Boser, B. E., Guyon, I. M., and Vapnik, V. N.: A training algorithm for optimal
margin classifiers, in: Proceedings of the fifth annual workshop on
Computational learning theory, 27–29 July 1992, New York, United States, 144–152,
https://doi.org/10.1145/130385.130401, 1992.
a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.:
Encoder-decoder with atrous separable convolution for semantic image
segmentation, in: Proceedings of the European conference on computer vision
(ECCV), 8–14 September 2018, Munich, Germany, 801–818,
https://doi.org/10.48550/arXiv.1802.02611, 2018.
a
Cherrington, E., Flores-Anderson, A., Thapa, R. B., Herndon, K. E., Wahome, A.,
Oduor, P., Mubea, K., Ouko, E., and Hanh, N.: Perspectives on the Future
Application of SAR in Forest and Environmental Monitoring, in: The SAR
handbook: comprehensive methodologies for forest monitoring and biomass
estimation, edited by: Flores-Anderson, A. I., Herndon, K. E., Thapa, R. B.,
and Cherrington, E., chap. 8, NASA,
https://doi.org/10.25966/nr2c-s697, 2019.
a
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.: Imagenet: A
large-scale hierarchical image database, in: 2009 IEEE conference on computer
vision and pattern recognition, IEEE, 20–25 June 2009, Miami, Florida, USA, 248–255,
https://doi.org/10.1109/CVPR.2009.5206848, 2009.
a
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X.,
Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.:
An Image is Worth 16
× 16 Words: Transformers for Image Recognition at Scale,
in: International Conference on Learning Representations, 26 April–1 May 2020, Addis Ababa, Ethiopia,
https://doi.org/10.48550/arXiv.2010.11929, 2020.
a
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F.,
Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2:
ESA's optical high-resolution mission for GMES operational services, Remote
Sens. Environ., 120, 25–36, 2012. a
Egli, S. and Höpke, M.: CNN-Based Tree Species Classification Using High
Resolution RGB Image Data from Automated UAV Observations, Remote Sens.,
12, 3892,
https://doi.org/10.3390/rs12233892, 2020.
a
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A.: The
pascal visual object classes (voc) challenge, Int. J.
Comput. Vision, 88, 303–338, 2010. a
Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M.,
Waser, L. T., Straub, C., and Ghosh, A.: Review of studies on tree species
classification from remotely sensed data, Remote Sens. Environ., 186,
64–87, 2016. a
Fricker, G. A., Ventura, J. D., Wolf, J. A., North, M. P., Davis, F. W., and
Franklin, J.: A Convolutional Neural Network Classifier Identifies Tree
Species in Mixed-Conifer Forest from Hyperspectral Imagery, Remote Sensing,
11, 2326,
https://doi.org/10.3390/rs11192326, 2019.
a
Ganz, S., Adler, P., and Kändler, G.: Forest Cover Mapping Based on a
Combination of Aerial Images and Sentinel-2 Satellite Data Compared to
National Forest Inventory Data, Forests, 11, 1322,
https://doi.org/10.3390/f11121322, 2020.
a,
b
Goutte, C. and Gaussier, E.: A probabilistic interpretation of precision,
recall and F-score, with implication for evaluation, in: Advances in
Information Retrieval. Lecture Notes in Computer Science, Vol. 3408, edited by: Losada,
D. E. and Fernández-Luna, J. M., Springer, 345–359,
https://doi.org/10.1007/978-3-540-31865-1_25, 2005.
a
Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K.: Forest stand
species mapping using the Sentinel-2 time series, Remote Sens., 11, 1197,
https://doi.org/10.3390/rs11101197,
2019.
a,
b
Hamedianfar, A., Mohamedou, C., Kangas, A., and Vauhkonen, J.: Deep learning
for forest inventory and planning: a critical review on the remote sensing
approaches so far and prospects for further applications, Forestry, 95, 451–465, 2022.
a,
b
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C.,
Turakhia, M. P., and Ng, A. Y.: Cardiologist-level arrhythmia detection and
classification in ambulatory electrocardiograms using a deep neural network,
Nat. Med., 25, 65–69, 2019. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, in: Proceedings of the IEEE conference on computer vision and
pattern recognition, 27–30 June 2016, Las Vegas, Nevada, USA, 770–778,
https://doi.org/10.1109/CVPR.2016.90, 2016.
