’ decision making processes were most influenced by factors such as ancestors and education, followed by peers, financial condition, and economic importance of the AF land holding. The results of this case study will not only benefit researchers and extension practitioners, but can also contribute to the policy platform to recognize the role of socio-psychological factors in agricultural decision making.

Urban forest inventory using open access web mapping services and photogrammetric solution

Community -based data collection is a new trend that can empower and diversify our data collection methods and increase our understanding of the environment. As citizens are becoming more interested and involved in ecological matters utilizing their manpower and knowledge can be a new valuable resource for researchers and urban managers. Utilizing web mapping services to assist in collecting spatially related data can provide data that previously required dedicated personnel. National or regional programs for urban forest inventories that utilize trained crews, such as the UFORE program, were previously used exclusively. Much of the information collected by crews can be collected by interested community volunteers if combined with proper education and other resources such as high resolution aerial images and ground images. Our research tested the idea of utilizing a web application built around Google Maps ™ web-based service that allows users to actually select and input information about their backyard vegetation. We extended this idea by extracting metric information such as crown diameter, tree heights and diameters by solving close-range photogrammetric models for images captured using consumer-grade digital cameras and uploaded by participating citizens. The images were processed using one of the commercial photogrammetric software. Ground survey measurements made using survey equipment were collected and analyzed to test the accuracy of the photogrammetric model. We compared the results of the photogrammetric solutions with the higher-accuracy survey-quality measurements. The preliminarily results of this research demonstrated the potential for photogrammetry to provide valuable data for urban forest inventory programs.

Direct Application of Invasive Species Prioritization: The Spatial Invasive Infestation and Priority Analysis Model

Effective and efficient prioritization of invasive species treatments is an important aspect of land management and ecological restoration, as the number and distribution of invasive species increase and budgets decrease. Land managers have a multitude of prioritization frameworks from which to choose, but the vast majority only consider entire species across a landscape rather than individual populations. Here, we discuss the Spatial Invasive Infestation and Priority Analysis (SIIPA) model (built in ESRI ArcGIS software), a customizable tool for rapid application of a prioritization framework to known invasive populations within a preserve, management area, or region. The SIIPA model is based on the framework provided in The Nature Conservancy’s Draft Weed Management Plan and prioritizes invasive species based on four characteristics common to many other schema: 1) current extent of the species; 2) current and potential impacts of the species; 3) value of habitats the species infests; and 4) difficulty of control and establishing replacement species. We describe how the model can be customized to have different classes for each characteristic, different weights for each class, and include other characteristics. To demonstrate how the model performs at different scales and for different land management objectives, we consider the application of this model in three different case studies: the Nature Conservancy’s Disney Wilderness Preserve in Kissimmee, Florida; the U.S. Forest Service’s Apalachicola National Forest near Tallahassee, Florida; and the Heartland Cooperative Invasive Species Management Area in central Florida. The SIIPA model provides land managers with an adaptable, easy-to-utilize decision support tool for making critical prioritization choices.