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Item A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005(MDPI, 2013-04-15) Giree, Namita; Stehman, Stephen V.; Potapov, Peter; Hansen, Matthew C.Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth.Item Quantification of Impact of Orbital Drift on Inter-Annual Trends in AVHRR NDVI Data(MDPI, 2014-07-22) Nagol, Jyoteshwar R.; Vermote, Eric F.; Prince, Stephen D.The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction and orbital drift of the satellites through their active life. Access to location specific quantification of uncertainty is crucial for appropriate evaluation of the trends and anomalies. This paper provides per pixel quantification of orbital drift related spurious trends in Long Term Data Record (LTDR) AVHRR NDVI data product. The magnitude and direction of the spurious trends was estimated by direct comparison with data from MODerate resolution Imaging Spectrometer (MODIS) Aqua instrument, which has stable inter-annual sun-sensor geometry. The maps show presence of both positive as well as negative spurious trends in the data. After application of the BRDF correction, an overall decrease in positive trends and an increase in number of pixels with negative spurious trends were observed. The mean global spurious inter-annual NDVI trend before and after BRDF correction was 0.0016 and −0.0017 respectively. The research presented in this paper gives valuable insight into the magnitude of orbital drift related trends in the AVHRR NDVI data as well as the degree to which it is being rectified by the MODIS BRDF correction algorithm used by the LTDR processing stream.Item A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation(MDPI, 2016-03-22) Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O.In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.Item Towards An Improved Long-term Data Record From The Advanced Very-high Resolution Radiometer: Evaluation, Atmospheric Correction, And Intercalibration(2021) Santamaria Artigas, Andres Eduardo; Justice, Christopher O; Franch, Belen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Long-term data records from satellite observations are crucial for the study of land surface properties and their long-term dynamics. The AVHRR long term data record (LTDR) is an ongoing effort to generate a consistent climate record of daily atmospherically corrected observations with global coverage that is suitable for long term studies of the Earth surface. In this dissertation, I identified three areas for the improvement of the LTDR: (1) The comprehensive evaluation of the LTDR performance and characterization if its uncertainties. (2) The retrieval of water vapor information from AVHRR data for a more accurate atmospheric correction. (3) The recalibration of the record to address inconsistency issues. The first study consisted on a global long-term evaluation of the LTDR with matched observations from the Landat-5 Thematic Mapper instrument. Results from this evaluation showed that the record performance was close to the proposed specification. The second study proposed a method for the retrieval of water vapor from AVHRR data, which provides a crucial input for the atmospheric correction process. Evaluation of the retrieved values with reference datasets showed excellent results, with a water vapor error lower than 0.45g/cm2. Finally, the last chapter proposed a novel method for the selection of stable areas suitable for satellite intercalibration and for the derivation of recalibration coefficients. The evaluation of the original and recalibrated record showed that for most cases the recalibrated record performed better.Item Outgoing Longwave Radiation at the Top of Atmosphere: Algorithm Development, Comprehensive Evaluation, and Case Studies(2019) Zhou, Yuan; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) represents the total outgoing radiative flux emitted from the Earth’s surface and atmosphere in the thermal-infrared wavelength range. It plays a role as a powerful diagnostic of Earth’s climate system response to absorbed incoming solar radiation (ASR). Long-term measurements of OLR are essential for quantitatively understanding the climate system and its variability. However, inconsistencies and uncertainties have been always existing in OLR estimation among different datasets and algorithms. The objective of this dissertation is to carry out a comprehensive investigation on OLR with three specific questions: 1) How large are the discrepancies in estimates from various OLR products and what are their spatial and temporal patterns? 2) How to generate more accurate and more useful OLR estimates from multi-spectral satellite observations? 