Spaceborne Lidar for Estimating Forest Biophysical Parameters

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and while this mission primarily serves to capture ice topography measurements of the earth’s surface, it also offers a phenomenal opportunity to estimate biophysical forest parameters at multiple spatial scales. This study served to develop approaches for utilizing ICESat-2 data over vegetated areas. The main objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data and examine the use of simulated PCL metrics for modeling AGB and canopy cover, (2) create wall-to-wall AGB maps at 30-m spatial resolution and characterize AGB uncertainty by using simulated PCL-estimated AGB and predictor variables from Landsat data and derived products, and (3) investigate deep learning (DL) neural networks for producing an AGB product with ICESat-2, using simulated PCL-estimated AGB Landsat imagery, canopy cover and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using existing airborne lidar data and known ICESat-2 track locations for the first two years of the mission. Three scenarios were analyzed; 1) simulated data without the addition of noise, 2) processed simulated data for nighttime and 3) daytime scenarios. AGB model testing with no noise, nighttime and daytime scenarios resulted in R^2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Canopy cover (4.6 m) models achieved R^2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the no noise, nighttime and daytime scenarios respectively. Random Forest (RF) and deep neural network (DNN) models used with predicted AGB estimates and the mapped predictors exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha. Overall, findings from this study suggest the potential of ICESat-2 for estimating AGB and canopy cover and generating a wall-to-wall AGB product by adopting a combinatory approach with spectral metrics derived from Landsat optical imagery, canopy cover and land cover

Spaceborne Lidar for Estimating Forest Biophysical Parameters

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and while this mission primarily serves to capture ice topography measurements of the earth’s surface, it also offers a phenomenal opportunity to estimate biophysical forest parameters at multiple spatial scales. This study served to develop approaches for utilizing ICESat-2 data over vegetated areas. The main objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data and examine the use of simulated PCL metrics for modeling AGB and canopy cover, (2) create wall-to-wall AGB maps at 30-m spatial resolution and characterize AGB uncertainty by using simulated PCL-estimated AGB and predictor variables from Landsat data and derived products, and (3) investigate deep learning (DL) neural networks for producing an AGB product with ICESat-2, using simulated PCL-estimated AGB Landsat imagery, canopy cover and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using existing airborne lidar data and known ICESat-2 track locations for the first two years of the mission. Three scenarios were analyzed; 1) simulated data without the addition of noise, 2) processed simulated data for nighttime and 3) daytime scenarios. AGB model testing with no noise, nighttime and daytime scenarios resulted in R^2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Canopy cover (4.6 m) models achieved R^2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the no noise, nighttime and daytime scenarios respectively. Random Forest (RF) and deep neural network (DNN) models used with predicted AGB estimates and the mapped predictors exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha. Overall, findings from this study suggest the potential of ICESat-2 for estimating AGB and canopy cover and generating a wall-to-wall AGB product by adopting a combinatory approach with spectral metrics derived from Landsat optical imagery, canopy cover and land cover

Spaceborne Lidar for Estimating Forest Biophysical Parameters

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and while this mission primarily serves to capture ice topography measurements of the earth’s surface, it also offers a phenomenal opportunity to estimate biophysical forest parameters at multiple spatial scales. This study served to develop approaches for utilizing ICESat-2 data over vegetated areas. The main objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data and examine the use of simulated PCL metrics for modeling AGB and canopy cover, (2) create wall-to-wall AGB maps at 30-m spatial resolution and characterize AGB uncertainty by using simulated PCL-estimated AGB and predictor variables from Landsat data and derived products, and (3) investigate deep learning (DL) neural networks for producing an AGB product with ICESat-2, using simulated PCL-estimated AGB Landsat imagery, canopy cover and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using existing airborne lidar data and known ICESat-2 track locations for the first two years of the mission. Three scenarios were analyzed; 1) simulated data without the addition of noise, 2) processed simulated data for nighttime and 3) daytime scenarios. AGB model testing with no noise, nighttime and daytime scenarios resulted in R^2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Canopy cover (4.6 m) models achieved R^2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the no noise, nighttime and daytime scenarios respectively. Random Forest (RF) and deep neural network (DNN) models used with predicted AGB estimates and the mapped predictors exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha. Overall, findings from this study suggest the potential of ICESat-2 for estimating AGB and canopy cover and generating a wall-to-wall AGB product by adopting a combinatory approach with spectral metrics derived from Landsat optical imagery, canopy cover and land cover