a
Helber, P., Bischke, B., Dengel, A., and Borth, D.: EuroSAT: A Novel Dataset
and Deep Learning Benchmark for Land Use and Land Cover Classification, IEEE
J. Sel. Top. Appl.,
12, 2217–2226, 2019. a
Hemmerling, J., Pflugmacher, D., and Hostert, P.: Mapping temperate forest tree
species using dense Sentinel-2 time series, Remote Sens. Environ.,
267, 112743,
https://doi.org/10.1016/j.rse.2021.112743, 2021.
a
Hlásny, T., Barka, I., Roessiger, J., Kulla, L., Trombik, J.,
Sarvašová, Z., Bucha, T., Kovalčík, M., and
Čihák, T.: Conversion of Norway spruce forests in the face of
climate change: a case study in Central Europe, Eur. J. For.
Res., 136, 1013–1028, 2017. a
Holzwarth, S., Thonfeld, F., Abdullahi, S., Asam, S., Da Ponte Canova, E.,
Gessner, U., Huth, J., Kraus, T., Leutner, B., and Kuenzer, C.: Earth
observation based monitoring of forests in Germany: A review, Remote Sens.,
12, 3570,
https://doi.org/10.3390/rs12213570, 2020.
a,
b,
c,
d
Hong, D., Gao, L., Yokoya, N., Yao, J., Chanussot, J., Du, Q., and Zhang, B.:
More diverse means better: Multimodal deep learning meets remote-sensing
imagery classification, IEEE T. Geosci. Remote Sens.,
59, 4340–4354, 2020. a
Immitzer, M., Neuwirth, M., Böck, S., Brenner, H., Vuolo, F., and
Atzberger, C.: Optimal input features for tree species classification in
Central Europe based on multi-temporal Sentinel-2 data, Remote Sens., 11,
2599,
https://doi.org/10.3390/rs11222599, 2019.
a,
b
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I,
II and III to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, IPCC, Geneva, Switzerland,
https://www.ipcc.ch/report/ar5/syr/ (last access: 31 January 2023), 2014. a
Karlson, M., Ostwald, M., Reese, H., Sanou, J., Tankoano, B., and Mattsson, E.:
Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian
woodlands using Landsat 8 and random forest, Remote Sens., 7,
10017–10041, 2015. a
Kattenborn, T., Eichel, J., Wiser, S., Burrows, L., Fassnacht, F. E., and
Schmidtlein, S.: Convolutional Neural Networks accurately predict cover
fractions of plant species and communities in Unmanned Aerial Vehicle
imagery, Remote Sensing in Ecology and Conservation, 6, 472–486, 2020. a
Kattenborn, T., Leitloff, J., Schiefer, F., and Hinz, S.: Review on
Convolutional Neural Networks (CNN) in vegetation remote sensing, ISPRS
J. Photogramm., 173, 24–49, 2021. a
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu,
T.-Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Adv. Neur. In., 30, 3146–3154, 2017.
a,
b
Kollert, A., Bremer, M., Löw, M., and Rutzinger, M.: Exploring the
potential of land surface phenology and seasonal cloud free composites of one
year of Sentinel-2 imagery for tree species mapping in a mountainous region,
Int. J. Appl. Earth Obs., 94,
102208, 2021.
a,
b
Kowalski, K., Senf, C., Hostert, P., and Pflugmacher, D.: Characterizing spring
phenology of temperate broadleaf forests using Landsat and Sentinel-2 time
series, Int. J. Appl. Earth Obs., 92, 102172,
https://doi.org/10.1016/j.jag.2020.102172, 2020.
a,
b
Krzystek, P., Serebryanyk, A., Schnörr, C., Červenka, J., and
Heurich, M.: Large-Scale Mapping of Tree Species and Dead Trees in
Šumava National Park and Bavarian Forest National Park Using Lidar and
Multispectral Imagery, Remote Sensing, 12, 661,
https://doi.org/10.3390/rs12040661, 2020.
