12 research outputs found
Biophysical evaluation of five land covers for land-climate interaction modeling in East Africa
No full text
Developing land use/land cover parameterization for climate-land modeling in East Africa
No full textRegional climate modeling studies now have numerous choices in selecting land use/land cover (LULC) products to provide land surface parameter information. The various LULC products were developed with different objectives, methods and data sources. Not all new LULC products have land classes that match the land class types defined in climate models. More importantly, when used in regional climate models, simulation results can vary significantly depending on the LULC products. Thus, developing appropriate LULC parameterization for climate models becomes critical depending on objectives and efforts. The objective of this paper is to develop the most accurate LULC scheme possible for East Africa for implementation in the Regional Atmospheric Modeling System (RAMS). A crosswalk procedure, based on assessments of various LULC products, was performed connecting land class types in RAMS and the newly created LULC scheme. No simulations are discussed here; rather, we present an outline of the procedures that were carried out to take advantage of the strengths of currently available LULC products, Africover and Global Land Cover 2000, for the purpose of conducting regional climate simulations
An assessment of africover and GLC2000 using general agreement and videography techniques
No full textPerformance Evaluation of UAVSAR and Simulated NISAR Data for Crop/Noncrop Classification Over Stoneville, MS
No full textAbstract Synthetic Aperture Radar (SAR) data are well‐suited for change detection over agricultural fields, owing to high spatiotemporal resolution and sensitivity to soil and vegetation. The goal of this work is to evaluate the science algorithm for the NASA ISRO SAR (NISAR) Cropland Area product using data collected by NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) platform and the simulated NISAR data derived from it. This study uses mode 129, which is to be used for global‐scale mapping. The mode consists of an upper (129A) and lower band (129B), respectively having bandwidths of 20 and 5 MHz. This work uses 129A data because it has a four times finer range resolution compared to 129B. The NISAR algorithm uses the coefficient of variation (CV) to perform crop/noncrop classification at 100 m. We evaluate classifications using three accuracy metrics (overall accuracy, J‐statistic, Cohen's Kappa) and spatial resolutions (10, 30, and 100 m) for crop/noncrop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded NISAR's mission accuracy requirement of 80%. The UAVSAR 10 m data performed best, achieving maximum overall accuracy, J‐statistic, and Kappa values of 85%, 0.62, and 0.60. The same metrics for the 129A product respectively are: 77%, 0.40, 0.36 at 10 m; 81%, 0.55, 0.49 at 30 m; 80%, 0.58, 0.50 at 100 m. We found that using a literature recommended CVthr value of 0.5 yielded suboptimal accuracy (65%) at this site and that optimal CVthr values monotonically decreased with decreasing spatial resolution
Impacts of land use/cover classification accuracy on regional climate simulations
No full textLand use/cover change has been recognized as a key component in global change. Various land cover data sets, including historically reconstructed, recently observed, and future projected, have been used in numerous climate modeling studies at regional to global scales. However, little attention has been paid to the effect of land cover classification accuracy on climate simulations, though accuracy assessment has become a routine procedure in land cover production community. In this study, we analyzed the behavior of simulated precipitation in the Regional Atmospheric Modeling System (RAMS) over a range of simulated classification accuracies over a 3 month period. This study found that land cover accuracy under 80% had a strong effect on precipitation especially when the land surface had a greater control of the atmosphere. This effect became stronger as the accuracy decreased. As shown in three follow-on experiments, the effect was further influenced by model parameterizations such as convection schemes and interior nudging, which can mitigate the strength of surface boundary forcings. In reality, land cover accuracy rarely obtains the commonly recommended 85% target. Its effect on climate simulations should therefore be considered, especially when historically reconstructed and future projected land covers are employed
Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery
