Use of pixel- and plot-scale screening variables to validate MODIS GPP predictions with Forest Inventory and Analysis NPP measures across the eastern USA

Moderate Resolution Imaging Spectroradiometer (MODIS) estimates of gross primary production (GPP) were validated using field-based estimates of net primary production from the Forest Inventory and Analysis (FIA) Program across the eastern USA. A total of 54 969 MODIS pixels and co-located FIA plots were analysed to validate MODIS GPP estimates. We used a data resolution of individual MODIS pixels and co-located FIA plots, and used detailed pixel- and plot-specific attributes by applying screening variables (SVs) to assess conditions under which MODIS GPP was most strongly validated. Eight SVs were used to test six hypotheses about the conditions under which MODIS GPP would be most strongly validated. The six hypotheses addressed were (1) MODIS pixel quality checks, (2) FIA plot quality checks, (3) land-cover classification comparability of co-located MODIS pixels and FIA plots, (4) FIA plot homogeneity, (5) FIA plot tree density and (6) MODIS seasonal variation. SVs were assessed in terms of trade-off between improved relations and reduced number of samples. MODIS seasonal variation and FIA plot tree density were the two most efficient SVs, followed by basic quality checks for each data set. Sequential application of SVs indicated that combined usage of five of the eight SVs provided an efficient data set of 17 090 co-located MODIS pixels and FIA plots, which raised the Pearson correlation coefficient from 0.01 for the Complete data set of 54 969 plots to 0.48 for this screened subset of 17 090 plots. The screened subset of plots exhibited good representation of the Complete data set in terms of species abundance, plot distribution and mean productivity. We conclude that the application of SVs provides a useful approach to ensure compatibility of two data sets for broad-scale forest carbon budget analysis and monitoring.     

Estimation of gross primary production in Moso bamboo forest based on light-use efficiency derived from MODIS reflectance data

Assessing the contribution of Moso bamboo (Phyllostachys pubescens) forest to forest ecosystem carbon storage requires accurate estimation of gross primary production (GPP). Based on measurements of light-use efficiency (LUE), defined as the ratio of measured GPP to photosynthetically active radiation (PAR), from the eddy covariance flux tower, the linear regression model and partial least squares regression model were used for estimation of LUE using the Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance data. GPP estimates were then calculated by the product of LUE estimates and PAR (named the LUE-PAR model), which was compared with GPP from the GPP algorithm designed for the MODIS sensor aboard the Aqua and Terra platforms (MOD17A2 model) and the EC-LUE model. The results revealed the PLS model performed better than the linear regression model in LUE estimation but had lager uncertainties in high and low LUE values. GPP estimates driven by a MODIS-based radiation product with high spatial resolution was more accurate than those driven by Modern-Era Retrospective Analysis for Research and Applications (MERRA) radiation product from the NASA’s Global Modelling and Assimilation Office data set. The LUE-PAR model had the highest accuracy than the other two LUE models. The GPP values derived from the EC-LUE model driven by photosynthetically active radiation (PAR) from MERRA and maximum LUE from the EC data were overestimated due to the overestimation in MERRA radiation product. The GPP values derived from the MOD17A2 model driven by PAR from the MERRA and maximum LUE from the biome properties look-up table were underestimated due to underestimation in the maximum LUE of Moso bamboo forest. This study implied that the LUE-PAR model driven by LUE estimates from the PLS model and PAR from MERRA is a superior approach in improving GPP simulations, and PAR products with high spatial resolution and accurate species-specific maximum LUE are necessary for the LUE models in estimating GPP at regional scale.     

Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data

In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004-2005 for the conterminous U.S. We then estimated and analyzed the regional water-use efficiency (WUE) based on the developed ET and MODIS gross primary production (GPP) for the region. We first trained the ANNs to predict evapotranspiration fraction (EF) based on the data at 28 AmeriFlux sites between 2003 and 2005. Five remotely-sensed variables including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), leaf area index (LAI) and photosynthetically active radiation (PAR) and ground-measured air temperature and wind velocity were used. The daily ET was calculated by multiplying net radiation flux derived from remote sensing products with EF. We then evaluated the model performance by comparing modeled ET with the data at 24 AmeriFlux sites in 2006. We found that the ANNs predicted daily ET well (R² = 0.52-0.86). The ANNs were applied to predict the spatial and temporal distributions of daily ET for the conterminous U.S. in 2004 and 2005. The ecosystem WUE for the conterminous U.S. from 2004 to 2005 was calculated using MODIS GPP products (MOD17) and the estimated ET. We found that all ecosystems' WUE-drought relationships showed a two-stage pattern. Specifically, WUE increased when the intensity of drought was moderate; WUE tended to decrease under severe drought. These findings are consistent with the observations that WUE does not monotonously increase in response to water stress. Our study suggests a new water-use efficiency mechanism should be considered in ecosystem modeling. In addition, this study provides a high spatial and temporal resolution ET dataset, an important product for climate change and hydrological cycling studies for the MODIS era.     

