Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images

Leaf phenology of tropical evergreen forests affects carbon and water fluxes. In an earlier study of a seasonally moist evergreen tropical forest site in the Amazon basin, time series data of Enhanced Vegetation Index (EVI) from the VEGETATION and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors showed an unexpected seasonal pattern, with higher EVI in the late dry season than in the wet season. In this study we conducted a regional-scale analysis of tropical evergreen forests in South America, using time series data of EVI from MODIS in 2002. The results show a large dynamic range and spatial variations of annual maximum EVI for evergreen forest canopies in the region. In tropical evergreen forests, maximum EVI in 2002 typically occurs during the late dry season to early wet season. This suggests that leaf phenology in tropical evergreen forests is not determined by the seasonality of precipitation. Instead, leaf phenological process may be driven by availability of solar radiation and/or avoidance of herbivory.     

Mapping insect defoliation in Scots pine with MODIS time-series data

Insect damage is a general problem that disturbs the growth of forests, causing economic losses and affecting carbon sequestration. Coarse-resolution data from satellites are potentially useful for national and regional mapping of forest damage, but the accuracy of these methods has not been fully examined. In this study, a method was tested for the mapping of defoliation in Scots pine [Pinus silvestris] forests in southeast Norway caused by the pine sawfly [Neodiprion sertifer], with the use of multi-temporal MODIS 16-day composite vegetation index data and the TIMESAT processing method. The damage mapping method used differences in summer mean values and angles of the seasonal profiles, indicating decreasing foliage density, to identify pixels that represent areas containing forest damage. In addition to 16-day NDVI the Wide Dynamic Range Vegetation Index (WDRVI) was tested. Damage areas were identified by classifying data into pixels representing damaged versus undamaged forest areas using a boolean combination of thresholded parameters. Classification results were evaluated against the change in LAI estimated from airplane LIDAR measurements, as an indicator of defoliation. The damage classifications detected 71% to 82% of the pixels with damage, and had kappa coefficients varying between 0.48 and 0.63, indicating some overestimation. This was due e.g. to failure to include clear-cut areas in the evaluation data. Damage classification with WDRVI only resulted in slight improvement compared to the NDVI. Only weak relationships were found between the LIDAR-estimated defoliation and the change parameters obtained from MODIS. Consequently, mapping of the degree of defoliation from MODIS was abandoned. In conclusion, the damage detection method based on MODIS data was found to be useful for locating insect damage, but not for estimating its intensity. Control of the detected damage areas using high-resolution remote sensing data, aerial survey, or fieldwork is recommended for accurate delineation in operational applications.     

Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains

Improved and up-to-date land use/land cover (LULC) data sets that classify specific crop types and associated land use practices are needed over intensively cropped regions such as the U.S. Central Great Plains, to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The Moderate Resolution Imaging Spectroradiometer (MODIS) holds considerable promise for detailed, large-area crop-related LULC mapping in this region given its global coverage, unique combination of spatial, spectral, and temporal resolutions, and the cost-free status of its data. The objective of this research was to evaluate the applicability of time-series MODIS 250 m normalized difference vegetation index (NDVI) data for large-area crop-related LULC mapping over the U.S. Central Great Plains. A hierarchical crop mapping protocol, which applied a decision tree classifier to multi-temporal NDVI data collected over the growing season, was tested for the state of Kansas. The hierarchical classification approach produced a series of four crop-related LULC maps that progressively classified: 1) crop/non-crop, 2) general crop types (alfalfa, summer crops, winter wheat, and fallow), 3) specific summer crop types (corn, sorghum, and soybeans), and 4) irrigated/non-irrigated crops. A series of quantitative and qualitative assessments were made at the state and sub-state levels to evaluate the overall map quality and highlight areas of misclassification for each map.The series of MODIS NDVI-derived crop maps generally had classification accuracies greater than 80%. Overall accuracies ranged from 94% for the general crop map to 84% for the summer crop map. The state-level crop patterns classified in the maps were consistent with the general cropping patterns across Kansas. The classified crop areas were usually within 1-5% of the USDA reported crop area for most classes. Sub-state comparisons found the areal discrepancies for most classes to be relatively minor throughout the state. In eastern Kansas, some small cropland areas could not be resolved at MODIS' 250 m resolution and led to an underclassification of cropland in the crop/non-crop map, which was propagated to the subsequent crop classifications. Notable regional areal differences in crop area were also found for a few selected crop classes and locations that were related to climate factors (i.e., omission of marginal, dryland cropped areas and the underclassification of irrigated crops in western Kansas), localized precipitation patterns (overclassification of irrigated crops in northeast Kansas), and specific cropping practices (double cropping in southeast Kansas).     