3) How does OLR respond to extreme climate and geological events such as El Niño/Southern Oscillation (ENSO) and giant earthquakes, and does the newly developed OLR products have any advantage to predict such events? To address those questions, this dissertation 1) conducts comprehensive evaluations on multiple OLR datasets by performing inter-comparisons among different satellite retrieved OLR products and different reanalysis OLR datasets, respectively; 2) develops an algorithm framework for estimating OLR from multi-spectral satellite observations based on radiative transfer simulations and statistical approaches; 3) investigates the correlation between OLR anomalies and historical ENSO events and a typical giant earthquake, and makes an attempt to predict ENSO and earthquake through OLR variations. Results indicate that 1) obvious discrepancies exist among different OLR datasets, with the two Japanese Meteorological Agency’s (JMA) Japanese Reanalysis project (JRA) OLRs displays the largest differences with others. However, all OLR products and datasets have comparable magnitude of inter-annual variability and monthly/seasonally anomaly, resulting in similar capability to capture the tropical expansion and ENSO events; 2) the developed OLR algorithm framework can generate reliable OLR estimates from multi-spectral remotely sensed data including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR); 3) OLR has a potential to predict ENSO events through traditional statistical approach and machine learning methods, and it has slight advantage over the sea-surface-temperature (SST) as a metric for this purpose. The developed high resolution AVHRR OLR performs better than High-Resolution Infrared Radiation Sounder (HIRS) and NOAA interpolated AVHRR OLR in predicting ENSO. In addition, the singularities in OLR spatial anomalies around the giant earthquake epicenter starting three days prior to the earthquake days also suggests the OLR as an effective precursor of such an event, and the developed AVHRR OLR showed much stronger sensitivity to the coming earthquake than the existing NOAA interpolated AVHRR OLR, suggesting that the former one as a better indicator for the earthquake prediction. In this dissertation, the in-depth inter-comparisons among various OLR datasets will contribute as a reference for peers in the climate community who use OLR as one of inputs in their climate models or other diagnostic purpose. The developed OLR algorithm framework could be utilized to estimate OLR from future multi-spectral satellite data. This study also demonstrates that OLR is a promising indicator to predict ENSO and testifies that it is a precursor of giant earthquakes, which has implications for decision making aimed at alleviating the impacts on life and property from these extreme climate variations through some preventive measures such as releasing weather alert and conducting evacuations.Item SPATIAL PATTERNS AND POTENTIAL MECHANISMS OF LAND DEGRADATION IN THE SAHEL(2013) Rishmawi, Khaldoun; Prince, Stephen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There is a great deal of debate on the extent, causes and even the reality of land degradation in the Sahel. On one hand, extrapolations from field-scale studies suggest widespread and serious reductions in biological productivity threatening the livelihoods of many communities. On the other hand, coarse resolution remote sensing studies consistently reveal a net increase in vegetation production exceeding that expected from the recovery of rainfall following the extreme droughts of the 1970s and 1980s, thus challenging the notion of widespread, subcontinental-scale degradation. Yet, the spatial variations in the rates of vegetation recovery are not fully explained by rainfall trends which suggest additional causative factors. In this dissertation, it is hypothesized that in addition to rainfall other climatic variables and anthropogenic uses of the land have had measurable impacts on vegetation production. It was found that over most of the Sahel, the interannual variability in growing season sum NDVI (used as a proxy of vegetation productivity) was strongly related to rainfall, humidity and temperature while the relationship with rainfall alone was generally weaker. The climate- sum NDVI relationships were used to predict potential NDVI; that is the NDVI expected in response to climate variability alone excluding any human-induced changes in productivity. The differences between predicted and observed NDVI were regressed against time to detect any long term (positive or negative) trends in vegetation productivity. It was found that over most of the Sahel the trends either exceeded or did not significantly depart from what is expected from the trends in climate. However, substantial and spatially contiguous areas (~8% of the total area of the Sahel) were characterized by significant negative trends. To test whether the negative trends were in fact human-induced, they were compared with the available data on population density, land use pressures and land biophysical properties that determine the susceptibility of land to degradation. It was found that the spatial variations in the trends of the residuals were not only well explained by the multiplicity of land use pressures but also by the geography of soil properties and percentage tree cover.Item QUANTIFICATION OF ERROR IN AVHRR NDVI DATA(2011) Nagol, Jyoteshwar Reddy; Prince, Stephen D; Vermote, Eric F; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. However temporal aggregation improved the precision of the data to a limited extent only. The research presented in this dissertation indicated that the