Phenological differences among vegetation types, reflected in temporal variations in the Normalized Difference Vegetation Index (NDVI) derived from satellite data, have been used to classify land ...cover at continental scales. Extending this technique to global scales raises several issues: identifying land cover types that are spectrally distinct and applicable at the global scale; accounting for phasing of seasons in different parts of the world; validating results in the absence of reliable information on global land cover; and acquiring high quality global data sets of satellite sensor data for input to land cover classifications. For this study, a coarse spatial resolution (one by one degree) data set of monthly NDVI values for 1987 was used to explore these methodological issues. A result of a supervised, maximum likelihood classification of eleven cover types is presented to illustrate the feasibility of using satellite sensor data to increase the accuracy of global land cover information, although the result has not been validated systematically. Satellite sensor data at finer spatial resolutions that include other bands in addition to NDVI, as well as methodologies to better identify and describe gradients between cover types, could increase the accuracy of results of global land cover data sets derived from satellite sensor data.
Measured and modeled point spread functions (PSF) of sensor systems indicate that a significant portion of the recorded signal of each pixel of a satellite image originates from outside the area ...represented by that pixel. This hinders the ability to derive surface information from satellite images on a per-pixel basis. In this study, the impact of the PSF of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m bands was assessed using four images representing different landscapes. Experimental results showed that though differences between pixels derived with and without PSF effects were small on the average, the PSF generally brightened dark objects and darkened bright objects. This impact of the PSF lowered the performance of a support vector machine (SVM) classifier by 5.4% in overall accuracy and increased the overall root mean square error (RMSE) by 2.4% in estimating subpixel percent land cover. An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%. This method differs from traditional PSF inversion deconvolution methods in that the PSF was adjusted with lower weighting factors for signals originating from neighboring pixels than those specified by the PSF model. By using this deconvolution method, the lost classification accuracy due to residual impact of PSF effects was reduced to only 1.66% in overall accuracy. The increase in the RMSE of estimated subpixel land cover proportions due to the residual impact of PSF effects was reduced to 0.64%. Spatial aggregation also effectively reduced the errors in estimated land cover proportion images. About 50% of the estimation errors were removed after applying the deconvolution method and aggregating derived proportion images to twice their dimensional pixel size.
The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite ...remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation.
Programs to provide alternative energy sources such as biogas improve indoor air quality and potentially reduce pressure on forests from fuelwood collection. This study tests whether biogas ...intervention is associated with higher forest biomass and forest regeneration in degraded forests in Chikkaballapur district in Southern India. Using propensity score matching, we find that forest plots in proximity to villages with biogas interventions (treatment) had greater forest biomass than comparable plots around villages without biogas (control). We also found significantly higher sapling abundance and diversity in treatment than control plots despite no significant difference in seedling abundances and diversity in treatment forests, suggesting that plants have a higher probability of reaching sapling stage. These results indicate the potential for alternative energy sources that reduce dependence on fuelwood to promote regeneration of degraded forests. However, forest regrowth is not uniform across treatments and is limited by soil nutrients and biased towards species that are light demanding, fire-resistant and can thrive in poor soil conditions.
One of the annual land cover products to be made from Moderate Resolution Imaging Spectroradiometer (MODIS) data is the vegetation continuous fields layers. Of these fields, one is a global percent ...tree cover map. Using field measurements, IKONOS data, Enhanced Thematic Mapper Plus (ETM+) data, and ancillary map sources, a tree cover map was made and validated for two WRS path/rows in Western Province, Zambia. This map will be used in validating the 500-m global MODIS tree cover product. The map was made at the 30-m Enhanced Thematic Mapper Plus (ETM+) resolution and also scaled up to 250- and 500-m resolutions. Five IKONOS images were classified into crown cover/no crown cover maps at 4-m resolution. These maps were aggregated to 30 m to create a continuous training data set of percent crown cover. Three dates of ETM+ data were acquired to predict percent crown cover using a regression tree algorithm. Comparisons of training accuracies and field data to ETM+ tree estimates yielded root mean square errors (rmse) of ∼±10% crown cover. When aggregating the 30-m map to 250- and 500-m MODIS cell sizes, the training errors are more than halved. The final 250-m map was assessed using a structural vegetation map of the area and an overall rmse of 8.5% is estimated. The 250-m map was sampled and used to derive a tree cover continuous field product using 3 Level 1B MODIS time slices, approximating the acquisitions of the ETM+ data. The results are promising as an overall root mean square error between the ETM+ derived tree crown cover map and an aggregated MODIS 500-m map was 5.2%. Results from this test in Zambia show that the MODIS 250-m bands should allow for improved depictions of percent tree cover.
