The forest resources of the Huron-Manistee National Forest, 1993.

1996 ◽  
Author(s):  
David Haugen ◽  
Rosalie Ingram ◽  
Forrest Ruppert
Keyword(s):  
Forest Resources ◽  
National Forest ◽  
Manistee National Forest

scholarly journals A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Matieu Henry ◽  
Zaheer Iqbal ◽  
Kristofer Johnson ◽  
Mariam Akhter ◽  
Liam Costello ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Data Collection ◽  
Land Cover ◽  
Forest Inventory ◽  
Sustainable Forest Management ◽  
National Forest Inventory ◽  
Forest Resources ◽  
National Forest ◽  
Land Cover Map

Abstract Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

scholarly journals The forest resources of the Chequamegon-Nicolet national forest

1998 ◽  
Author(s):  
David E. Haugen ◽  
Phillip C. Freeman ◽  
Mark A. Theisen ◽  
Keyword(s):  
Forest Resources ◽  
National Forest

Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA

2013 ◽  
Vol 22 (2) ◽  
pp. 174 ◽  
Author(s):  
Avi Bar Massada ◽  
Alexandra D. Syphard ◽  
Susan I. Stewart ◽  
Volker C. Radeloff
Keyword(s):  
Random Forests ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Environmental Data ◽  
Distribution Model ◽  
National Forest ◽  
Similar Species ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Manistee National Forest

Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar species-distribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution model types: one parametric, statistical model (Generalised Linear Models, GLM) and two machine-learning algorithms (Random Forests and Maximum Entropy, Maxent). We parameterised the models using 16 years of ignitions data and environmental data for the Huron–Manistee National Forest in Michigan, USA. Random Forests and Maxent had slightly better prediction accuracies than did GLM, but model fit was similar for all three. Variables related to human population and development were the best predictors of wildfire ignition locations in all models (although variable rankings differed slightly), along with elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models. We thus suggest that when accurate predictions are desired, the outcomes of different model types should be compared, or alternatively combined, to produce ensemble predictions.

scholarly journals Forest resources of the Hoosier National Forest, 2005

2007 ◽  
Author(s):  
Christoper W. Woodall ◽  
Judith A. Perez ◽  
Thomas R. Thake
Keyword(s):  
Forest Resources ◽  
National Forest ◽  
Hoosier National Forest
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