Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis—[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.     

Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon

The main objective of this study is to generate a knowledge base which is composed of user-defined variables and included raster imagery, vector coverage, spatial models, external programs, and simple scalars and to develop an expert classification using Landsat 7 (ETM+) imagery for land cover classification in a part of Trabzon city. Expert systems allow for the integration of remote-sensed data with other sources of geo-referenced information such as land use data, spatial texture, and digital elevation model to obtain greater classification accuracy. Logical decision rules are used with the various datasets to assign class values for each pixel. Expert system is very suitable for the work of image interpretation as a powerful means of information integration. Landsat ETM data acquired in the year 2000 were initially classified into seven classes for land cover using a maximum likelihood decision rule. An expert system was constructed to perform post-classification sorting of the initial land cover classification using additional spatial datasets such as land use data. The overall accuracy of expert classification was 95.80%. Individual class accuracy ranged from 75% to 100% for each class.     

Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography

We assess the feasibility of using airborne imagery for Buffel grass detection in Australian arid lands and evaluate four commonly used image classification techniques (visual estimate, manual digitisation, unsupervised classification and normalised difference vegetation index (NDVI) thresholding) for their suitability to this purpose. Colour digital aerial photography captured at approximately 5 cm of ground sample distance (GSD) and four-band (visible–near-infrared) multispectral imagery (25 cm GSD) were acquired (14 February 2012) across overlapping subsets of our study site. In the field, Buffel grass projected cover estimates were collected for quadrates (10 m diameter), which were subsequently used to evaluate the four image classification techniques. Buffel grass was found to be widespread throughout our study site; it was particularly prevalent in riparian land systems and alluvial plains. On hill slopes, Buffel grass was often present in depressions, valleys and crevices of rock outcrops, but the spread appeared to be dependent on soil type and vegetation communities. Visual cover estimates performed best (r ² 0.39), and pixel-based classifiers (unsupervised classification and NDVI thresholding) performed worst (r ² 0.21). Manual digitising consistently underrepresented Buffel grass cover compared with field- and image-based visual cover estimates; we did not find the labours of digitising rewarding. Our recommendation for regional documentation of new infestation of Buffel grass is to acquire ultra-high-resolution aerial photography and have a trained observer score cover against visual standards and use the scored sites to interpolate density across the region.     

A corporate approach to social monitoring and assessment for development in a fragile environment

The prevailing corporate trend regardingdevelopment of energy resources in the tropics emphasizesfinancial gain over long-term societal benefits. Somecorporations are beginning to find a competitive advantage linkedto proactive relations with host communities and adequateprotection of fragile ecosystems. Herein, we describe a casestudy where an international energy production company workedwith stakeholders to achieve social capital and sustainabledevelopment. The strategies aimed to strengthen local capacity toimprove social welfare and to ensure conservation and wise use ofbiodiversity. We provide examples, discuss lessons learned andmake recommendations for future development projects.     

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.     

Strategies to optimize monitoring schemes of recreational waters from Salta,Argentina: a multivariate approach

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively, and cluster analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments, and Vaqueros and La Caldera rivers were the most similar. Canonical correlation analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and principal component analysis allowed finding relationships among the nine measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros rivers were influenced by recreational activities. Discriminant analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved.     

A novel approach to rapid detection of acute water toxicity and its policy implications for grassroots sustainable environmental monitoring

Unknown chemicals, emerging contaminants, and the resulting reactions among them make early detection and warning of acute water toxicity extremely challenging. The conventional approach using small fish for toxicity monitoring normally requires a designated species of fish and over a week's time to complete, including dilution of the wastewater, a pre-test, and a full test. It often increases chances for error and delays emergency management. This paper reports a novel approach, based on grassroots knowledge and field and lab experience in Jilin, China. This approach uses a combination of different species of aquarium fish to achieve fast and reliable monitoring. It tests the original source water directly without going through the dilution procedure, while paying attention to the time factor. The approach does not require a pre-test and may shorten the time needed for detecting acute toxicity to a few minutes, using a new classification of aquatic toxicity levels. It is inexpensive to use and may be easily adopted by grassroots organizations. The approach is "greener" and more financially and socially sustainable than traditional approaches. The wide use of the approach has the potential to encourage policy innovations for making toxicity monitoring grassroots based and more effective in reducing acute contamination emergencies.     

Modeling economy-wide climate change impacts on Egypt: A case for an integrated approach

This study looks at the role of biophysical sectors and world markets on an integrated economic assessment of climate change impacts on Egypt. Using the outcome from Global Circulation Models (GCMs) and a global model of world food trade (the Basic Linked System; BLS) — changes in crop yields; crop water demands; water and land resource availability; and world market prices were applied to a dynamic, computable general equilibrium model of Egypt (the Standard National Model of the BLS). Modeling results consistently showed that the net effect of climate change on the macro indicator of per capita GDP was not great — regardless of the level of integration. This outcome was only realized through autonomous economic adjustments which implied significant socio-economic and structural change. Including or excluding certain biophysical sectors and world markets influenced the development path and the nature of the autonomous adjustments. The importance of a particular biophysical sector or of world markets was also dependent upon the specific climate change scenario.     