Global, monthly, 1° by 1° biophysical land surface datasets for 1982–90 were derived from data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board theNOAA-7, -9, ...and-11satellites. The AVHRR data are adjusted for sensor degradation, volcanic aerosol effects, cloud contamination, short-term atmospheric effects (e.g., water vapor and aerosol effects ≤2 months), solar zenith angle variations, and missing data. Interannual variation in the data is more realistic as a result. The following biophysical parameters are estimated: fraction of photosynthetically active radiation absorbed by vegetation, vegetation cover fraction, leaf area index, and fraction of green leaves. Biophysical retrieval algorithms are tested and updated with data from intensive remote sensing experiments. The multiyear vegetation datasets are consistent spatially and temporally and are useful for studying spatial, seasonal, and interannual variability in the biosphere related to the hydrological cycle, the energy balance, and biogeochemical cycles. The biophysical data are distributed via the Internet by the Goddard Distributed Active Archive Center as a precursor to the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. Release of more extensive, higher-resolution datasets (0.25° by 0.25°) over longer time periods (1982–97/98) is planned for ISLSCP Initiative II.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
In June 2013, the Malay Peninsula experienced severe smoke pollution, with daily surface particulate matter (PM) concentrations in Singapore greater than 350 μg/m3, over 2 times the air quality ...standard for daily mean PM10 set by the U.S. Environmental Protection Agency. Unlike most haze episodes in the Malay Peninsula in recent decades (e.g., the September 2015 event), the June 2013 haze occurred in the absence of an El Niño, during negative Indian Ocean Dipole conditions, with smoke carried eastward to the Peninsula from fires in the Riau province of central Sumatra. We show that June 2013 was not an exceptional event; inspection of visibility data during 2005–2015 reveals two other severe haze events in the Malay Peninsula (August 2005 and October 2010) occurring under similar conditions. Common to all three events was a combination of anomalously strong westerly winds over Riau province concurrent with late phases of the Real‐Time Multivariate Madden‐Julian Oscillation Index, during negative phases of the Indian Ocean Dipole. Our work suggests that identifying the meteorological mechanism driving these westerly wind anomalies could help stakeholders prepare for future non‐El Niño haze events in Singapore and the Malay Peninsula.
Key Points
Three recent, severe haze events in the Malay Peninsula cannot be explained by El Nino‐positive Indian Ocean Dipole conditions
All three events are associated with anomalous westerly wind speeds occurring over Riau as the Madden‐Julian Oscillation moves across Indonesia
Late phases of the MJO are associated with the transport of extreme smoke pollution from Riau province to the Malay Peninsula
As an alternative to the traditional approach of using predefined classification schemes with discrete numbers of cover types to describe the geographic distribution of vegetation over the Earth's ...land surface, we apply a linear mixture model to derive global continuous fields of percentage woody vegetation, herbaceous vegetation and bare ground from 8 km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Land data. Linear discriminants for input into the mixture model are derived from 30 metrics representing the annual phenological cycle, using training data derived from a global network of scenes acquired by Landsat. We test the stability and robustness of the method by assessing the consistency of results derived independently for each year in the 1982 to 1994 AVHRR data set. For those forested locations where land cover variability would not be expected, the percentage woody estimates displayed standard deviations over the 12 years of less than 10%. Problems with the method occur in high latitudes where snow cover in some years and not others produces inconsistencies in the continuous fields. Overall, the results suggest that the method produces fairly consistent results despite apparent problems with artifacts in the multi-year AVHRR data set due to calibration problems, aerosols and other atmospheric effects, bidirectional effects, changes in equatorial crossing time, and other factors. Comparison of continuous fields with other land cover data sets derived from remote sensing suggests 69% to 84% agreement in the per cent woody field, with the highest agreement when per cent woody is averaged over the 12 years. In comparison with regional data sets for the US and Bolivia, the method overestimates per cent woody vegetation for grassland and sparsely wooded locations. We conclude that the method, with possible refinements and more sophisticated methods to include multiple endmembers, improved estimates of endmember values and nonlinear responses of vegetation to proportional cover, can potentially be used to indicate changes in land cover characteristics over time using multi-year data sets as inputs when perfect calibration and consistency between years cannot be assumed.
Classification trees are a powerful alternative to more traditional approaches of land cover classification. Trees provide a hierarchical and nonlinear classification method and are suited to ...handling non-parametric training data as well as categorical or missing data. By revealing the predictive hierarchical structure of the independent variables, the tree allows for great flexibility in data analysis and interpretation. In this Letter, we compare a tree' s performance to that of a maximum likelihood classifier using a 1° by 1° global data sel. The tree's accuracy in classifying a validation dala set is comparable to that when using maximum likelihood (82 per cent). The tree also may be used to reduce the dimensionality of data sets and to find those metrics that are most useful for discriminating among cover types.
Seasonal changes in the greenness of vegetation, described in remotely sensed data as changes in the normalized difference vegetation index (NDVI) throughout the year, have been the basis for ...discriminating between cover types in previous attempts to derive land cover from AVHRR data at global and continental scales. Several researchers have suggested and applied the use of metrics, such as maximum NDVI or length of growing season derived from a temporal profile of 10-day or monthly NDVI values, as an alternative to classifying cover types from the monthly NDVI values directly. This study examines the use of metrics derived from the NDVI temporal profile, as well as metrics derived from observations in red, infrared, and thermal bands, to improve discrimination between 12 cover types on a global scale. According to separability measures calculated from Bhattacharya distances, average separabilities improved by using 12 of the 16 metrics tested (1.97) compared to separabilities using 12 monthly NDVI values alone (1.88). Separabilities improved from poor to good in 20 out of 25 pairs of cover types with poor separability. Percentage of pixels correctly classified in a maximum likelihood classifications also improved by using the metrics from 76% to 86%. Overall, the most robust metrics for discriminating between cover types were: mean NDVI, maximum NDVI, NDVI amplitude, AVHRR Band 2 (near-infrared reflectance) and Band 1 (red reflectance) corresponding to the time of maximum NDVI, and maximum land surface temperature. Deciduous and evergreen vegetation can be distinguished by mean NDVI, maximum NDVI, NDVI amplitude, and maximum land surface temperature. Needleleaf and broadleaf vegetation can be distinguished by either mean NDVI and NDVI amplitude or maximum NDVI and NDVI amplitude.
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