Global forest management data for 2015 at a 100 m resolution

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services

Diameter Structure Analysis of Forest Stand and Selection of Suitable Model

Ecologically and economically it is important to understand how many tree stems are in each diameter class. The purpose of this study was to fi nd larch forest ( Larix sibirica ) diameter distribution model among Weibull, Burr and Johnson SB distributions. Inventory was conducted near Gachuurt village, Ulaanbaatar, Mongolia. The goodness of fi t test were accompanied with Kolmogorov- Smirnov, Anderson-Darling and Chi-Squared tests for distribution models. Study result shows Johnson SB distribution gave the best performance in terms of quality of fi t to the diameter distribution of larch forest

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Changes of Land Use and Land Cover (LULC) affect atmospheric, climatic, and biological spheres of the earth. Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction. This paper examined effects of pansharpening and atmospheric correction on LULC classification. Object-Based Support Vector Machine (OB-SVM) and Pixel-Based Maximum Likelihood Classifier (PB-MLC) were applied for LULC classification. Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image. Nevertheless, pansharpening plays much more important roles on the classification accuracy than the atmospheric correction. It can help to increase classification accuracy by 12% on average compared to the ones without pansharpening. PB-MLC and OB-SVM achieved similar classification rate. This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82% and 89% respectively. A combination of atmospheric correction, pansharpening, and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images

Flowchart of image processing and analyses used in this study.

<p>Flowchart of image processing and analyses used in this study.</p

Classification of Tree Species in Overstorey Canopy of Subtropical Forest Using QuickBird Images

<div><p>This paper proposes a supervised classification scheme to identify 40 tree species (2 coniferous, 38 broadleaf) belonging to 22 families and 36 genera in high spatial resolution QuickBird multispectral images (HMS). Overall kappa coefficient (OKC) and species conditional kappa coefficients (SCKC) were used to evaluate classification performance in training samples and estimate accuracy and uncertainty in test samples. Baseline classification performance using HMS images and vegetation index (VI) images were evaluated with an OKC value of 0.58 and 0.48 respectively, but performance improved significantly (up to 0.99) when used in combination with an HMS spectral-spatial texture image (SpecTex). One of the 40 species had very high conditional kappa coefficient performance (SCKC ≥ 0.95) using 4-band HMS and 5-band VIs images, but, only five species had lower performance (0.68 ≤ SCKC ≤ 0.94) using the SpecTex images. When SpecTex images were combined with a Visible Atmospherically Resistant Index (VARI), there was a significant improvement in performance in the training samples. The same level of improvement could not be replicated in the test samples indicating that a high degree of uncertainty exists in species classification accuracy which may be due to individual tree crown density, leaf greenness (inter-canopy gaps), and noise in the background environment (intra-canopy gaps). These factors increase uncertainty in the spectral texture features and therefore represent potential problems when using pixel-based classification techniques for multi-species classification.</p></div

A false color picture of the QuickBird multispectral image (a composite of band 4, 3, 2 for red, green, and blue) showing the land covers over the study site.

<p>A false color picture of the QuickBird multispectral image (a composite of band 4, 3, 2 for red, green, and blue) showing the land covers over the study site.</p

A comparison of the training performance, test accuracy, and uncertainty among classifiers in variant classification schemes.

<p>A comparison of the training performance, test accuracy, and uncertainty among classifiers in variant classification schemes.</p

Calibration coefficients for absolute radiance conversion of 16-bit QuickBird images¹⁾.

<p>¹⁾ Revised factor for the panchromatic band varies with its exposure levels using time-delayed-integration (TDI); factor value is positively proportional to the TDI level, which can be found in. IMD files. Revised factor was set to 1 for each band for the product generated after June 6, 2003. Coefficients listed in the table address the case for 10 TDI levels. Absolute calibration coefficients for each band are also available from the. IMD file [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125554#pone.0125554.ref032" target="_blank">32</a>].</p><p>Calibration coefficients for absolute radiance conversion of 16-bit QuickBird images<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125554#t002fn001" target="_blank">¹⁾</a>.</p

Generalized trends of the average monthly temperature (curve) and precipitation (bars) of the study site for the years 2006 to 2014.

<p>Generalized trends of the average monthly temperature (curve) and precipitation (bars) of the study site for the years 2006 to 2014.</p

Comparisons of the training-samples-based SCKC for the five HCA-species and the OKC for all 40 species in the classification using various data sets.

<p>Comparisons of the training-samples-based SCKC for the five HCA-species and the OKC for all 40 species in the classification using various data sets.</p