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Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods.
Published in:
Remote Sensing, 2022, v. 14, n. 16, p. 3912, doi. 10.3390/rs14163912
By:
- Revenga, Jaime C.;
- Trepekli, Katerina;
- Oehmcke, Stefan;
- Jensen, Rasmus;
- Li, Lei;
- Igel, Christian;
- Gieseke, Fabian Cristian;
- Friborg, Thomas
Publication type:
Article
Scattered tree death contributes to substantial forest loss in California.
Published in:
Nature Communications, 2024, v. 15, n. 1, p. 1, doi. 10.1038/s41467-024-44991-z
By:
- Cheng, Yan;
- Oehmcke, Stefan;
- Brandt, Martin;
- Rosenthal, Lisa;
- Das, Adrian;
- Vrieling, Anton;
- Saatchi, Sassan;
- Wagner, Fabien;
- Mugabowindekwe, Maurice;
- Verbruggen, Wim;
- Beier, Claus;
- Horion, Stéphanie
Publication type:
Article
More than one quarter of Africa's tree cover is found outside areas previously classified as forest.
Published in:
Nature Communications, 2023, v. 14, n. 1, p. 1, doi. 10.1038/s41467-023-37880-4
By:
- Reiner, Florian;
- Brandt, Martin;
- Tong, Xiaoye;
- Skole, David;
- Kariryaa, Ankit;
- Ciais, Philippe;
- Davies, Andrew;
- Hiernaux, Pierre;
- Chave, Jérôme;
- Mugabowindekwe, Maurice;
- Igel, Christian;
- Oehmcke, Stefan;
- Gieseke, Fabian;
- Li, Sizhuo;
- Liu, Siyu;
- Saatchi, Sassan;
- Boucher, Peter;
- Singh, Jenia;
- Taugourdeau, Simon;
- Dendoncker, Morgane
Publication type:
Article
Both sides of the story: comparing student-level data on reading performance from administrative registers to application generated data from a reading app.
Published in:
EPJ Data Science, 2021, v. 10, n. 1, p. 1, doi. 10.1140/epjds/s13688-021-00300-y
By:
- Sortkær, Bent;
- Smith, Emil;
- Reimer, David;
- Oehmcke, Stefan;
- Andersen, Ida Gran
Publication type:
Article

Found: 4

Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods.

Scattered tree death contributes to substantial forest loss in California.

More than one quarter of Africa's tree cover is found outside areas previously classified as forest.

Both sides of the story: comparing student-level data on reading performance from administrative registers to application generated data from a reading app.