Spaceborne Lidar for Estimating Forest Biophysical Parameters

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and while this mission primarily serves to capture ice topography measurements of the earth’s surface, it also offers a phenomenal opportunity to estimate biophysical forest parameters at multiple spatial scales. This study served to develop approaches for utilizing ICESat-2 data over vegetated areas. The main objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data and examine the use of simulated PCL metrics for modeling AGB and canopy cover, (2) create wall-to-wall AGB maps at 30-m spatial resolution and characterize AGB uncertainty by using simulated PCL-estimated AGB and predictor variables from Landsat data and derived products, and (3) investigate deep learning (DL) neural networks for producing an AGB product with ICESat-2, using simulated PCL-estimated AGB Landsat imagery, canopy cover and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using existing airborne lidar data and known ICESat-2 track locations for the first two years of the mission. Three scenarios were analyzed; 1) simulated data without the addition of noise, 2) processed simulated data for nighttime and 3) daytime scenarios. AGB model testing with no noise, nighttime and daytime scenarios resulted in R^2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Canopy cover (4.6 m) models achieved R^2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the no noise, nighttime and daytime scenarios respectively. Random Forest (RF) and deep neural network (DNN) models used with predicted AGB estimates and the mapped predictors exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha. Overall, findings from this study suggest the potential of ICESat-2 for estimating AGB and canopy cover and generating a wall-to-wall AGB product by adopting a combinatory approach with spectral metrics derived from Landsat optical imagery, canopy cover and land cover

Woody Biomass Availability for Energy: A Perspective from Non-Industrial Private Forest Landowners in the U.S. Great Lakes States

Non-industrial private forest (NIPF) landowners control 58% of all forests in the U.S. Great Lakes States consisting of Michigan, Minnesota and Wisconsin. A regional assessment of the availability of woody biomass for bioenergy will therefore be incomprehensive without a consideration of supply from the most dominant ownership group. This study aimed to evaluate the social availability of woody biomass for renewable energy in the U.S. Great Lakes States by examining NIPF landowners’ willingness-to-harvest (WTH) their woodlands. Following the Tailored Design Method, surveys were mailed to 4,190 NIPF landowners from Michigan, Minnesota and Wisconsin. Results identified two latent factors summarizing landowners’ bioenergy perceptions: (a) bioenergy support and (b) environmental degradation and four latent factors behind woodland ownership: (a) amenity, (b) personal use, (c) production and (d) legacy. A two-step cluster analysis approach was used to construct a landowner typology for the region based on landowners’ bioenergy views and reasons for woodland ownership. Four types of landowners were consequently identified: recreationist, indifferent, preservationist and multiple-objective. Recreationists were found to own the majority or 51% of the total woodlands reported by sample respondents and were also most willing to harvest their woodlands with an estimated 38% potentially available for timber harvest and 46% for biomass harvest. A comparison of WTH by landowner type and state revealed that the greatest level of acceptance as indicated by potential acreage availability were from recreationists owning NIPFs in Michigan. Binary logit regression models were also used to determine significant factors influencing landowners’ WTH timber and woody biomass. Findings indicated that non-timber objectives decreased the odds of harvesting and timber and biomass prices increased those odds. However, marginal probability effects of prices on WTH highlighted the substantial impact that timber price, rather than biomass price had on landowners’ choice to harvest. These results suggested that the availability of woody biomass will be contingent upon timber prices

