scholarly journals ACCURACY ASSESSMENT OF LANDSAT-DERIVED CONTINUOUS FIELDS OF TREE COVER PRODUCTS USING AIRBORNE LIDAR DATA IN THE EASTERN UNITED STATES

2015 ◽  
Vol XL-7/W4 ◽  
pp. 241-246 ◽  
Author(s):  
X. P. Song ◽  
H. Tang
Keyword(s):  
United States ◽  
Land Cover ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Tree Cover ◽  
Mean Bias Error ◽  
Bias Error ◽  
Lidar Data ◽  
Eastern United States ◽  
Continuous Fields

Knowing the detailed error structure of a land cover map is crucial for area estimation. Facilitated by the opening of the Landsat archive, global land cover mapping at 30-m resolution has become possible in recent years. Two global Landsat-based continuous fields of tree cover maps have been generated by Sexton et al. (2013) and Hansen et al. (2013) but the accuracy of which have not been comprehensively evaluated. Here we used canopy cover derived from airborne small-footprint Lidar data as a reference to evaluate the accuracy of these two datasets as well as the National Land Cover Database 2001 canopy cover layer (Homer et al. 2004) in two entire counties in Maryland, United States. Our results showed that all three Landsat datasets captured well the spatial variations of tree cover in the study area with an <i>r</i><sup>2</sup> ranging between 0.54 and 0.58, a mean bias error ranging between -15% and 5% tree cover, and a root mean square error ranging between 27% and 29% tree cover. When the continuous tree cover maps were converted to binary forest/nonforest maps, all three products were proved to have an overall accuracy >= 80% but with significant differences in producer’s accuracy and user’s accuracy. Data users are thus suggested to beware of these accuracy patterns when selecting the most appropriate dataset for their specific applications.

scholarly journals CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

2021 ◽  
Vol 13 (15) ◽  
pp. 2996
Author(s):  
Qinwei Zhang ◽  
Mingqi Li ◽  
Maohua Wang ◽  
Arthur Paul Mizzi ◽  
Yongjian Huang ◽  
...  
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Co2 Flux ◽  
Total Carbon ◽  
Mean Bias Error ◽  
Bias Error ◽  
Parallel Experiment ◽  
Concentration Measurements ◽  
Global Stocktake

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

scholarly journals Estimation of crown and canopy cover from airborne lidar data

2010 ◽  
Vol 52 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Mait Lang
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Stand Structure ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Silver Birch ◽  
Lidar Data ◽  
Crown Cover ◽  
Pine Trees ◽  
Airborne Lidar Data

Metsa katvuse ja liituse hindamine lennukilt laserskanneriga Tests were carried out in mature Scots pine, Norway spruce and Silver birch stands at Järvselja, Estonia, to estimate canopy cover (K) and crown cover (L) from airborne lidar data. Independent estimates Kc and Lc for K and L were calculated from the Cajanus tube readings made on the ground at 1.3 m height. Lidar data based cover estimates depended on the inclusion of different order returns significantly. In all the stands first order return based estimate K1 was biased positively (3-10%) at the reference height of 1.3 m compared to ground measurements. All lidar based estimates decreased with increasing the reference height. Single return (Ky) and all return (Kk) based canopy cover estimates depended more on the sand structure compared to K1. The ratio of all return count to the first return count D behaved like crown cover estimate in all stands. However, in spruce stand D understimated Lc significantly. In the Scots pine stand K1(1.3) = 0.7431 was most similar canopy cover estimate relative to the ground estimate Kc = 0,7362 whereas Ky(1.3) and Kk(1.3) gave significant underestimates (>15%) of K. Caused by the simple structure of Scots pine stand - only one layer pine trees, the Cajanus tube based canopy cover (Kc), crown cover (Lc) and lidar data based canopy density D(1.3) values were rather similar. In the Norway spruce stand and in the Silver birch stand second layer and regeneration trees were present. In the Silver birch stand Kk(1.3) and Ky(1.3) estimated Kc rather well. In the Norway spruce stand Ky(1.3) and K1(1.3) were the best estimators of Kc whereas Kk(1.3) underestimated canopy cover. Lidar data were found to be usable for canopy cover and crown cover assessment but the selection of the estimator is not trivial and depends on the stand structure.

Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project

2010 ◽  
Vol 2 (4) ◽  
pp. 279-288 ◽  
Author(s):  
Matthew C. Hansen ◽  
Alexey Egorov ◽  
David P. Roy ◽  
Peter Potapov ◽  
Junchang Ju ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Landsat Data ◽  
First Results ◽  
Continuous Fields ◽  
The Web

Thematic accuracy of MRLC land cover for the eastern United States

2001 ◽  
Vol 76 (3) ◽  
pp. 418-422 ◽  
Author(s):  
Limin Yang ◽  
Stephen V Stehman ◽  
Jonathan H Smith ◽  
James D Wickham
Keyword(s):  
United States ◽  
Land Cover ◽  
Eastern United States ◽  
Thematic Accuracy

Land use and land cover in critical source areas on small dairy farms in the eastern United States

2015 ◽  
Vol 70 (4) ◽  
pp. 257-266 ◽  
Author(s):  
N. R. Jordan ◽  
K. M. Clower ◽  
S. M. Manson ◽  
D. B. Bonsal ◽  
J. L. Immich
Keyword(s):  
United States ◽  
Land Use ◽  
Land Cover ◽  
Dairy Farms ◽  
Eastern United States ◽  
Critical Source Areas ◽  
Source Areas

An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction

2018 ◽  
Vol 33 (1) ◽  
pp. 301-315 ◽  
Author(s):  
Wesley G. Page ◽  
Natalie S. Wagenbrenner ◽  
Bret W. Butler ◽  
Jason M. Forthofer ◽  
Chris Gibson
Keyword(s):  
United States ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Wildland Fire ◽  
Fire Behavior ◽  
The United States ◽  
Mean Bias Error ◽  
Bias Error ◽  
Behavior Prediction

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.

scholarly journals ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM) WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

2015 ◽  
Vol II-2/W2 ◽  
pp. 183-189 ◽  
Author(s):  
M. R. M. Salleh ◽  
Z. Ismail ◽  
M. Z. A. Rahman
Keyword(s):  
Tropical Forest ◽  
Digital Terrain Model ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Terrain Slope ◽  
Airborne Lidar Data

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

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