scholarly journals Seasonal patterns of tropical forest leaf area index and CO2exchange

2008 ◽  
Vol 113 (G1) ◽  
pp. n/a-n/a ◽  
Author(s):  
Christopher E. Doughty ◽  
Michael L. Goulden
Keyword(s):  
Tropical Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Patterns ◽  
Area Index

scholarly journals Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia

2021 ◽  
Author(s):  
Yonghua Qu ◽  
Ahmed Shaker ◽  
Carlos Alberto Silva ◽  
Carine Klauberg ◽  
Ekena Rangel Pinagé
Keyword(s):  
Tropical Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Tropical Forests ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Area Index ◽  
Dense Forest ◽  
Modis Lai ◽  
Airborne Lidar Data

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

scholarly journals Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia

2021 ◽  
Author(s):  
Yonghua Qu ◽  
Ahmed Shaker ◽  
Carlos Alberto Silva ◽  
Carine Klauberg ◽  
Ekena Rangel Pinagé
Keyword(s):  
Tropical Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Tropical Forests ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Area Index ◽  
Dense Forest ◽  
Modis Lai ◽  
Airborne Lidar Data

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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