Assessing change in national forest monitoring capacities of 99 tropical countries

Romijn, Erika; Lantican, C.B.; Herold, Martin; Lindquist, Erik; Ochieng, Robert M.; Wijaya, Arief; Murdiyarso, Daniel; Verchot, Louis

doi:10.1016/j.foreco.2015.06.003

Cited by 195 publications

(144 citation statements)

References 26 publications

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“…Thus, the quality of data, estimations and level of precision are quite different among them (Hansen et al 2013). For the countries only using RS surveys, some variables such as the forest area change, growing stock or biomass are sometimes difficult to estimate, particularly if there is no field assessment and precision estimates are rarely available (Grainger 2008;Romijn et al 2015).…”

Section: The Forest Resources Assessmentmentioning

confidence: 99%

The role of European National Forest Inventories for international forestry reporting

Vidal¹,

Alberdi²,

Redmond³

et al. 2016

Annals of Forest Science

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Section: The Forest Resources Assessmentmentioning

confidence: 99%

The role of European National Forest Inventories for international forestry reporting

Vidal¹,

Alberdi²,

Redmond³

et al. 2016

Annals of Forest Science

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“…Forests cover almost one third of the terrestrial surface and play an important role in biodiversity conservation and mitigation of world's climate change (Romijn et al, 2015). The identification of species and successional stage of forests helps to monitor this type of vegetation and Remote Sensing measurements appear as a promising alternative, mainly with imaging spectroscopy using hyperspectral sensors (Féret and Asner, 2013;Baldeck et al, 2015;Näsi et al, 2016;Nevalainen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Time Series of Images to Improve Tree Species Classification

Miyoshi

Imai

Moraes

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26%.

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“…About one-third of the earth's land surface is covered by forests, which store almost half of the world's terrestrial carbon in wood, leaves, roots, and soil. In developing countries, forests contribute largely to livelihood security, provide major energy sources, and various ecological, economic, social, and aesthetic functions for people (FAO, 2014;Miura et al, 2015;Romijn et al, 2015). According to Ruhyan and D'Odorico (2016), more than 27% of the global land was converted from forest to agriculture through the 1900s.…”

Section: Introductionmentioning

confidence: 99%

Forestation in Puerto Rico, 1970s to Present

Yuan¹,

Lopez²,

Arnold³

et al. 2017

JGG

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It is important to monitor the trend of forestland changes, as forests are vital sources and sinks of carbon on the earth. One of the most densely populated jurisdictions of the United States, Puerto Rico, has experienced significant transformations in the past century. This study examines forestation in the main island of Puerto Rico during the past four decades using feature extraction and change detection analysis in multitemporal Landsat satellite imagery. The results of the study show that forest cover in Puerto Rico had almost tripled from 15.7% to 45.7% between 1972 and 2014. Moreover, the forestation trend and pace in abandoned coffee plantations and pastures continued after 1990, driven by continuous socioeconomic transformation. Natural forestation and conservation efforts from the government and nongovernment organizations have also contributed to the forest growth on the island. The information gained and lessons learned during the process may be applied to other densely populated tropical insular territories.

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Assessing change in national forest monitoring capacities of 99 tropical countries

Cited by 195 publications

References 26 publications

The role of European National Forest Inventories for international forestry reporting

The role of European National Forest Inventories for international forestry reporting

Time Series of Images to Improve Tree Species Classification

Forestation in Puerto Rico, 1970s to Present

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