An innovative approach to inventory and monitoring of natural resources in the Mexican State of Jalisco

This paper discusses the statistical design and estimation processes developed for the assessment of key land resources relevant to questions of their condition and change in the State of Jalisco, Mexico. Some initial results of the first phase of Jalisco's Natural Resource Inventory and Monitoring Program (IMRENAT) conducted in 2006 are presented and discussed. Since this is a relatively new approach for an inventory and monitoring program of this magnitude, designed from the beginning specifically for integration with orbital satellite data, it is anticipated that changes will have to be made over time to improve conceptual and operational design aspects. Alternative remote sensing capabilities to further improve local information, more sophisticated analysis to satisfy the requirements of officials as they become more aware of the capabilities of the system, possible improvements in change assessments over time, and opportunities to focus on specific interesting changes will be assessed. As this study indicates, current and future information technology advancements provide a solid prospect for the development and application of integrative sampling strategies to support sustainability science and management processes.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

Use of perlite in cadmium plant studies: an approach to polluted soil conditions

Two different types of hydroponic cultures, "water culture" and "perlite system", were compared using white lupin plants (Lupinus albus L., cv. Marta) under different Cd treatments: 0, 0.2, 0.6, 2, 4, 6, 13, 20, 40 and 60 microM (water culture) and 0.2, 2, 20, 60 and 150 microM (moistened perlite). Fresh weight, shoot and root length, and total Cd concentration in the plants were measured. Moreover, a batch experiment was carried out to study the ability of perlite to adsorb and desorb Cd from nutrient solution. Lupin plants under Cd treatments in "water culture" showed a higher growth inhibition than those grown on perlite. A high positive correlation between Cd concentration in the plant and Cd supply was obtained regardless of the substrate used. Moreover, a high positive correlation between Cd doses with the "perlite system" and their equivalent Cd doses estimated for the "water culture" system was observed. Thus, the "water culture-equivalent" Cd doses were 14 times lower than the Cd doses in the perlite system. On the other hand, desorbed Cd concentrations were calculated giving values 12 times lower than the tested Cd doses.     

Application of a risk assessment based approach to designing ambient air quality monitoring networks for evaluating non-cancer health impacts

An ambient air quality monitoring network has been establishedusing risk assessment techniques to evaluate adverse health effects from exposures to airborne contaminants. The risk assessment method was compared to traditional methods of establishing air quality monitoring networks: identifying maximumconcentration impacts or maximum total population. Results suggest that the health risk method best predicted the locationof adverse, non-carcinogenic respiratory illnesses during the evaluation period. Spearman Rank Correlation Coefficient, r _s, values obtained using the risk assessment method werestatistically greater than the values obtained using theconcentration and population methods. The concentration methodwas the least accurate predictor of adverse effects.     

A systems approach to modeling Community-Based Environmental Monitoring: a case of participatory water quality monitoring in rural Mexico

Community-Based Environmental Monitoring (CBM) is a social practice that makes a valuable contribution to environmental management and construction of active societies for sustainable future. However, its documentation and analysis show deficiencies that hinder contrast and comparison of processes and effects. Based on systems approach, this article presents a model of CBM to orient assessment of programs, with heuristic or practical goals. In a focal level, the model comprises three components, the social subject, the object of monitoring, and the means of action, and five processes, data management, social learning, assimilation/decision making, direct action, and linking. Emergent properties were also identified in the focal and suprafocal levels considering community self-organization, response capacity, and autonomy for environmental management. The model was applied to the assessment of a CBM program of water quality implemented in rural areas in Mexico. Attributes and variables (indicators) for components, processes, and emergent properties were selected to measure changes that emerged since the program implementation. The assessment of the first 3 years (2010–2012) detected changes that indicated movement towards the expected results, but it revealed also the need to adjust the intervention strategy and procedures. Components and processes of the model reflected relevant aspects of the CBM in real world. The component called means of action as a key element to transit “from the data to the action.” The CBM model offered a conceptual framework with advantages to understand CBM as a socioecological event and to strengthen its implementation under different conditions and contexts.     

An innovative statistical approach for analysing non-continuous variables in environmental monitoring: assessing temporal trends of TBT pollution

The current work presents an innovative statistical approach to model ordinal variables in environmental monitoring studies. An ordinal variable has values that can only be compared as "less", "equal" or "greater" and it is not possible to have information about the size of the difference between two particular values. The example of ordinal variable under this study is the vas deferens sequence (VDS) used in imposex (superimposition of male sexual characters onto prosobranch females) field assessment programmes for monitoring tributyltin (TBT) pollution. The statistical methodology presented here is the ordered logit regression model. It assumes that the VDS is an ordinal variable whose values match up a process of imposex development that can be considered continuous in both biological and statistical senses and can be described by a latent non-observable continuous variable. This model was applied to the case study of Nucella lapillus imposex monitoring surveys conducted in the Portuguese coast between 2003 and 2008 to evaluate the temporal evolution of TBT pollution in this country. In order to produce more reliable conclusions, the proposed model includes covariates that may influence the imposex response besides TBT (e.g. the shell size). The model also provides an analysis of the environmental risk associated to TBT pollution by estimating the probability of the occurrence of females with VDS ≥ 2 in each year, according to OSPAR criteria. We consider that the proposed application of this statistical methodology has a great potential in environmental monitoring whenever there is the need to model variables that can only be assessed through an ordinal scale of values.     

Indices for assessment and monitoring of large mammals within an adaptive management framework

Many development projects intended to exploit natural resourcesare occurring in fragile ecosystems, and therefore the need forsound biodiversity assessment and monitoring programs is growing.Large mammals are important components of these fragile ecosystems, yet there are few strategies that attempt to assess and monitor entire large mammal communities in relation to development projects. We propose the use of two indices applied within a framework of adaptive management. An occurrence indexassesses the composition and distribution of large mammals at a site, and an abundance index monitors the abundance of large mammals over time in relation to development. We discuss the design, applicability and effectiveness of these indices based onour experience with a natural gas development project in the Amazon forests of southeastern Peru.