a
LGLN: Geodatenportal Niedersachsen (GDI-NI), Landesamt für Geoinformation und
Landesvermessung Niedersachsen,
https://www.geodaten.niedersachsen.de/, last access: 31 December 2020. a
MacDicken, K., Jonsson, Ö., Piña, L., Maulo, S., Contessa, V., Adikari,
Y., Garzuglia, M., Lindquist, E., Reams, G., and D'Annunzio, R.: Global
forest resources assessment 2015: how are the world's forests changing?, FAO,
https://agris.fao.org/agris-search/search.do?recordID=XF2017001127 (last access: 31 January 2023),
2016. a
Martins, G. B., La Rosa, L. E. C., Happ, P. N., Coelho Filho, L. C. T., Santos,
C. J. F., Feitosa, R. Q., and Ferreira, M. P.: Deep learning-based tree
species mapping in a highly diverse tropical urban setting, Urban For. Urban Gree., 64, 127241,
https://doi.org/10.1016/j.ufug.2021.127241, 2021.
a
Mäyrä, J., Keski-Saari, S., Kivinen, S., Tanhuanpää, T.,
Hurskainen, P., Kullberg, P., Poikolainen, L., Viinikka, A., Tuominen, S.,
Kumpula, T., and Vihervaara, P.: Tree species classification from airborne hyperspectral
and LiDAR data using 3D convolutional neural networks, Remote Sens.
Environ., 256, 112322,
https://doi.org/10.1016/j.rse.2021.112322, 2021.
a
NLF: Betriebsinventurdaten der Niedersächsischen Landesforsten (BI),
Niedersächsische Landesforsten, 2021a.
a,
b,
c,
d
NLF: Waldeinrichtungsflächen der Niedersächsischen Landesforsten
(WEFL), Niedersächsische Landesforsten (Lower Saxony State Forest
Management Organisation), 2021b.
a,
b,
c,
d
NW-FVA: Waldzustandsbericht 2021 für Niedersachsen, Nordwestdeutsche
Forstliche Versuchsanstalt, Niedersächsisches Ministerium für
Ernährung, Landwirtschaft und Verbraucherschutz, Zenodo,
https://doi.org/10.5281/zenodo.5615008, 2021.
a
Ottosen, T.-B., Petch, G., Hanson, M., and Skjøth, C. A.: Tree cover mapping
based on Sentinel-2 images demonstrate high thematic accuracy in Europe,
Int. J. Appl. Earth Obs., 84,
101947,
https://doi.org/10.1016/j.jag.2019.101947, 2020.
a
Saborowski, J., Marx, A., Nagel, J., and Böckmann, T.: Double sampling for
stratification in periodic inventories—Infinite population approach, Forest
Ecol. Manag., 260, 1886–1895, 2010. a
Schiefer, F., Kattenborn, T., Frick, A., Frey, J., Schall, P., Koch, B., and
Schmidtlein, S.: Mapping forest tree species in high resolution UAV-based
RGB-imagery by means of convolutional neural networks, ISPRS J.
Photogramm., 170, 205–215, 2020.
a,
b
Schmitt, M., Hughes, L. H., Qiu, C., and Zhu, X. X.: SEN12MS–A Curated Dataset
of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and
Data Fusion, arXiv [preprint],
https://doi.org/10.48550/arXiv.1906.07789, 2019.
a
Schulz, C., Ahlswede, S., Gava, C., Helber, P., Bischke, B., Arias, F., Förster, M., Hees, J., Demir, B., and Kleinschmit, B.: TreeSatAI Benchmark Archive for Deep Learning in Forest Applications (1.0.1), Zenodo [code, data set],
https://doi.org/10.5281/zenodo.6598390, 2022.
a,
b,
c,
d,
e,
f
Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A., and Davis, C. H.:
Training deep convolutional neural networks for land–cover classification of
high-resolution imagery, IEEE Geosci. Remote S., 14,
549–553, 2017. a
Senf, C., Buras, A., Zang, C. S., Rammig, A., and Seidl, R.: Excess forest
mortality is consistently linked to drought across Europe, Nat.
