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1. Global forest management data for 2015 at a 100 m resolution

Author

Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S., Kraxner, F., Rabia, A.H., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Å., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., Fritz, S., Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S., Kraxner, F., Rabia, A.H., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Å., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., and Fritz, S.

Abstract

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.

Published
2022

2. Global forest management data at a 100m resolution for the year 2015

Author

Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F.S., Pangin, K., Bilous, S., Kovalevskii, S.B., Kraxner, F., Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., ul Hassan Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., Fritz, S., Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F.S., Pangin, K., Bilous, S., Kovalevskii, S.B., Kraxner, F., Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., ul Hassan Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., and Fritz, S.

Abstract

We provide four data records: 1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes: “ID” is a unique location identifier “Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel. “Land_use_ID “is a land use class: 11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests. 20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc. 31 - Planted forest. 32 - Short rotation plantations for timber. 40 - Oil palm plantations. 53 - Agroforestry. “Flag” identifies a data origin: 1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach. 2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ". 3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif" 4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes: “ID” is a unique location identifier “pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection “first_landuse_class “is a land use class, as in (1). “second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence.

Published
2021

3. Methodology for generating a global forest management layer

Author

Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Muñoz Brenes, C., Krivobokov, L.V., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S.B., Harb Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S.S., Bungnamei, K., Bordolo, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A.P., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I.H., Sharma, K., Domashovets, G., Spawn, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Muñoz Brenes, C., Krivobokov, L.V., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S.B., Harb Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S.S., Bungnamei, K., Bordolo, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A.P., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I.H., Sharma, K., Domashovets, G., Spawn, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., and Blyshchyk, I.

Abstract

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects.

Published
2020

5. Global forest management data for 2015 at a 100 m resolution.

Author

Lesiv M, Schepaschenko D, Buchhorn M, See L, Dürauer M, Georgieva I, Jung M, Hofhansl F, Schulze K, Bilous A, Blyshchyk V, Mukhortova L, Brenes CLM, Krivobokov L, Ntie S, Tsogt K, Pietsch SA, Tikhonova E, Kim M, Di Fulvio F, Su YF, Zadorozhniuk R, Sirbu FS, Panging K, Bilous S, Kovalevskii SB, Kraxner F, Rabia AH, Vasylyshyn R, Ahmed R, Diachuk P, Kovalevskyi SS, Bungnamei K, Bordoloi K, Churilov A, Vasylyshyn O, Sahariah D, Tertyshnyi AP, Saikia A, Malek Ž, Singha K, Feshchenko R, Prestele R, Akhtar IUH, Sharma K, Domashovets G, Spawn-Lee SA, Blyshchyk O, Slyva O, Ilkiv M, Melnyk O, Sliusarchuk V, Karpuk A, Terentiev A, Bilous V, Blyshchyk K, Bilous M, Bogovyk N, Blyshchyk I, Bartalev S, Yatskov M, Smets B, Visconti P, Mccallum I, Obersteiner M, and Fritz S

Subjects
Ecosystem, Conservation of Natural Resources, Forests
Abstract

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., (© 2022. The Author(s).)

Published
2022
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6. Classification of Tree Species in Overstorey Canopy of Subtropical Forest Using QuickBird Images.

Author

Lin C, Popescu SC, Thomson G, Tsogt K, and Chang CI

Subjects
Trees anatomy & histology, Trees classification
Abstract

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.

Published
2015
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