How To Predict Soil Carbon Stock

Size: px

Start display at page:

Download "How To Predict Soil Carbon Stock"

Maurice Turner
3 years ago
Views:

1 CASO DE ESTUDIO SOBRE PREDICCION DE PROPIEDADES DE SUELOS A TRAVÉS DE PUNTOS: El Carbono Organico en los Suelos de Rio de Janeiro, Brasil M.L. Mendonça Santos, Dart, R., Rizzato, M. Embrapa Solos

2 Objectivos y Procedimientos Procedures of digital mapping were used to predict the stock of organic carbon of the topsoil (0-10cm) in the State of Rio de Janeiro. For that purpose, a multilinear analysis was used as predictive model and some environmental variables as predictors. Seven different models were built and statistically compared. The best model was applied to the digital mapping of the soil carbon stock Pedotransfer functions (PTFs) were estimated to bulk density (from granulometry and O.C.), in order to calculate soil carbon stock. SCORPAN model was used with following environmental correlates as predictor variables: satellite data, lithology, DEM and its derivatives, and and polygon soil map.

3 The study area RJ State km2 BD_RJ : 731 perfis 431 soil perfis with X,Y, Just a few data on bulk density Irregular spatial distribution of soil profiles

4 Dados de Solos (Descr. Perfis, Laboratório, sensores) S c,p = f (s.c.o.r.p.a.n.) + e Dados Auxiliares (Imagens, Uso Atual, MNT e Derivadas, Litologia, Geomorfologia...) SINFERS (modelagem e predição espacial de variáveis de solo) PTFs DSM de Propiedades do Solo (ph, textura, Carbono, N,P,K,...) Modelling DSM de Classes de Solo (tipos de solo, classe de textura, cor... McBratney, Mendonça-Santos & Minasny, (2003). On Digital Soil Mapping. Geoderma,117:3-52. Mendonça-Santos et al., (2007). In: Digital Soil Mapping with limited data. Springer

5 S Soil Map C Climate (temperature...) Satellite Images O LULC Maps NDVI, Biomassa DEM + Derivates Altitude Aspect R Slope Profile Curvature CTI P Lithology Covariates S c,p = f (s.c.o.r.p.a.n.) + e A Age (pedogenesis) N Spatial Location (X,Y) f Linear Regression Generalized Linear Models Classification and Regression Trees ANN Fuzzy Logic Knowledge Expert

6 BD Perfiles de Suelos

7 BD Dados ambientales auxiliares

8 PTF of bulk density from granulometry and Organic Carbon Mendonça-Santos et al. 2007

9 PTF da densidade dos solos_rj a partir da granulometria e do Carbono Orgânico lise preliminar dos dados UFRRJ, 24/11/2006

10 Calculating SOC stock StockOC = C* d * p C = content of SOC (g/kg) d = soil bulk density (g/cm 3 ) p = thickness (cm)

11 The Models Built Predictive models s.c.o.r.p.a.n.. built to estimate the soil carbon stock in topsoil (0-10cm). Models Predictors Variables SCORPAN model Stepwise Number of Soil Profiles M1 R (ELEV, ASPECT, PLAN, PROF, QWETI, SLOPE) 429 M2 M3 M4 M5 M6 M7 R (ELEV, ASPECT, PLAN, PROF, QWETI, SLOPE) O (Landsat ETM + -B7, B4, B2 e NDVI), R (ELEV, ASPECT, PLAN, PROF, QWETI, SLOPE) O (Landsat ETM + -B7, B4, B2 e NDVI), R (ELEV, ASPECT, PLAN, PROF, QWETI, SLOPE) O (Landsat ETM + -B7, B4, B2 e NDVI), R (ELEV, ASPECT, PLAN, PROF, QWETI, SLOPE), P (Litology Map vector format) S (Soil Map - polygon), O (Landsat ETM + -B7, B4, B2 e NDVI), R (ELEV, ASPECT, PLAN, PROF, QWETI, SLOPE), P ( Litology Map vector format) O (Landsat ETM + -B7, B4, B2, NDVI and LULC Map), R (ELEV, ASPECT, PLAN, PROF, QWETI, SLOPE), P ( Litology Map vector format ) ELEV, ASPECT, PLAN, QWETI, SLOPE 429 all 427 B7, B4, ELEV, ASPECT, PLAN, QWETI, SLOPE 429 all 427 all 427 all 427