Commun., 11, 1–8, 2020. a
Sesnie, S. E., Finegan, B., Gessler, P. E., Thessler, S., Ramos Bendana, Z.,
and Smith, A. M.: The multispectral separability of Costa Rican rainforest
types with support vector machines and Random Forest decision trees,
Int. J. Remote Sens., 31, 2885–2909, 2010. a
Smith, L. N.: Cyclical learning rates for training neural networks, in: 2017
IEEE winter conference on applications of computer vision (WACV), IEEE, 24–31 March 2017, Santa Rosa, CA, USA,
464–472,
https://doi.org/10.1109/WACV.2017.58, 2017.
a
Sumbul, G., Charfuelan, M., Demir, B., and Markl, V.: Bigearthnet: A
Large-Scale Benchmark Archive for Remote Sensing Image Understanding, in:
IEEE International Geoscience and Remote Sensing Symposium, 28 July–2 August 2019, Yokohama, Japan, 5901–5904,
https://doi.org/10.1109/IGARSS.2019.8900532,
2019.
a,
b
Sumbul, G., De Wall, A., Kreuziger, T., Marcelino, F., Costa, H., Benevides,
P., Caetano, M., Demir, B., and Markl, V.: BigEarthNet-MM: A Large-Scale,
Multimodal, Multilabel Benchmark Archive for Remote Sensing Image
Classification and Retrieval, IEEE Geoscience and Remote Sensing Magazine, 9,
174–180, 2021. a
Takahashi, K., Yamamoto, K., Kuchiba, A., and Koyama, T.: Confidence interval
for micro-averaged F1 and macro-averaged F1 scores, Appl. Intell., 52,
4961–4972, 2022. a
Tanase, M. A., Aponte, C., Mermoz, S., Bouvet, A., Le Toan, T., and Heurich,
M.: Detection of windthrows and insect outbreaks by L-band SAR: A case study
in the Bavarian Forest National Park, Remote Sens. Environ., 209,
700–711, 2018. a
Thonfeld, F., Gessner, U., Holzwarth, S., Kriese, J., da Ponte, E., Huth, J.,
and Kuenzer, C.: A First Assessment of Canopy Cover Loss in Germany's
Forests after the 2018–2020 Drought Years, Remote Sens., 14, 562,
https://doi.org/10.3390/rs14030562, 2022.
a
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring
vegetation, Remote Sens. Environ., 8, 127–150, 1979. a
Waser, L. T., Rüetschi, M., Psomas, A., Small, D., and Rehush, N.: Mapping
dominant leaf type based on combined Sentinel-1/-2 data–Challenges for
mountainous countries, ISPRS J. Photogramm.,
180, 209–226, 2021. a
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., and White, E.: Individual
tree-crown detection in RGB imagery using semi-supervised deep learning
neural networks, Remote Sensing, 11, 1309,
https://doi.org/10.3390/rs11111309, 2019a.
a,
b
Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., and White, E. P.:
Geographic generalization in airborne RGB deep learning tree detection,
bioRxiv [preprint],
https://doi.org/10.1101/790071, 2019b.
a
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., Singh, A., Graves, S. J.,
and White, E.: A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network,
eLife, 10, e62922,
https://doi.org/10.7554/eLife.62922, 2021.
a,
b
Welle, T., Aschenbrenner, L., Kuonath, K., Kirmaier, S., and Franke, J.:
Mapping Dominant Tree Species of German Forests, Remote Sens., 14, 3330,
https://doi.org/10.3390/rs14143330,
2022.
a
Wurm, M., Stark, T., Zhu, X. X., Weigand, M., and Taubenböck, H.: Semantic
segmentation of slums in satellite images using transfer learning on fully
convolutional neural networks, ISPRS J. Photogramm., 150, 59–69, 2019. a
Xu, K., Zhang, Z., Yu, W., Zhao, P., Yue, J., Deng, Y., and Geng, J.: How
Spatial Resolution Affects Forest Phenology and Tree-Species Classification
Based on Satellite and Up-Scaled Time-Series Images, Remote Sens., 13,
2716,
https://doi.org/10.3390/rs13142716, 2021.
a
Yang, Y. and Newsam, S.: Bag-Of-Visual-Words and Spatial Extensions for
Land-Use Classification, ACM SIGSPATIAL International Conference on Advances
in Geographic Information Systems, 2–5 November 2005, San Jose, California, USA,
https://doi.org/10.1145/1869790.1869829, 2010.
a
Zhang, C., Xia, K., Feng, H., Yang, Y., and Du, X.: Tree species classification
using deep learning and RGB optical images obtained by an unmanned aerial
vehicle, J. Forestry Res., 32, 1879–1888, 2021. a
