Remote sensing support for national forest inventories

McRoberts, Ronald E.; Tomppo, Erkki

doi:10.1016/j.rse.2006.09.034

Cited by 376 publications

(260 citation statements)

References 41 publications

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“…For example, Canada's new National Forest Inventory is based on a 1% sample of the 401.9 million hectare of Canada's forests and other wooded lands (Gillis et al 2005). Aerial photography has played an important role in the development of forest inventories (Hall 2003), and more recently, light detection and ranging (LIDAR) instruments have emerged as useful tools for collecting vertical forest structure information (McRoberts and Tomppo 2007).…”

Section: Introductionmentioning

confidence: 99%

Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder

White

Stinson

et al. 2009

Environ Monit Assess

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Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p < 0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare ( p < 0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant ( p = 0.288). Overall, the relationship between the two biomass estimates was strong (R 2 = 0.92, p < 0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.

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Section: Introductionmentioning

confidence: 99%

Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder

White

Stinson

et al. 2009

Environ Monit Assess

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“…Tomppo et al 2008, McRoberts andTomppo 2007;Boyd et al 2005;Cohen et al 1998;Falkenström and Ekstrand 2002). In Sweden, data from the SPOT (Satellite Probatoire d'Observation de la Terre) and Landsat Thematic Mapper (TM and ETM+) satellites have commonly been used for national mapping applications (Olsson et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands

Reese

Nilsson

Olsson

2009

International Journal of Remote Sensing

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In this study, the utility of AWiFS data in relation to stem volume estimation for managed boreal forest stands was investigated. Multiple linear regressions were used to predict stem volume (m 3 ha -1 ) with standwise mean spectral values as the independent variables. For comparison, two SPOT-5 images were also used, one with nearly simultaneous acquisition. The adjusted coefficient of determination (R 2 adj ) using AWiFS data to predict stem volume was 0.573 (SE 56.9%) while SPOT had an R 2 adj of 0.598 (SE 55.2%). All bands were negatively correlated, with the SWIR band having the single strongest correlation with stem volume. The best two band predictor of stem volume was the NIR and red band for AWiFS, and the NIR and SWIR bands for SPOT. When stem volume was predicted based on stand size, AWiFS and SPOT produced an R 2 adj of 0.310 and 0.293, respectively, for stands less than 2 ha in size. A steady increase of predictive ability appeared to increase with stand size, with the highest R

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“…Systematic national forest monitoring however, began only in the early 19 th Century. The practice of forest monitoring was later exported to the European colonies, and has since been taken over and conducted by post-colonial governments in many developing countries (FAO, 1993;Freitas et al, 2010;Lei et al, 2010;McRoberts and Tomppo, 2007;Tomppo et al, 2010a). From an earlier focus on assessment of timber stocks, the practice has evolved to include assessments of other forest variables than timber (Mohren et al, 2012) such as 'extent of forests' and 'forest health and vitality'.…”

Section: Redd+ and New Demands For Forest Monitoring In Developing Comentioning

confidence: 99%

“…Although the variables included in national forest surveys have been influenced by international developments (Holmgren and Persson, 2002a, b;Lei et al, 2009) Although the development of forest inventories in developing countries has been examined in terms of technical aspects, such as sampling strategies, plot designs, use of RS and information technologies, et cetera (Hansen et al, 2008;Holmgren and Persson, 2002a;Joseph et al, 2013a;Lei et al, 2010;McRoberts and Tomppo, 2007;Romijn et al, 2012;Tomppo et al, 2010a;Xie et al, 2011), exactly how and to what extent the international discussion on REDD+ MRV has shaped institutional arrangements for forest measurements in developing countries is unknown. This paper aims to bridge this knowledge gap by examining: (1) the historical development of institutional arrangements for forest assessments in Peru; and (2) how and to what extent the evolution of these institutional arrangements have been shaped by international processes on forests, especially REDD+ MRV.…”

Section: Introductionmentioning

confidence: 99%

“…Since the post-colonial era, many developing countries have monitored and reported on the status and extent of their forests (FAO, 1993;Freitas et al, 2010;Lei et al, 2010;Lund, no.date;McRoberts and Tomppo, 2007;Tomppo et al, 2010a). Although initially focused on timber stocks, forest monitoring in both developed and developing countries have evolved to include measurement of other variables such as forest health and vitality (Lund, no.date;Mohren et al, 2012), largely in response to the paradigm shift from 'mono-functional' to 'multi-functional' forestry.…”

Section: Introductionmentioning

confidence: 99%

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The role of forests in climate change mitigation : a discursive-institutional analysis of REDD+ MRV

Ochieng

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Remote sensing support for national forest inventories

Cited by 376 publications

References 41 publications

Implications of differing input data sources and approaches upon forest carbon stock estimation

Implications of differing input data sources and approaches upon forest carbon stock estimation

Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands

The role of forests in climate change mitigation : a discursive-institutional analysis of REDD+ MRV

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