Estimating canopy cover from ICESat-2

1041571593NASA ICESat-2 Science Tea

Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama

Airborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. Models from Random Forests and linear regression were competitive with one another; neither approach produced substantially better models. Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. In Random Forests modeling, the most important variables were those derived from lidar. The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. The results of this study show that the addition of multispectral imagery is not substantially beneficial for improving estimates of BA, volume, and AGB in mixed forests and suggests that the investigation of other variables to explain forest variability is necessary

Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama

Airborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. Models from Random Forests and linear regression were competitive with one another; neither approach produced substantially better models. Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. In Random Forests modeling, the most important variables were those derived from lidar. The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. The results of this study show that the addition of multispectral imagery is not substantially beneficial for improving estimates of BA, volume, and AGB in mixed forests and suggests that the investigation of other variables to explain forest variability is necessary

What Drives Land Use Change in the Southern U.S.? A Case Study of Alabama

Land use change reflects fundamental transformations in society. To better understand factors contributing to current land use changes in Alabama, we expand on existing land use studies by employing a generalized least-square method nested in a system of equations for the analysis. We correct for endogeneity issues in our paper by incorporating a control function technique. Using repeated land use data from 1990–2018, we focus on analyzing factors affecting land use changes among timberland, agricultural, urban, and conservation land use types. Our results reveal that land quality factors influence land allocation and land use decisions. We also indicate that population density is a driver for replacing timberland for urban development and agricultural purposes. We show that interest rates are important factors in timberland use decisions as timberland investments are sensitive to capital cost. We provide a basis for future simulations of nationwide land use changes under different economic and policy scenarios, as we offer new insights and contribute to the existing knowledge into public policies that are related to land use planning and management

Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example

National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides rich insights over the Earth’s surface through elevation data collected by its Advanced Topographic Laser Altimeter System (ATLAS) since its launch in September 2018. While this mission is primarily aimed at capturing ice measurements, ICESat-2 also provides data over vegetated areas, offering the capability to gain insights into ecosystem structure and the potential to contribute to the sustainable management of forests. This study involved an examination of the utility of ICESat-2 for estimating forest aboveground biomass (AGB). The objectives of this study were to: (1) investigate the use of canopy metrics for estimating AGB, using data extracted from an ICESat-2 transect over forests in south-east Texas; (2) compare the accuracy for estimating AGB using data from the strong beam and weak beam; and (3) upscale predicted AGB estimates using variables from Landsat multispectral imagery and land cover and canopy cover maps, to generate a 30 m spatial resolution AGB map. Methods previously developed with simulated ICESat-2 data over Sam Houston National Forest (SHNF) in southeast Texas were adapted using actual data from an adjacent ICESat-2 transect over similar vegetation conditions. Custom noise filtering and photon classification algorithms were applied to ICESat-2’s geolocated photon data (ATL03) for one beam pair, consisting of a strong and weak beam, and canopy height estimates were retrieved. Canopy height parameters were extracted from 100 m segments in the along-track direction for estimating AGB, using regression analysis. ICESat-2-derived AGB estimates were then extrapolated to develop a 30 m AGB map for the study area, using vegetation indices from Landsat 8 Operational Land Imager (OLI), National Land Cover Database (NLCD) landcover and canopy cover, with random forests (RF). The AGB estimation models used few canopy parameters and suggest the possibility for applying well-developed methods for modeling AGB with airborne light detection and ranging (lidar) data, using processed ICESat-2 data. The final regression model achieved a R2 and root mean square error (RMSE) value of 0.62 and 24.63 Mg/ha for estimating AGB and RF model evaluation with a separate test set yielded a R2 of 0.58 and RMSE of 23.89 Mg/ha. Findings provide an initial look at the ability of ICESat-2 to estimate AGB and serve as a basis for further upscaling efforts