A hybrid approach to mapping land-use modification and land-cover transition from MODIS time-series data: A case study from the Bolivian seasonal tropics

This study uses a combination of satellite imagery and GIS data, a vegetation map, interview data, and on-site field studies to map detailed natural vegetation to land-use conversion pathways (~ 22,000 possible combinations) in the seasonal tropics of Santa Cruz Department in southeastern Bolivia from 1994 to 2008. We mapped a suite of land-use classes based on the seasonal phenology of double- and single season cropping regimes; pasture; and bare soil cropland (fallow). Analyses focus specifically on the Corredor Bioceánico, which bisects some of the most sensitive and poorly understood ecosystems in the world and indirectly creating one of the most important agricultural region-deforestation hotspots in South America at the present time. Training data to predict class membership were based on MODIS NDVI annual mean, maximum, minimum, and amplitude derived from field observations, semi-structured interviews, and aerial videography. Results show that over 8,000 km² of forest was lost during the 14-year study period. In the first years of cultivation, pasture is the dominant land use, but quickly gives way to cropland. The main findings according to forest type is that transitional forest types on deep and poorly drained soils of alluvial plains have lost the most in terms of percentage area cleared. The resulting transition pathways can potentially provide decision-makers with more detailed insight as to the proximate causes or driving forces of land change in addition to the most threatened forests remaining in the Tierras Bajas and those most likely to be cleared in the Brazilian Shield and Pantanal.     

Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data

Accurate assessment of temporal changes in gross primary production (GPP) is important for carbon budget assessments and evaluating the impact of climate change on crop productivity. The objective of this study was to devise a simple remote sensing-based GPP model to quantify daily GPP of maize. In the model, (1) daily shortwave radiation (SW), derived from the reanalysis data (North American Land Data Assimilation System; NLDAS-2) and (2) smoothed Wide Dynamic Range Vegetation Index (WDRVI) data, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations were used as proxy variables of the incident photosynthetically active radiation (PAR) and the total canopy chlorophyll content, respectively. The model was calibrated and validated by using tower-based CO₂ flux observations over an 8-year period (2001 to 2008) for one rainfed and two irrigated sites planted to maize as part of the Carbon Sequestration Program at the University of Nebraska-Lincoln. The results showed the temporal features of the product SW*WDRVI closely related to the temporal GPP variations in terms of both daily variations and seasonal patterns. The simple GPP model was able to predict the daily GPP values and accumulated GPP values of maize with high accuracy.     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

MODIS time-series imagery for forest disturbance detection and quantification of patch size effects

The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m single day surface reflectance (MOD09GQK) and 16-day composite gridded vegetation index data (MOD13Q1) were used to detect forest harvest disturbance between 2000 and 2004 in northern Maine. A MODIS multi-date Normalized Difference Vegetation Index (NDVI) forest change detection map was developed from each MODIS data set. A Landsat TM/ETM+ change detection map was developed as a reference to assess the effect of disturbed forest patch size on classification accuracy (agreement) and disturbed area estimates of MODIS. The MODIS single day and 16-day composite data showed no significant difference in overall classification accuracies. However, the 16-day NDVI change detection map had marginally higher overall classification accuracy (at 85%), but had significantly lower detection accuracy related to disturbed patch size than the single day NDVI change detection map. The 16-day composite NDVI data achieved 69% detection accuracy and the single day NDVI achieved 76% when the disturbed patch size was greater than 20 ha. The detection accuracy increased to approximately 90% for both data sets when the patch size exceeded 50 ha. The R² (range 0.6 to 0.9) and slope (range 0.5 to 0.9) of regression lines between Landsat and MODIS data (based on forest disturbance percent of township) increased with the mean disturbed patch size of each township. The 95% confidence intervals of forest disturbance percent estimate for each township were narrow with less than 1% of each township at the mean MODIS forest disturbance level.     

