This study was conducted to explore the effects of topography and land use changes on particulate organic carbon(POC),particulate total nitrogen(PTN),organic carbon(OC) and total nitrogen(TN) associated with different...This study was conducted to explore the effects of topography and land use changes on particulate organic carbon(POC),particulate total nitrogen(PTN),organic carbon(OC) and total nitrogen(TN) associated with different size primary particle fractions in hilly regions of western Iran.Three popular land uses in the selected site including natural forest(NF),disturbed forest(DF) and cultivated land(CL) and three slope gradients(0-10 %,S1,10-30 %,S2,and 30-50%,S3) were employed as the basis of soil sampling.A total of 99 soil samples were taken from the 0-10 cm surface layer in the whole studied hilly region studied.The results showed that the POC in the forest land use in all slope gradients was considerably more than the deforested and cultivated lands and the highest value was observed at NF-S1 treatment with 9.13%.The values of PTN were significantly higher in the forest land use and in the down slopes(0.5%) than in the deforested and cultivated counterparts and steep slopes(0.09%) except for the CL land use.The C:N ratios in POC fraction were around 17-18 in the forest land and around 23 in the cultivated land.In forest land,the silt-associated OC was highest among the primary particles.The enrichment factor of SOC,EC,was the highest for POC.For the primary particles,EC of both primary fractions of silt and clay showed following trend for selected land uses and slope gradients:CL> DF> NF and S3 > S2> S1.Slope gradient of landscape significantly affected the OC and TN contents associated with the silt and clay particles,whereas higher OC and TN contents were observed in lower positions and the lowest value was measured in the steep slopes.Overall,the results showed that native forest land improves soil organic carbon storage and can reduce the carbon emission and soil erosion especially in the mountainous regions with high rainfall in west of Iran.展开更多
Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cyc...Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass 展开更多
文摘This study was conducted to explore the effects of topography and land use changes on particulate organic carbon(POC),particulate total nitrogen(PTN),organic carbon(OC) and total nitrogen(TN) associated with different size primary particle fractions in hilly regions of western Iran.Three popular land uses in the selected site including natural forest(NF),disturbed forest(DF) and cultivated land(CL) and three slope gradients(0-10 %,S1,10-30 %,S2,and 30-50%,S3) were employed as the basis of soil sampling.A total of 99 soil samples were taken from the 0-10 cm surface layer in the whole studied hilly region studied.The results showed that the POC in the forest land use in all slope gradients was considerably more than the deforested and cultivated lands and the highest value was observed at NF-S1 treatment with 9.13%.The values of PTN were significantly higher in the forest land use and in the down slopes(0.5%) than in the deforested and cultivated counterparts and steep slopes(0.09%) except for the CL land use.The C:N ratios in POC fraction were around 17-18 in the forest land and around 23 in the cultivated land.In forest land,the silt-associated OC was highest among the primary particles.The enrichment factor of SOC,EC,was the highest for POC.For the primary particles,EC of both primary fractions of silt and clay showed following trend for selected land uses and slope gradients:CL> DF> NF and S3 > S2> S1.Slope gradient of landscape significantly affected the OC and TN contents associated with the silt and clay particles,whereas higher OC and TN contents were observed in lower positions and the lowest value was measured in the steep slopes.Overall,the results showed that native forest land improves soil organic carbon storage and can reduce the carbon emission and soil erosion especially in the mountainous regions with high rainfall in west of Iran.
基金supported by the State Forestry Administration of China under the national forestry commonwealth project grant#201404309the Expert Workstation of Academician Tang Shouzheng of Yunnan Province,the Yunnan provincial key project of Forestrythe Research Center of Kunming Forestry Information Engineering Technology
文摘Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass
