Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Records 1 - 7 / 7

  • help
  • print

    Print search results

  • export

    Export search results

  • alert
    We will mail you new results for this query: q=Saatchi
Check title to add to marked list
The global forest age dataset and its uncertainties (GFADv1.1)
Poulter, B. ; Aragão, L. ; Andela, N. ; Bellassen, V. ; Ciais, P. ; Kato, T. ; Lin, X. ; Nachin, B. ; Luyssaert, S. ; Pederson, N. ; Peylin, P. ; Piao, S. ; Pugh, T. ; Saatchi, S. ; Schepaschenko, D. ; Schelhaas, M. ; Shivdenko, A. - \ 2019
The global forest age dataset (GFAD v.1.1) provides a correction to GFAD v1.0, as well as its uncertainties. GFAD describes the age distributions of plant functional types (PFT) on a 0.5-degree grid. Each grid cell contains information on the fraction of each PFT within an age class. The four PFTs, needleaf evergreen (NEEV), needleleaf deciduous (NEDE), broadleaf evergreen (BREV) and broadleaf deciduous (BRDC) are mapped from the MODIS Collection 5.1 land cover dataset, crosswalking land cover types to PFT fractions. The source of data for the age distributions is from country-level forest inventory for temperate and high-latitude countries, and from biomass for tropical countries. The inventory and biomass data are related to fifteen age classes defined in ten-year intervals, from 1-10 up to a class greater than 150 years old. The uncertainties are estimated for the inventory derived forest age classes as +/- 40% of the mean age. For the areas where age is derived from aboveground biomass, the uncertainty is derived from the 5th and 95th percentile estimates of biomass, but using the same age-aboveground biomass curves. The GFAD dataset represents the 2000-2010 era.
Using a Finer Resolution Biomass Map to Assess the Accuracy of a Regional, Map-Based Estimate of Forest Biomass
McRoberts, Ronald E. ; Næsset, Erik ; Liknes, Greg C. ; Chen, Qi ; Walters, Brian F. ; Saatchi, Sassan ; Herold, Martin - \ 2019
Surveys in Geophysics (2019). - ISSN 0169-3298
Design-based inference - Greenhouse gas inventory - Hybrid inference - IPCC good practice guidelines - Model-based inference

National greenhouse gas inventories often use variations of the gain–loss approach whereby emissions are estimated as the products of estimates of areas of land-use change characterized as activity data and estimates of emissions per unit area characterized as emission factors. Although the term emissions is often intuitively understood to mean release of greenhouse gases from terrestrial sources to the atmosphere, in fact, emission factors can also be negative, meaning removal of the gases from the atmosphere to terrestrial sinks. For remote and inaccessible forests for which ground sampling is difficult if not impossible, emission factors may be based on map-based estimates of biomass or biomass change obtained from regional maps. For the special case of complete deforestation, the emission factor for the aboveground biomass pool is simply mean aboveground, live-tree, biomass per unit area prior to the deforestation. If biomass maps are used for these purposes, estimates must still comply with the first IPCC good practice guideline regarding accuracy relative to the true value and the second guideline regarding uncertainty. Accuracy assessment for a map-based estimate entails comparison of the estimate to a second estimate obtained using independent reference data. Assuming ground sampling is not feasible, a map of greater quality than the regional map may be considered as a source of reference data where greater quality connotes attributes such as finer resolution and/or greater accuracy. For a local, sub-regional study area in Minnesota in the USA, the accuracy of an estimate of mean aboveground, live-tree biomass per unit area (AGB, Mg/ha) obtained from a coarser resolution, regional, MODIS-based biomass map was assessed using reference data sampled from a finer resolution, local, airborne laser scanning (ALS)-based biomass map. The rationale for a local assessment of a regional map is that, although assessment of a regional map would be difficult for the entire extent of the map, it can likely be assessed for multiple local sub-regions in which case expected local regional accuracy for the entire map can perhaps be inferred. For this study, the local assessment was in the form of a test of the hypothesis that the local sub-regional estimate from the regional map did not deviate from the local true value. A hybrid approach to inference was used whereby design-based inferential techniques were used to estimate uncertainty due to sampling from the finer resolution map, and model-based inferential techniques were used to estimate uncertainty resulting from using the finer resolution map unit values which were subject to prediction error as reference data. The test revealed no statistically significant difference between the MODIS-based and ALS-based map estimates, thereby indicating that for the local sub-region, the regional, MODIS-based estimate complied with the first IPCC good practice guideline for accuracy.