Comparing MODIS and ETM+ data for regional and global land classification

Nearly simultaneous reflectance data sets from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), at 30-m resolution, and the Terra satellite instrument MODIS, at 500-m resolution, are compared for their ability to map fractional coverage of surface types over large areas. Lower spatial resolution MODIS classification results are generally comparable those of ETM+, with discrepancies for some regions with mixed surface types. Analysis of laboratory and field spectra suggests an ambiguity, the “brightness ambiguity”, which can prevent accurate area estimation of pixels having two or more surface types. This ambiguity, plus general mathematical inversion issues, can account for the discrepancy. Thus, occasional high-resolution measurements, as from Landsat 7, are necessary to refine estimations of large area surface types from MODIS and similar lower spatial resolution instruments.     

A VARI-based relative greenness from MODIS data for computing the Fire Potential Index

The Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation Index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate that VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R² of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FPI outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.     

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth (Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation detection products and methods that could be applied in near real time without intensive field survey data collection as a precursor. In our study, MODIS data for 2000-2006 were processed for the mid-Appalachian highland region of the United States. Gypsy moth defoliation maps showing defoliated forests versus non-defoliated areas were produced from temporally filtered and composited MOD02 and MOD13 data using unsupervised classification and image thresholding of maximum value normalized difference vegetation index (NDVI) datasets computed for the defoliation period (June 10-July 27) of 2001 and of the entire time series. These products were validated by comparing stratified random sample locations to relevant Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reference data sets. Composites of 250 m daily MOD02 outperformed 16-day MOD13 data in terms of classifying forest defoliation, showing a lower omission error rate (0.09 versus 0.56), a similar Kappa (0.67 versus 0.79), a comparable commission error rate (0.22 versus 0.14), and higher overall classification agreement (88 versus 79%). Results suggest that temporally processed MODIS time-series data can detect with good agreement to available reference data the extent and location of historical regional gypsy moth defoliation patches of 0.25 km² or more for 250-meter products. The temporal processing techniques used in this study enabled effective broad regional, “wall to wall” gypsy moth defoliation detection products for a 6.2 million ha region that were not produced previously with either MODIS or other satellite data. This study provides new, previously unavailable information on the relative agreement of temporally processed, gypsy moth defoliation detection products from MODIS NDVI time series data with respect to higher spatial resolution Landsat and ASTER data. These results also provided needed timely information on the potential of MODIS data for contributing near real time defoliation products to a USDA Forest Service Forest Threat Early Warning System.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data

The honey bee industry is of immense importance to global agriculture. In many countries beekeepers are migratory and move their hives between flowering events. Predicting such flowering events is particularly difficult in Australia due to the irregular flowering of eucalypts. We have developed a web-based application for Victorian beekeepers to visualise patterns of growth in floral resources using MODIS and other data, and thus make remote predictions about whether flowering will occur at their apiary sites. We demonstrate the use of this application through comparing ironbark (Eucalyptus tricarpa) growth patterns with flowering and honey production records. While the scientific community as a whole has embraced the use of satellite imagery as a tool for phenological studies, our prototype represents the first attempt to make this same information available to a more general audience.     

Validation of SeaWiFS and MODIS aerosol products with globally distributed AERONET data

The validation of aerosol products derived from ocean color missions is required for the assessment of their uncertainties and as a diagnostic for the atmospheric correction schemes used for determining the ocean apparent optical properties. A comprehensive validation of the aerosol products obtained from the ocean color missions SeaWiFS and MODIS is presented; it relies on the field observations collected at 85 AERONET sites and is completed by preliminary results obtained with the data of the maritime AERONET component. A robust match-up selection protocol yields approximately 7000 match-ups for each sensor. The median absolute relative difference for the aerosol optical thickness τ_a increases from 20-22% at 443 nm to 45-48% in the near-infrared. The validation statistics are comparable for both sensors but MODIS results appear degraded particularly for sites located on isolated islands. The median absolute difference is approximately 0.03 at all wavelengths. Results are further analyzed for specific geographic regions or groups of sites selected to represent oceanic, continental, or desert dust conditions. Importantly, the match-up sets appear generally representative of the regional natural variability in τ_a amplitude and spectral shape, with the notable exception of high τ_a conditions that are excluded. An important finding is the underestimate by the atmospheric correction of the Ångström exponent α, with a median bias of − 0.52. This underestimate is apparent even at low α values and regularly increases with α. This discrepancy in τ_a spectral shape might result from an inappropriate set of candidate aerosol models and/or uncertainties in the calibration at the near-infrared bands. As the validation data base is expanded and updated in relation to new versions of the processing chains, this work provides a benchmark for the assessment of the aerosol products derived from the SeaWiFS and MODIS ocean color missions.     