No full textDue to increasing global demand for natural rubber products, rubber (Hevea brasiliensis) plantation expansion has occurred in many regions where it was originally considered unsuitable. However, accurate maps of rubber plantations are not available, which substantially constrain our understanding of the environmental and socioeconomic impacts of rubber plantation expansion. In this study we developed a simple algorithm for accurate mapping of rubber plantations in northern tropical regions, by combining a forest map derived from microwave data and unique phenological characteristics of rubber trees observed from multi-temporal Landsat imagery. Phenology of rubber trees and natural evergreen forests in Hainan Island, China, was evaluated using eighteen Landsat TM/ETM+ images between 2007 and 2012. Temporal profiles of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), and near-infrared (NIR) reflectance for rubber trees and natural forest were constructed. The results showed that rubber plantations are distinguishable from natural evergreen forests in two phenological phases: 1) during the defoliation (leaf-off) phase in late February-March, vegetation index (NDVI, EVI, LSWI) values were lower in rubber plantations than in natural evergreen forests; and 2) during the foliation (new leaf emergence) phase in late March-April, rubber plantations had similar NDVI and LSWI values but higher EVI and NIR reflectance values than in natural forests. Therefore, it is possible to delineate rubber plantations within forested landscapes using one to two optical images acquired in the defoliation and/or foliation period. The mapping technique was developed and applied in the Danzhou Region of Hainan. Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50-m Orthorectified Mosaic images were used to generate a forest cover map and further integrated with the phenological information of rubber plantations extracted from Landsat TM images during the foliation phase. The resultant map of rubber plantations has high accuracy (both producer's and user's accuracy is 96%). This simple and integrated algorithm has the potential to improve mapping of rubber plantations at the regional scale. This study also shows the value of time series Landsat images and emphasizes imagery selection at appropriate phenological phase for land cover classification, especially for delineating deciduous vegetation. (C) 2013 Elsevier Inc. All rights reserved
High Resolution Mapping of Peatland Hydroperiod at a High-Latitude Swedish Mire
No full textMonitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scattering mechanisms, and data interrelationships. A Classification and Regression Tree (CART), based on random forest, fused multi-mode (fine-beam single, dual, quad pol) Phased Array L-band Synthetic Aperture Radar (PALSAR) and LiDAR-derived elevation to effectively map hydroperiod attributes at the Swedish mire across an aggregated warm season (May–September, 2006–2010). Image derived estimates of water and peat moisture were sensitive (R<sup>2</sup> = 0.86) to field measurements of water table depth (cm). Peat areas that are underlain by permafrost were observed as areas with fluctuating soil moisture and water table changes
Assessing Cyanobacterial Harmful Algal Blooms as Risk Factors for Amyotrophic Lateral Sclerosis
No full textReoccurring seasonal cyanobacterial harmful algal blooms (CHABs) persist in many waters, and recent work has shown links between CHAB and elevated risk of amyotrophic lateral sclerosis (ALS). Quantifying the exposure levels of CHAB as a potential risk factor for ALS is complicated by human mobility, potential pathways, and data availability. In this work, we develop phycocyanin concentration (i.e., CHAB exposure) maps using satellite remote sensing across northern New England to assess relationships with ALS cases using a spatial epidemiological approach. Strategic semi-analytical regression models integrated Landsat and in situ observations to map phycocyanin concentration (PC) for all lakes greater than 8 ha (n = 4117) across the region. Then, systematic versions of a Bayesian Poisson Log-linear model were fit to assess the mapped PC as a risk factor for ALS while accounting for model uncertainty and modifiable area unit problems. The satellite remote sensing of PC had strong overall ability to map conditions (adj. R2, 0.86; RMSE, 11.92) and spatial variability across the region. PC tended to be positively associated with ALS risk with the level of significance depending on fixed model components. Meta-analysis shows that when average PC exposure is 100 μg/L, an all model average odds ratio is 1.48, meaning there is about a 48% increase in average ALS risk. This research generated the first regionally comprehensive map of PC for thousands of lakes and integrated robust spatial uncertainty. The outcomes support the hypothesis that cyanotoxins increase the risk of ALS, which helps our understanding of the etiology of ALS
Roles of atmospheric and land surface data in dynamic regional downscaling
No full textIn studies dealing with the impact of land use changes on atmospheric processes, a key methodological step is the validation of simulated current conditions. However, regions lacking detailed atmospheric and land use data provide limited information with which to accurately generate control simulations. In this situation, the difference between baseline control simulations and different land use change simulations can be quite different owing to the quality of the atmospheric and land use data sets. Using multiple simulations at the Monteverde cloud forest region of Costa Rica as an example, we show that when a regional climate model is used to study the effect of land use change, it can produce distinctly different results at regional scales, depending on the amount of data available to run the climate simulations. We show that for the specific case of land use change impact studies, the simulation results are very sensitive to the prescribed atmospheric information (e.g., lateral boundary conditions) compared to the land use (surface boundary) information