The global forest age dataset (GFADv1.0), link to NetCDF file
Poulter, B. ; Aragão, L. ; Andela, N. ; Bellassen, V. ; Ciais, P. ; Kato, T. ; Lin, X. ; Nachin, B. ; Luyssaert, S. ; Pederson, N. ; Peylin, P. ; Piao, S. ; Saatchi, S. ; Schepaschenko, D. ; Schelhaas, M. ; Shivdenko, A. - \ 2018
The global forest age dataset (GFAD) describes the age distributions of plant functional types (PFT) on a 0.5-degree grid. Each grid cell contains information on the fraction of each PFT within an age class. The four PFTs, needleaf evergreen (NEEV), needleleaf deciduous (NEDE), broadleaf evergreen (BREV) and broadleaf deciduous (BRDC) are mapped from the MODIS Collection 5.1 land cover dataset, crosswalking land cover types to PFT fractions. The source of data for the age distributions is from country-level forest inventory for temperate and high-latitude countries, and from biomass for tropical countries. The inventory and biomass data are related to fifteen age classes defined in ten-year intervals, from 1-10 up to a class greater than 150 years old. The GFAD dataset represents the 2000-2010 era.
Carbon storage potential in degraded forests of Kalimantan, Indonesia
Ferraz, António ; Saatchi, Sassan ; Xu, Liang ; Hagen, Stephen ; Chave, Jerome ; Yu, Yifan ; Meyer, Victoria ; Garcia, Mariano ; Silva, Carlos ; Roswintiart, Orbita ; Samboko, Ari ; Sist, Plinio ; Walker, Sarah ; Pearson, Timothy R.H. ; Wijaya, Arief ; Sullivan, Franklin B. ; Rutishauser, Ervan ; Hoekman, Dirk ; Ganguly, Sangram - \ 2018
Environmental Research Letters 13 (2018)9. - ISSN 1748-9318
aboveground biomass mapping - airborne lidar - carbon - forest degradation - Indonesia - Kalimantan - peat swamp forests

The forests of Kalimantan are under severe pressure from extensive land use activities dominated by logging, palm oil plantations, and peatland fires. To implement the forest moratorium for mitigating greenhouse gas emissions, Indonesia's government requires information on the carbon stored in forests, including intact, degraded, secondary, and peat swamp forests. We developed a hybrid approach of producing a wall-to-wall map of the aboveground biomass (AGB) of intact and degraded forests of Kalimantan at 1 ha grid cells by combining field inventory plots, airborne lidar samples, and satellite radar and optical imagery. More than 110 000 ha of lidar data were acquired to systematically capture variations of forest structure and more than 104 field plots to develop lidar-biomass models. The lidar measurements were converted into biomass using models developed for 66 439 ha of drylands and 44 250 ha of wetland forests. By combining the AGB map with the national land cover map, we found that 22.3 Mha (106 ha) of forest remain on drylands ranging in biomass from 357.2 ±12.3 Mgha-1 in relatively intact forests to 134.2 ±6.1 Mgha-1 in severely degraded forests. The remaining peat swamp forests are heterogeneous in coverage and degradation level, extending over 3.62 Mha and having an average AGB of 211.8 ±12.7 Mgha-1. Emission factors calculated from aboveground biomass only suggest that the carbon storage potential of more than 15 Mha of degraded and secondary dryland forests will be about 1.1 PgC.

Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations
Li, Wei ; Ciais, Philippe ; Peng, Shushi ; Yue, Chao ; Wang, Yilong ; Thurner, Martin ; Saatchi, Sassan S. ; Arneth, Almut ; Avitabile, Valerio ; Carvalhais, Nuno ; Harper, Anna B. ; Kato, Etsushi ; Koven, Charles ; Liu, Yi Y. ; Nabel, Julia E.M.S. ; Pan, Yude ; Pongratz, Julia ; Poulter, Benjamin ; Pugh, Thomas A.M. ; Santoro, Maurizio ; Sitch, Stephen ; Stocker, Benjamin D. ; Viovy, Nicolas ; Wiltshire, Andy ; Yousefpour, Rasoul ; Zaehle, Sönke - \ 2017
Biogeosciences 14 (2017)22. - ISSN 1726-4170 - p. 5053 - 5067.
The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.
An integrated pan-tropical biomass map using multiple reference datasets
Avitabile, V. ; Herold, M. ; Heuvelink, G.B.M. ; Lewis, S.L. ; Phillips, O.L. ; Asner, G.P. ; Armston, J. ; Asthon, P. ; Banin, L.F. ; Bayol, N. ; Berry, N. ; Boeckx, P. ; Jong, B. De; Devries, B. ; Girardin, C. ; Kearsley, E. ; Lindsell, J.A. ; Lopez-gonzalez, G. ; Lucas, R. ; Malhi, Y. ; Morel, A. ; Mitchard, E. ; Nagy, L. ; Qie, L. ; Quinones, M. ; Ryan, C.M. ; Slik, F. ; Sunderland, T. ; Vaglio Laurin, G. ; Valentini, R. ; Verbeeck, H. ; Wijaya, A. ; Willcock, S. - \ 2016
Global Change Biology 22 (2016)4. - ISSN 1354-1013 - p. 1406 - 1420.
We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.
Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model
Parmentier, I. ; Harrigan, R. ; Buermann, W. ; Mitchard, E.T.A. ; Saatchi, S. ; Malhi, Y. ; Bongers, F. ; Hawthorne, W.D. ; Leal, M.E. ; Lewis, S. ; Nusbaumer, L. ; Sheil, D. ; Sosef, M.S.M. ; Bakayoko, A. ; Chuyong, G. ; Chatelain, C. ; Comiskey, J. ; Dauby, G. ; Doucet, J.L. ; Hardy, O. - \ 2011
Journal of Biogeography 38 (2011)6. - ISSN 0305-0270 - p. 1164 - 1176.
tropical tree diversity - species richness - plant diversity - geographical ecology - red herrings - autocorrelation - patterns - amazon - scale - dynamics
Aim Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Location Tropical rain forest, West Africa and Atlantic Central Africa. Methods Alpha diversity estimates were compiled for trees with diameter at breast height = 10 cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone. Results The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances > 50 km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests. Main conclusions The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity.
Check title to add to marked list

Show 20 50 100 records per page

 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.