A spatially explicit land surface phenology data product for science,monitoring and natural resources management applications

Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN).We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans.     

A new approach to evaluate the MODIS annual NPP product (MOD17A3) using forest field data from Turkey

In this study, we present the first evaluation of the MODIS (Moderate Resolution Imaging Spectroradiometer) annual net primary production (NPP) for Turkey’s forest ecosystems using field measurements. Due to lack of country scale field measurements (i.e. flux tower for forest ecosystems), tree DBH (diameter at breast height) data set provided by Ministry of Forest and Water Affairs (MFWA) of Turkey is used to calculate NPP of Turkey’s forest ecosystems. The lack of a reliable NPP data set leads the researchers to use global NPP models such as MODIS annual NPP product. The MODIS MOD17A3 product of vegetation NPP is one of the most highly used data sources for studies of global carbon cycle. However, it is still necessary to test its predictions in multiple biomes, especially for heterogeneous areas in terms of its accuracy and potential bias. Here, we studied a new approach to evaluate coarse scale NPP estimates from the MODIS NPP-MOD17A3 data product, using 2008–2013 field measurements of tree growth throughout Turkey. Three different methods were used to calculate field NPP, including standardized growth coefficients (ministry coefficients [MC]), growth coefficients from North America (Jenkins coefficients [JC]), and annual expected increment (AEI). The average NPP values for all the country is calculated as 2.06 kgC m^–1/5 years (0.412 kgC m^–2 year^–1) (SD = 1.15 kgC m^–1/5 years) from MOD17A3, 0.90 kgC m^–1/5 years (0.18 kgC m^–2 year^–1) (SD = 0.57 kgC m^–1/5 years) with MC, 0.63 kgC m^–1/5 years (0.126 kgC m^–2 year^–1) (SD = 0.37 kgC m^–1/5 years) with JC and 0.58 kgC m^–2 year^–1 (SD = 0.29 kgC m^–1/5 years) with AEI for the studied plots. We found that the MODIS NPP product has a clear relation with both the NPP estimates obtained by using MC (R² = 0.34, root mean square error (RMSE) = 1.51 kgC m^–1/5 years) and JC (R² = 0.32, RMSE = 1.73 kgC m^–1/5 years). In addition to that, the relation between MOD17A3 product and AEI-derived NPP is relatively strong (R² = 0.48, RMSE = 0.26 kgC m^–2 year^–1). We discuss possible reasons for these trade-offs among different methods. This study lays out a new approach to validate coarse scale MODIS product using field data directly, including for highly heterogeneous areas.     

Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for large-area crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral-temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region's major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop's multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state's climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season.     

Evaluation of annual forest disturbance monitoring using a static decision tree approach and 250 m MODIS data

Research on change detected has largely focused on method development and evaluation in a temporally dependent manner where training and validation data are from the same temporal period. Monitoring over several change periods needs to account for increased variability resulting from possible combinations of atmosphere, sensor, and surface conditions. Training a change method for each monitoring period (i.e. a dynamic approach) is an option, but can be costly to develop the needed training datasets and may not be warranted if sufficient accuracy can be obtained without retraining (i.e. a static approach). In this research the potential of change detection using a static approach suitable for near-real time annual monitoring was evaluated. The research assessed the influence of feature set size, radiometric normalization, incorporation of temporal information, and change object size and sub-pixel fraction on accuracy. The static approach was based on a decision tree developed using 250 m MODIS data from 2005 to 2006 and applied annually for the period 2001-2005. Change results between years were combined and compared to reference data representing change from 2001 to 2005 to evaluate monitoring performance. Results revealed high accuracy for the decision tree change model development from 2005 to 2006 (bootstrap cross-validation KAPPA = 0.91), with lower accuracy (KAPPA = 0.80) when applied for monitoring from 2001 to 2005. Radiometric normalization increased monitoring accuracy (KAPPA = 0.86). Further improvement was achieved with the incorporation of temporal contextual tests to combine the 2001-2005 inter-annual change maps (KAPPA = 0.90), but required a time lag of 1 year. An alternative temporal test that was not restricted by the 1 year time lag produced slightly lower accuracy (KAPPA = 0.88). Evaluation of the effect of object size on detection accuracy showed that accuracy for objects less than 7 pixels was strongly related to object size, with objects less than 3 pixels having low detection rates. The effect of sub-pixel change fraction was found to be dependent on object size with larger objects reducing detection error across the range of fractions evaluated.     

