Sampling Strategies for Forest Monitoring from Global to National Levels

2016 ◽  
pp. 65-91
Author(s):  
Stephen V. Stehman
Keyword(s):  
Forest Monitoring ◽  
Sampling Strategies

Comparing sampling strategies in forest monitoring programs

1996 ◽  
Vol 82 (1-3) ◽  
pp. 231-238 ◽  
Author(s):  
Sucharita Ghosh ◽  
John L. Innes
Keyword(s):  
Forest Monitoring ◽  
Sampling Strategies ◽  
Monitoring Programs

Who owns a personal home page?

2003 ◽  
Vol 62 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Astrid Schütz ◽  
Franz Machilek
Keyword(s):  
Search Engine ◽  
Home Page ◽  
Sampling Strategies ◽  
Home Pages ◽  
Personal Home Pages ◽  
Age And Sex

Research on personal home pages is still rare. Many studies to date are exploratory, and the problem of drawing a sample that reflects the variety of existing home pages has not yet been solved. The present paper discusses sampling strategies and suggests a strategy based on the results retrieved by a search engine. This approach is used to draw a sample of 229 personal home pages that portray private identities. Findings on age and sex of the owners and elements characterizing the sites are reported.

Developing community-based forest monitoring systems through action research

2015 ◽  
Vol 5 (1) ◽  
pp. 16-19
Author(s):  
Henry Scheyvens ◽  
Makino Yamanoshita ◽  
Taiji Fujisaki ◽  
Agus Setyarso ◽  
Saykham Boutthavong ◽  
...  
Keyword(s):  
Action Research ◽  
Forest Monitoring ◽  
Monitoring Systems ◽  
Community Based

Wald und Klimawandel in der inneralpinen Trockenregion Visp

2012 ◽  
Vol 163 (12) ◽  
pp. 481-492
Author(s):  
Andreas Rigling ◽  
Ché Elkin ◽  
Matthias Dobbertin ◽  
Britta Eilmann ◽  
Arnaud Giuggiola ◽  
...  
Keyword(s):  
Climate Change ◽  
Ecosystem Services ◽  
Forest Management ◽  
Forest Ecosystem ◽  
Bark Beetles ◽  
Forest Ecosystems ◽  
Field Experiments ◽  
Forest Monitoring ◽  
Management Interventions ◽  
Forest Ecosystem Services

Forest and climate change in the inner-Alpine dry region of Visp Over the past decades, observed increases in temperature have been particularly pronounced in mountain regions. If this trend should continue in the 21st Century, frequency and intensity of droughts will increase, and will pose major challenges for forest management. Under current conditions drought-related tree mortality is already an important factor of forest ecosystems in dry inner-Alpine valleys. Here we assess the sensitivity of forest ecosystems to climate change and evaluate alternative forest management strategies in the Visp region. We integrate data from forest monitoring plots, field experiments and dynamic forests models to evaluate how the forest ecosystem services timber production, protection against natural hazards, carbon storage and biodiver-sity will be impacted. Our results suggest that at dry low elevation sites the drought tolerance of native tree species will be exceeded so that in the longer term a transition to more drought-adapted species should be considered. At medium elevations, drought and insect disturbances as by bark beetles are projected to be important for forest development, while at high elevations forests are projected to expand and grow better. All of the ecosystem services that we considered are projected to be impacted by changing forest conditions, with the specific impacts often being elevation-dependent. In the medium term, forest management that aims to increase the resilience of forests to drought can help maintain forest ecosystem services temporarily. However, our results suggest that relatively rigid management interventions are required to achieve significant effects. By using a combination of environmental monitoring, field experiments and modeling, we are able to gain insight into how forest ecosystem, and the services they provide, will respond to future changes.

scholarly journals How to measure uncertainty in uncertainty sampling for active learning

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Sampling Strategies ◽  
Total Uncertainty ◽  
Uncertainty Sampling ◽  
Different Types ◽  
Alternative Approaches ◽  
Active Learner ◽  
Probabilistic Nature ◽  
Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

scholarly journals Resource and dependency based test case generation for RESTful Web services

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Man Zhang ◽  
Bogdan Marculescu ◽  
Andrea Arcuri
Keyword(s):  
Web Services ◽  
Domain Knowledge ◽  
Automated System ◽  
Test Case ◽  
Test Cases ◽  
Evolutionary Search ◽  
Sampling Strategies ◽  
Mutation Operators ◽  
On Line ◽  
Restful Web Services

AbstractNowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.

Urban forest monitoring based on multiple features at the single tree scale by UAV

2020 ◽  
pp. 126958
Author(s):  
Xiaofeng Wang ◽  
Yi Wang ◽  
Chaowei Zhou ◽  
Lichang Yin ◽  
Xiaoming Feng
Keyword(s):  
Urban Forest ◽  
Forest Monitoring ◽  
Multiple Features ◽  
Single Tree

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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