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Ökologischer Betrieb
Ökologischer Betrieb
Institute of

BW Farm Economics

Further Publications of Marcel Schwieder

  1. 0

    do Nascimento Bendini H, Garcia Fonseca LM, Schwieder M, Sehn Körting T, Rufin P, Del Arco Sanches I, Leitão PJ, Hostert P (2019) Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series. Int J Appl Earth Observ Geoinf 82:101872, DOI:10.1016/j.jag.2019.05.005

    https://literatur.thuenen.de/digbib_extern/dn062564.pdf

  2. 1

    Leitão PJ, Schwieder M, Pedroni F, Sanchez M, Pinto JRR, Maracahipes L, Bustamante M, Hostert P (2019) Mapping woody plant community turnover with space-borne hyperspectral data - a case study in the Cerrado. Remote Sensing Ecol Conserv 5(1):107-115, DOI:10.1002/rse2.91

    https://literatur.thuenen.de/digbib_extern/dn062568.pdf

  3. 2

    Suess S, van der Linden S, Okujeni A, Griffiths P, Leitão PJ, Schwieder M, Hostert P (2018) Characterizing 32 years of shrub cover dynamics in southern Portugal using annual Landsat composites and machine learning regression modeling. Remote Sens Environ 219:353-364, DOI:10.1016/j.rse.2018.10.004

  4. 3

    Leitão PJ, Schwieder M, Pötzschner F, Pinto JRR, Teixeira AMC, Pedroni F, Sanchez M, Rogass C, van der Linden S, Bustamante M, Hostert P (2018) From sample to pixel: multi-scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes. Ecosphere 9(8):e02298, DOI:10.1002/ecs2.2298

    https://literatur.thuenen.de/digbib_extern/dn062570.pdf

  5. 4

    Schwieder M (2018) Landsat derived land surface phenology metrics for the characterization of natural vegetation in the Brazilian savanna. Berlin: Humboldt-Univ, 129 p, Berlin, Humboldt-Universität, Diss, DOI:10.18452/19368

    https://literatur.thuenen.de/digbib_extern/dn062579.pdf

  6. 5

    Schwieder M, Leitão PJ, Pinto JRR, Teixeira AMC, Pedroni F, Sanchez M, Bustamante M, Hostert P (2018) Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna. Carbon Balance Manag 13:7, DOI:10.1186/s13021-018-0097-1

    https://literatur.thuenen.de/digbib_extern/dn062574.pdf

  7. 6

    Leitão PJ, Schwieder M, Senf C (2017) sgdm: An R package for performing sparse generalized dissimilarity modelling with tools for gdm. Int J Geo Inf 6(1):23, DOI:10.3390/ijgi6010023

    https://literatur.thuenen.de/digbib_extern/dn062566.pdf

  8. 7

    Schwieder M, Leitão PJ, da Cunha Bustamante MM, Ferreira LG, Rabe A, Hostert P (2016) Mapping Brazilian savanna vegetation gradients with Landsat time series. Int J Appl Earth Observ Geoinf 52:361-370, DOI:10.1016/j.jag.2016.06.019

  9. 8

    Leitão PJ, Schwieder M, Suess S, Catry I, Milton EJ, Moreira F, Osborne PE, Pinto MJ, van der Linden S, Hostert P (2015) Mapping beta diversity from space: Sparse Generalised Dissimilarity Modelling (SGDM) for analysing high-dimensional data. Methods Ecol Evol 6(7):764-771, DOI:10.1111/2041-210X.12378

  10. 9

    Leitão PJ, Schwieder M, Suess S, Okujeni A, Galvão LS, van der Linden S, Hostert P (2015) Monitoring natural ecosystem and ecological gradients: Perspectives with EnMAP. Remote Sensing 7(10):13098-13119, DOI:10.3390/rs71013098

    https://literatur.thuenen.de/digbib_extern/dn062567.pdf

  11. 10

    van der Linden S, Rabe A, Held M, Jakimow B, Leitão PJ, Okujeni A, Schwieder M, Suess S, Hostert P (2015) The EnMAP-Box - A toolbox and application programming interface for EnMAP data processing. Remote Sensing 7(9):11249-11266, DOI:10.3390/rs70911249

    https://literatur.thuenen.de/digbib_extern/dn062578.pdf

  12. 11

    Suess S, van der Linden S, Okujeni A, Leitão PJ, Schwieder M, Hostert P (2015) Using class probabilities to map gradual transitions in shrub vegetation from simulated EnMAP data. Remote Sensing 7(8):10668-10688, DOI:10.3390/rs70810668

    https://literatur.thuenen.de/digbib_extern/dn062575.pdf

  13. 12

    Schwieder M, Leitão PJ, Suess S, Senf C, Hostert P (2014) Estimating fractional shrub cover using simulated EnMAP data: A comparison of three machine learning regression techniques. Remote Sensing 6(4):3427-3445, DOI:10.3390/rs6043427

    https://literatur.thuenen.de/digbib_extern/dn062572.pdf

  14. 13

    Buchhorn M, Walker DA, Heim B, Raynolds MK, Epstein HE, Schwieder M (2013) Ground-based hyperspectral characterization of Alaska tundra vegetation along environmental gradients. Remote Sensing 5(8):3971-4005, DOI:10.3390/rs5083971

    https://literatur.thuenen.de/digbib_extern/dn062565.pdf

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