Tree density estimation in a tropical woodland ecosystem with multiangular MISR and MODIS data

In this paper we evaluate the potential of spectral, temporal and angular aspect of remotely sensed data for quantitative extraction of forest structure information in tropical woodlands. Moderate resolution imaging spectroradiometer (MODIS) multispectral data at 500-meter spatial resolution from different dates, multiangle imaging spectroradiometer (MISR) bidirectional reflectance factors (BRF) and normalized difference angular index (NDAI) derived from MISR data at 275-meter spatial resolution were used as input data. The number of trees per hectare bigger than 20cm in diameter at breast height was taken as variable of interest. Simple and multiple ordinary least square regressions and artificial neural networks (ANN) were tested to understand the relationships between the various sources of remotely sensed data and the output variable. An experimental design technique, followed by a classification of the input variables and a factor analysis were implemented in order to understand the structure, reduce the dimensionality of the data and avoid the overfitting of the neural network. The results show that there is a significant amount of independent information in the angular dimension, and this information is highly relevant to the estimation of tree densities in the study area. The MISR NDAI indexes improved the performance of the MISR BRF. The non-linear ANN outperformed the linear regressions. The best results were obtained with the ANN after selecting the input variables according to the results of the experimental design, the classification and the factor analysis, with a 0.71 correlation coefficient against the 0.58 of the best linear regression model.     

View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data

Imaging spectrometry has the potential to provide improved discrimination of crop types and better estimates of crop yield. Here we investigate the potential of Hyperion to discriminate three Brazilian soybean varieties and to evaluate the relationship between grain yield and 17 narrow-band vegetation indices. Hyperion analysis focused on two datasets acquired from opposite off-nadir viewing directions but similar solar geometry: one acquired on 08 February 2005 (forward scattering) and the other on 14 January 2006 (back scattering). In 2005, the soybean canopies were observed by Hyperion at later reproductive stages than in 2006. Additional Hyperion datasets were not available due to cloud cover. To further examine the impact of viewing geometry within the same season, Hyperion data were complemented by 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) images (bands 1 and 2) acquired in consecutive days (05-06 February 2005) with opposite viewing geometries (− 42° and + 44°, respectively). MODIS data analysis was used to keep reproductive stage as a constant factor while isolating the impact of viewing geometry. For discrimination purposes, multiple discriminant analysis (MDA) was applied over each dataset using surface reflectance values as input variables and a stepwise procedure for band selection. All possible Hyperion band ratios and the 17 narrow-band vegetation indices with soybean grain yield were evaluated across years through Pearson's correlation coefficients and linear regression. MODIS-derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were evaluated within the same growing season. Results showed that: (1) the three soybean varieties were discriminated with highest accuracy in the back scattering direction, as deduced from MDA classification results from Hyperion and MODIS data; (2) the highest correlation between Hyperion vegetation indices and soybean yield was observed for the Normalized Difference Water Index (NDWI) (r = + 0.74) in the back scattering direction and this result was consistent with band ratio analysis; (3) higher Hyperion correlation results were observed in the back scattering direction when compared to the forward scattering image. For the same reproductive stage, stronger shadowing effects were observed over the MODIS red band in the forward scattering direction producing lower and lesser variable reflectance for the sensor. As a result, the relationship between MODIS-derived NDVI and soybean yield improved from the forward (r of + 0.21) to the back scattering view (r of + 0.60). The same trend was observed for SR that increased from + 0.22 to + 0.58.