scholarly journals Modeling the View Angle Dependence of Gap Fractions in Forest Canopies: Implications for Mapping Fractional Snow Cover Using Optical Remote Sensing

2008 ◽  
Vol 9 (5) ◽  
pp. 1005-1019 ◽  
Author(s):  
Jicheng Liu ◽  
Curtis E. Woodcock ◽  
Rae A. Melloh ◽  
Robert E. Davis ◽  
Ceretha McKenzie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Land Surface ◽  
Forest Canopy ◽  
Forest Cover ◽  
Airborne Lidar ◽  
Analysis Model ◽  
Forest Canopies ◽  
Fractional Snow Cover ◽  
Go Model

Abstract Forest canopies influence the proportion of the land surface that is visible from above, or the viewable gap fraction (VGF). The VGF limits the amount of information available in satellite data about the land surface, such as snow cover in forests. Efforts to recover fractional snow cover from the spectral mixture analysis model Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG) indicate the importance of view angle effects in forested landscapes. The VGF can be estimated using both hemispherical photos and forest canopy models. For a set of stands in the Cold Land Field Processes Experiment (CLPX) sites in the Fraser Experimental Forest in Colorado, the convergence of both measurements and models of the VGF as a function of view angle supports the idea that VGF can be characterized as a function of forest properties. A simple geometric optical (GO) model that includes only between-crown gaps can capture the basic shape of the VGF as a function of view zenith angle. However, the GO model tends to underestimate the VGF compared with estimates derived from hemispherical photos, particularly at high view angles. The use of a more complicated geometric optical–radiative transfer (GORT) model generally improves estimates of the VGF by taking into account within-crown gaps. Small footprint airborne lidar data are useful for mapping forest cover and height, which makes the parameterization of the GORT model possible over a landscape. Better knowledge of the angular distribution of gaps in forest canopies holds promise for improving remote sensing of snow cover fraction.

scholarly journals Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

2020 ◽  
Vol 12 (16) ◽  
pp. 2559 ◽  
Author(s):  
Yuzhen Zhang ◽  
Shunlin Liang
Keyword(s):  
Aboveground Biomass ◽  
Land Surface ◽  
Forest Canopy ◽  
Forest Cover ◽  
Airborne Lidar ◽  
Estimation Methods ◽  
Area Index ◽  
Temperature And Precipitation ◽  
Error Removal ◽  
Forest Aboveground Biomass

Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.

scholarly journals Patterns and Drivers of Deforestation and Forest Degradation in Myanmar

2021 ◽  
Vol 13 (14) ◽  
pp. 7539
Author(s):  
Zaw Naing Tun ◽  
Paul Dargusch ◽  
DJ McMoran ◽  
Clive McAlpine ◽  
Genia Hill
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Local Governments ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Map Algebra ◽  
Change Detection Analysis ◽  
Detection Analysis

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar

2019 ◽  
Vol 222 ◽  
pp. 34-49 ◽  
Author(s):  
Tihomir S. Kostadinov ◽  
Rina Schumer ◽  
Mark Hausner ◽  
Kat J. Bormann ◽  
Rowan Gaffney ◽  
...  
Keyword(s):  
Snow Cover ◽  
Forest Canopy ◽  
Watershed Scale ◽  
Conifer Forest ◽  
Fractional Snow Cover

scholarly journals A Century of Forest Regrowth and Snow Loss Alters the Cooling Effect of Historical Land Use in the Upper Midwest

Ecosystems ◽  
2019 ◽  
Vol 23 (5) ◽  
pp. 1056-1074
Author(s):  
Bethany J. Blakely ◽  
Adrian V. Rocha ◽  
Jason S. McLachalan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Snow Cover ◽  
Forest Canopy ◽  
Forest Cover ◽  
Cooling Effect ◽  
Forest Regrowth ◽  
Historical Land Use ◽  
Over Time

