scholarly journals Identifying structural differences in mixed mesophytic and northern hardwood forests on the Monongahela National Forest using remote sensing data

1999 ◽  
Author(s):  
John Richard Bender
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Hardwood Forests ◽  
National Forest ◽  
Northern Hardwood Forests ◽  
Northern Hardwood ◽  
Sensing Data ◽  
Monongahela National Forest ◽  
Structural Differences

Supporting decision makers through rapid assessments of remote sensing data: the case of Tefé National Forest, Brazil

2020 ◽  
Author(s):  
Ana Carolina Moreira Pessôa ◽  
Liana O. Anderson ◽  
Rafael Suertegaray Rossato ◽  
Victor Marchezini ◽  
Bruna Maria Pechini Bento ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Protected Area ◽  
Rapid Assessment ◽  
Remote Sensing Data ◽  
Pilot Project ◽  
Management Plan ◽  
Extreme Drought ◽  
National Forest ◽  
Sensing Data

<p>Providing scientific subsidies for public policies is a compromise that is beyond the boundaries created by the academic universe, requiring scientists to respond to the challenges posed by increasingly complex societies, both socially and environmentally. Considering this, the objective of this work was to build a pilot project for rapid assessment of Tefé National Forest (TNF) land use zoning and evaluate its relevance as a tool to support actions and influence discussions in protected area management councils.</p><p>The assessment considered remote sensing data on deforestation and fire from 2005 to 2015. Deforestation maps (PRODES-INPE) and active fire (MODIS) information were overlapped with TNF land use zoning. Although National Forest, in general, has its land use rules provided by law, each protected area defines on its Management Plan their own land use zoning, with specific rules.</p><p>The study showed that in 2015, 97% of TNF was covered by forest, and although no deforestation was recorded in the same year, the number of active fires was 1.8 times higher than the average from 2005 to 2014. This demonstrates the vulnerability of this area to the extreme drought which affected the region this year. The Population Zone, where 44% of the TNF population lives, recorded the highest rates of deforestation and fire. The Preservation Zone, on the other hand, showed to be fulfilling its function, presenting no active fires and only one deforestation event during the whole analyzed period.</p><p>These results were presented at the 20th TNF Council Meeting, in 2017. The TNF manager pointed out the great importance of spatial and temporal diagnoses, which can exert in prioritize actions to tackle specific problems in most threatened zones. Community leaders participating in the meeting contributed to the completion of the results with in situ day-to-day reports, offering hypotheses for some phenomena observed on the assessment, such as the deforestation observed in 2010. After that, it became clear that actions directly focused on the Population Zone, and mainly related to the use of fire in years of extreme drought, can improve the conservation outcome for this protected area. Integrated socio-environmental diagnosis, such as this pilot project, can be an important tool, allowing a broader version of the monitoring strategies.</p>

Combining national forest inventory field plots and remote sensing data for forest databases

2008 ◽  
Vol 112 (5) ◽  
pp. 1982-1999 ◽  
Author(s):  
Erkki Tomppo ◽  
Håkan Olsson ◽  
Göran Ståhl ◽  
Mats Nilsson ◽  
Olle Hagner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
National Forest ◽  
Sensing Data ◽  
Field Plots

Lidar remote sensing of biophysical properties of tolerant northern hardwood forests

2003 ◽  
Vol 29 (5) ◽  
pp. 658-678 ◽  
Author(s):  
Kevin Lim ◽  
Paul Treitz ◽  
Ken Baldwin ◽  
Ian Morrison ◽  
Jim Green
Keyword(s):  
Remote Sensing ◽  
Hardwood Forests ◽  
Biophysical Properties ◽  
Northern Hardwood Forests ◽  
Northern Hardwood

New estimate of growing stock volume and carbon sequestration of Russian forests based on national forest inventory and remote sensing data

2021 ◽  
Author(s):  
Dmitry Schepaschenko ◽  
Elena Moltchanova ◽  
Stanislav Fedorov ◽  
Victor Karminov ◽  
Petr Ontikov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soviet Union ◽  
Forest Inventory ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
The State ◽  
National Forest ◽  
Sensing Data ◽  
Growing Stock ◽  
Russian Forests

<p>Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported (FAO, 2014) almost no changes in growing stock (+1.8%) and biomass (+0.6%). Yet remote sensing products indicate increased vegetation productivity (Guay et al., 2014), tree cover (Song et al., 2018) and above-ground biomass (Liu et al., 2015). Here, we challenge the official national statistics with a combination of recent National Forest Inventory and remote sensing data products to provide an alternative estimate of the growing stock of Russian forests and assess the relative changes in the post-Soviet era. Our estimate for the year 2014 is 118.29±1.3 10<sup>9</sup> m<sup>3</sup>, which is 48% higher than the official value reported for the same year in the State Forest Register. The difference is explained by increased biomass density in forested areas (+39%) and larger forest area estimates (+9%). Using the last Soviet Union report (1988) as a reference, Russian forests have accumulated 1163×10<sup>6</sup> m<sup>3</sup> yr<sup>-1</sup> of growing stock between 1988–2014, which compensates for forest growing stock losses in tropical countries (FAO FRA, 2015). Our estimate of the growing stock of managed forests is 94.2 10<sup>9</sup> m<sup>3</sup>, which corresponds to sequestration of 354 Tg C yr<sup>-1</sup> in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory (National Inventory Report, 2020).</p><p>Acknowledgement: The research plots data collection was performed within the framework of the state assignment of the Center for Forest Ecology and Productivity of the Russian Academy of Sciences (no. АААА-А18-118052590019-7), and the ground data pre-processing were financially supported by the Russian Science Foundation (project no. 19-77-30015).</p>

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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