@Article{SilveiraEWACMMSTCS:2019:PrMoPl,
               author = "Silveira, Eduarda M. O. and Esp{\'{\i}}rito Santos, Fernando D. 
                         and Wulder, Michael A. and Acerbi J{\'u}nior, Fausto W. and 
                         Carvalho, M{\^o}nica C. and Mello, Carlos R. and Mello, Jos{\'e} 
                         M{\'a}rcio and Shimabukuro, Yosio Edemir and Terra, Marcela 
                         Castro Nunes Santos and Carvalho, Luis Marcelo T. and Scolforo, 
                         Jos{\'e} R. S.",
          affiliation = "{Universidade Federal de Lavras (UFLA)} and {University of 
                         Leicester} and {Canadian Forest Service (Pacific Forestry Centre)} 
                         and {Universidade Federal de Lavras (UFLA)} and {Universidade 
                         Federal de Lavras (UFLA)} and {Universidade Federal de Lavras 
                         (UFLA)} and {Universidade Federal de Lavras (UFLA)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Lavras (UFLA)} and {Universidade Federal de Lavras (UFLA)} and 
                         {Universidade Federal de Lavras (UFLA)}",
                title = "Pre-stratified modelling plus residuals kriging reduces the 
                         uncertainty of aboveground biomass estimation and spatial 
                         distribution in heterogeneous savannas and forest environments",
              journal = "Forest Ecology and Management",
                 year = "2019",
               volume = "445",
                pages = "96--109",
                month = "Aug.",
             keywords = "AGB, Random forests, Brazilian biomes, Climate Seasonality.",
             abstract = "Mapping aboveground biomass (AGB)is a challenge in heterogeneous 
                         environments, such as the Brazilian savannas and tropical forests 
                         located in Minas Gerais state (MG), Brazil. The factors linked to 
                         AGB stocks vary in climate, soil characteristics, and stand-level 
                         structural attributes over short distances, making generalization 
                         of AGB difficult over regional-scales. We offer the hypothesis 
                         that stratification into vegetation types at the plot level plus a 
                         regression kriging technique, can reduce the variability of 
                         factors controlling AGB, helping to select the appropriate 
                         predictor variables and result in an ability to produce reliable 
                         models and maps. To do so, we incorporate remotely sensed data 
                         (Landsat and MODerate resolution Imaging Spectroradiometer-MODIS), 
                         spatio-environmental variables, and forest inventory data to 
                         develop spatial-explicit maps of AGB across three important 
                         Brazilian biomes (savanna, Atlantic forest, and semi-arid 
                         woodland). We modelled and predicted the spatial distribution of 
                         AGB of six individual vegetation types of savanna-forest biomes 
                         (shrub savanna, woodland savanna, densely wooded savanna, 
                         deciduous forest, semi-deciduous forest and rain forest), 
                         utilizing a random forests (RF)algorithm plus residual kriging, 
                         selecting the lowest number of variables that offer the best 
                         predictive performance. The stratified models notably improved the 
                         AGB prediction by reducing the mean absolute error MAE (%)and the 
                         root-mean-square error RMSE (Mg/ha)for all vegetation types, 
                         mainly for shrub savanna (MAE reduced from 82.69 to 54.73%). The 
                         AGB spatial distribution is governed mainly by precipitation and 
                         seasonality. The south and east of MG presented high values of AGB 
                         due to the predominance of semi-deciduous trees and rain forest 
                         conditions within Atlantic forest biome (total of 491,456,607 Mg), 
                         with a higher amount rain over the year, lower temperatures, and 
                         lower precipitation seasonality. Rain forests have the largest 
                         mean AGB per area (157.71 Mg/ha)while semi-deciduous forests hold 
                         the largest AGB stocks in the state (583,176,472 Mg). Shrub 
                         savannas, located in the central, northwest and north regions of 
                         MG (lower amount of rain, higher temperatures and strong 
                         seasonality), accounted the lowest amount of AGB in both total AGB 
                         (27,906,281 Mg)and AGB per area (18.80 Mg/ha). Our study 
                         demonstrates that stratification can reduce variability and 
                         improve estimates by developing individual models and selecting 
                         optimal predictor variables dependent on the characteristics of 
                         specific vegetation types. The methods demonstrated and the 
                         resultant maps and estimates improve the quality of regional 
                         biomass estimates needed to understand and mitigate climate 
                         change, enabling researchers to refine estimates of greenhouse gas 
                         emissions.",
                  doi = "10.1016/j.foreco.2019.05.016",
                  url = "http://dx.doi.org/10.1016/j.foreco.2019.05.016",
                 issn = "0378-1127",
             language = "en",
           targetfile = "Silveira1-s2.0-S0378112719301185-main.pdf",
        urlaccessdate = "28 abr. 2024"
}