AbstractAnthropogenic land use affects climate by altering the energy balance of the Earth’s surface. In temperate regions, cooling from increased albedo is a common result of historical land-use change. However, this albedo cooling effect is dependent mainly on the exposure of snow cover following forest canopy removal and may change over time due to simultaneous changes in both land cover and snow cover. In this paper, we combine modern remote sensing data and historical records, incorporating over 100 years of realized land use and climatic change into an empirical assessment of centennial-scale surface forcings in the Upper Midwestern USA. We show that, although increases in surface albedo cooled through strong negative shortwave forcings, those forcings were reduced over time by a combination of forest regrowth and snow-cover loss. Deforestation cooled strongly (− 5.3 Wm−2) and mainly in winter, while composition shift cooled less strongly (− 3.03 Wm−2) and mainly in summer. Combined, changes in albedo due to deforestation, shifts in species composition, and the return of historical forest cover resulted in − 2.81 Wm−2 of regional radiative cooling, 55% less than full deforestation. Forcings due to changing vegetation were further reduced by 0.32 Wm−2 of warming from a shortened snow-covered season and a thinning of seasonal snowpack. Our findings suggest that accounting for long-term changes in land cover and snow cover reduces the estimated cooling impact of deforestation, with implications for long-term land-use planning.

scholarly journals Evaluation of CLASS Snow Simulation over Eastern Canada

2017 ◽  
Vol 18 (5) ◽  
pp. 1205-1225 ◽  
Author(s):  
Diana Verseghy ◽  
Ross Brown ◽  
Libo Wang
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern Canada ◽  
Fractional Snow Cover ◽  
Forest Regions ◽  
Snow Water ◽  
Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

scholarly journals Special Issue on Remote Sensing of Snow and Its Applications

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 277 ◽  
Author(s):  
Ali Nadir Arslan ◽  
Zuhal Akyürek
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Land Surface ◽  
Climate Models ◽  
Weather Forecasting ◽  
Special Issue ◽  
Carbon Cycles ◽  
Wide Range ◽  
Remote Sensing Techniques

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, “Remote Sensing of Snow and Its Applications” in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

scholarly journals The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval

2020 ◽  
Vol 12 (22) ◽  
pp. 3693
Author(s):  
Hongyu Zhao ◽  
Xiaohua Hao ◽  
Jian Wang ◽  
Hongyi Li ◽  
Guanghui Huang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Absolute Error ◽  
Satellite Observation ◽  
Landsat 8 ◽  
Endmember Extraction ◽  
Analysis Model ◽  
Fractional Snow Cover ◽  
Extraction Algorithm ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

A plot-based method for rapid estimation of forest canopy chemistry

2001 ◽  
Vol 31 (3) ◽  
pp. 549-555 ◽  
Author(s):  
Marie-Louise Smith ◽  
Mary E Martin
Keyword(s):  
Remote Sensing ◽  
Rapid Method ◽  
Forest Canopy ◽  
Forest Stand ◽  
Litter Fall ◽  
Forest Canopies ◽  
Rapid Estimation ◽  
Simple Leaf ◽  
Leaf Level ◽  
Fractional Distribution

In this study we present a rapid method to scale the leaf-level chemistry of forest stands to the whole-canopy level. The method combines simple leaf-level measurements of mass and chemistry with a camera-based technique to estimate the fractional distribution of species' foliage area in a forest canopy. Results using this methodology for the estimation of whole-canopy N concentration (g/100 g) are presented and are shown to be comparable with those derived directly from litter fall collection. The ability to efficiently scale leaf-level traits to whole forest canopies enhances our ability to examine key relationships associated with these traits at various levels from the leaf to the forest stand and, with remote sensing technologies, to larger landscapes.

scholarly journals Impact of Surface Albedo Assimilation on Snow Estimation

2020 ◽  
Vol 12 (4) ◽  
pp. 645 ◽  
Author(s):  
Sujay Kumar ◽  
David Mocko ◽  
Carrie Vuyovich ◽  
Christa Peters-Lidard
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Snow Accumulation ◽  
High Plains ◽  
Surface Model ◽  
Fractional Snow Cover ◽  
The Impact

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

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