NDVI change of ~0.03 to ~0.08 NDVI units in 10 to 20 years, frequently reported in recent literature, can be significant in some cases. However, unless spatially explicit uncertainty metrics are quantified for the specific spatiotemporal aggregation schemes used by these studies, the significance of observed differences between sites and temporal trends in NDVI will remain unknown.Item Improving Predictive Capabilities of Environmental Change with GLOBE Data(2006-07-25) Robin, Jessica; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses two applications of Normalized Difference Vegetation Index (NDVI) essential for predicting environmental changes. The first study focuses on whether NDVI can improve model simulations of evapotranspiration for temperate Northern (> 35) regions. The second study focuses on whether NDVI can detect phenological changes in start of season (SOS) for high Northern (> 60) environments. The overall objectives of this research were to (1) develop a methodology for utilizing GLOBE data in NDVI research; and (2) provide a critical analysis of NDVI as a long-term monitoring tool for environmental change. GLOBE is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. The first study utilized data collected by one GLOBE school in Greenville, Pennsylvania and the second utilized phenology observations made by GLOBE students in Alaska. Results from the first study showed NDVI could predict transpiration periods for environments like Greenville, Pennsylvania. In phenological terms, these environments have three distinct periods (QI, QII, and QIII). QI reflects onset of the growing season (mid March - mid May) when vegetation is greening up (NDVI < 0.60) and transpiration is less than 2mm/day. QII reflects end of the growing season (mid September - October) when vegetation is greening down and transpiration is decreasing. QIII reflects height of the growing season (mid May - mid September) when transpiration rates average between 2 and 5 mm per day and NDVI is at its maximum (>0.60). Results from the second study showed that a climate threshold of 153 ± 22 growing degree days was a better predictor of SOS for Fairbanks than a NDVI threshold applied to temporal AVHRR and MODIS datasets. Accumulated growing degree days captured the inter-annual variability of SOS better than the NDVI threshold and most closely resembled actual SOS observations made by GLOBE students. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska. Both studies did show that GLOBE data provides an important source of input and validation information for NDVI research.Item Monitoring land degradation in Southern Africa by assessing changes in primary productivity.(2005-06-15) Wessels, Konrad; Prince, Stephen D.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Land degradation is one of the most serious environmental problems of our time. Land degradation describes circumstances of reduced biological productivity. The fundamental goal of this thesis was to develop land degradation monitoring approaches based on remotely sensed estimates of vegetation production, which are capable of distinguishing human impacts from the effects of natural climatic and spatial variability. Communal homelands in South Africa (SA) are widely regarded to be severely degraded and the existence adjacent, non-degraded areas with the same soils and climate, provides a unique opportunity to test regional land degradation monitoring methods. The relationship between 1km AVHRR, growth season sumNDVI and herbaceous biomass measurements (1989-2003) was firstly tested in Kruger National Park, SA. The relationship was moderately strong, but weaker than expected. This was attributed to the fact that the small areas sampled at field sites were not representative of the spatial variability within 1x1km. The sumNDVI adequately estimated inter-annual changes in vegetation production and should therefore be useful for monitoring land degradation. Degraded areas mapped by the National-Land-Cover in north-eastern SA were compared to non-degraded areas in the same land capability units. The sumNDVI of the degraded areas was consistently lower, regardless of large variations in rainfall. However, the ecological stability and resilience of the degraded areas, as measured by the annual deviations from each pixel's mean sumNDVI, were no different to those of non-degraded areas. This suggests that the degraded areas may be in an alternative, but stable ecological state. To monitor human-induced land degradation it is essential to control for the effects of rainfall on vegetation production. Two methods were tested (i) Rain-Use Efficiency (RUE=NPP/Rainfall) and (ii) negative trends in the differences between the observed sumNDVI and the sumNDVI predicted by the rainfall using regressions calculated for each pixel (RESTREND). RUE had a strong negative correlation with rainfall and did not provide a reliable index of degradation. The RESTREND method identified areas in and around the degraded communal lands that exhibit negative trends in production per unit rainfall. This research made a significant contribution to the development of remote sensing based land degradation monitoring methods.