12 Modelos Preditores stepwise Rsquare RMSE NP Obs AIC S (Mapa de solos - em formato vetorial), O (Landsat ETM -B7, B4, B2 e NDVI), M6 R (ELEV, ASPECT, PLAN, 0,34 11, PROF, QWETI, SLOPE), P (Mapa de litologia - em formato vetorial) S (Mapa de solos - em formato vetorial), O (Landsat ETM -B7, B4, B2 e NDVI), 1/4 (M6) R (ELEV, ASPECT, PLAN, 0,4 13, ,83 PROF, QWETI, SLOPE), P (Mapa de litologia - em formato vetorial) Model validation S= f (SCORPAN) * B * B * B * NDVI * ELEV * Aspect * PLAN * PROF * QWETI * SLOPE * Cambissolo * Latossolo * Argissolo * Planossolo * Espodossolo * Gleissolo * Chernossolo * Organossolo * Neossolo * Migmatitos * Granitoide * Gnaisses * Sedimentos Holocenicos * Granitos * Sedimentos Terciarios * Rochas Alcalinas * Granulitos * Quartzitos * Marmores * B * B * B * NDVI * ELEV * Aspect * PLAN * PROF * QWETI * SLOPE * Cambissolo * Latossolo * Argissolo * Planossolo * Espodossolo * Gleissolo * Neossolo * Migmatitos * Granitoide * Gnaisses * Sedimentos Holocenicos * Granitos * Sedimentos Terciarios * Rochas Alcalinas * Granulitos * Quartzitos S (Mapa de solos - em formato vetorial), O (Landsat ETM -B7, B4, B2 e NDVI), 3/4 (M6) R (ELEV, ASPECT, PLAN, 0,36 11, ,67 PROF, QWETI, SLOPE), P (Mapa de litologia - em formato vetorial) * B * B * B * NDVI * ELEV * Aspect * PLAN * PROF * QWETI * SLOPE * Cambissolo * Latossolo * Argissolo * Planossolo * Espodossolo * Gleissolo * Chernossolo * Organossolo * Neossolo * Migmatitos * Granitoide * Gnaisses * Sedimentos Holocenicos * Granitos * Sedimentos Terciarios * Rochas Alcalinas * Granulitos * Quartzitos * Marmores

13 Comparing the Models Models RMSE Number of AIC arameters M1 14, ,603 M2 14, ,604 M3 14, ,976 M4 14, ,386 M5 13, ,548 M6 11, ,578 M7 12, ,807

14 Mapping SOC topsoil with Model 6 A) result of the predictive s.c.o.r.p.a.n. soil-landscape landscape modelling (multilinear( regression) B) krigged residues C) Predicted + Krigged residuals (regression krigging)

15 The map of the soil organic carbon of Rio de Janeiro State (0-10 cm) with environmental features

16 Vertical distribution of SOC in Soil Profile 0 Teor de Carbono nos perfis 1 e 2 0 Teor de Carbono nos perfis 11 e Profundidade cm Profundidade cm Teor de Carbono g/kg Coluna A Coluna A Teor de Carbono g/kg Coluna A Coluna A ORGANOSSOLO LATOSSOLO VA

17 Conclusions The whole process of DSM has been demonstrated, using the soil formation factors as predictor variables in order to build the models; Seven predictive models were tested and have not shown great variations. However, the best result for carbon stock was performed by model 6 (M6), that presented the smallest AIC and RMSE. This model encompasses information on existing polygon soil map, satellite images, DEM and its derivates and lithology map; The spatial distribution of soil organic carbon is correlated with with the different geo-environments of the study area, e.g. the highest stocks of organic carbon is found in the lowlands areas. Soil profiles has been described in each zones, where OC vertical distribution has been studied...depth Functions

19 Nome do Apresentador nome do apresentador nome do apresentador Instituição a que pertence instituição a que pertence instituição a que pertence @ . telefones

Australian Soil Resources Information System (ASRIS)

Australian Soil Resources Information System (ASRIS) 2001 ASRIS Collated CSIRO and State soils data into a single nation-wide Oracle database Collated digital soil, land systems, and lithology maps Produced