Estimation of potential forest productivity across the Oregon transect using satellite data and monthly weather records

Detailed physiological and micrometeorological studies have provided new insights that greatly simplify the prediction of gross photosynthesis ( P G ) and the fraction of production that goes into above-ground net primary production (NPPA). These simplifications have been incorporated into a process-based forest growth model called 3-PGS (Physiological Principles Predicting Growth with Satellite Data). Running the model requires only monthly weather data, an estimate of soil texture and rooting depth, quantum efficiency ( f ), and a satellitederived Normalized Difference Vegetation Index (NDVI) correlated with the fraction of visible light intercepted by foliage. The model was originally tested in Australia where seasonal variation in NDVI is extreme. In Oregon, NDVI varies much less seasonally and fully stocked coniferous stands maintain nearly constant canopy greenness throughout the year. We compared 3-PGS estimates of P G and NPPA across a steep environmental gradient in western Oregon where groundbased measurements at six sites were available from previous studies. We first tested the simplification in data acquisition of assigning the same quantum efficiency ( f =0.04 mol C/MJ APAR) and available soil water storage capacity ( θ =226 mm) to all sites. With these two variables fixed, the linear relation between predicted and measured P G was y =1.45 x +2.4 with an r 2 =0.85. When values of θ were adjusted to match seasonal measurements of predawn water potentials more closely, and the quantum efficiency was increased to 0.05 mol C/MJ absorbed photosynthetically active radiation (APAR) on the most productive site, predicted and observed values of P G and NPP A were in near 1:1 agreement with r 2 =0.92. Because maximum greenness (NDVI) reflects the seasonal availability of water, limits on soil water storage capacity can be inferred from calculated water balances derived following the onset of summer drought. The simplifications embedded in the 3-PGS model, along with the need to acquire only one midsummer estimate of maximum greenness, make the approach well suited for assessing the productive capacity of forest lands throughout the Pacific Northwest, USA.     

Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories

According to the IPCC GPG (Intergovernmental Panel on Climate Change, Good Practice Guidance), remote sensing methods are especially suitable for independent verification of the national LULUCF (Land Use, Land-Use Change, and Forestry) carbon pool estimates, particularly the aboveground biomass. In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3 ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m³ ha^− 1) and aboveground biomass (t ha^− 1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15 m × 15 m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250 m × 250 m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. The estimates obtained were significantly close to the district-level mean values provided by the Finnish National Forest Inventory; the relative RMSE was 9.9%. The use of finer spatial resolution data is an essential step to integrate ground measurements with coarse spatial resolution data. Furthermore, the use of standwise forest inventory data reduces co-registration errors and helps in solving the scaling problem between the datasets. The approach employed here can be used for estimating the stand volume and biomass, and as required independent verification data.     

An effective assessment protocol for continuous geospatial datasets of forest characteristics using USFS Forest Inventory and Analysis (FIA) data

Geospatial datasets of forest characteristics are modeled representations of real populations on the ground. The continuous spatial character of such datasets provides an incredible source of information at the landscape level for ecosystem research, policy analysis, and planning applications, all of which are critical for addressing current challenges related to climate change, urbanization pressures, and data requirements for monitoring carbon sequestration. However, the effectiveness of these applications is dependent upon the accuracy of the geospatial input datasets. A comprehensive set of robust measures is necessary to provide sufficient information to effectively assess the accuracy of these modeled geospatial datasets being produced. Yet challenges in the availability of reference data, in the appropriateness of assessment methods to dataset use, and in the completeness of assessment methods available have continued to hamper the timely and consistent application of map assessments. In this study we present a suite of assessments that can be used to characterize the accuracy of geospatial datasets of modeled continuous variables—an increasingly common format for modeling such attributes as proportion or probability of forestland as well as more traditionally continuous attributes such as leaf area index and forest biomass. It is a comparative accuracy assessment, in which each modeled dataset is compared to a set of reference data, recognizing both the potential for error in reference data, and probable differences in spatial support between the datasets. When used together, this proposed suite of assessments provides essential information on the type, magnitude, frequency and location of errors in each dataset. The assessments presented depend upon reference data with large sample sizes. The U.S. Forest Service (USFS) Forest Inventory and Analysis (FIA) database is introduced as an available reference dataset of sufficient sampling intensity to take full advantage of these assessments and facilitate their prompt application after modeled datasets are developed. We illustrate the application of this suite of assessments with two modeled datasets of forest biomass, in Minnesota and New York. The information provided by this suite of assessments substantially improves a user's ability to apply modeled geospatial datasets effectively and to assess the relative strengths and weaknesses of multiple datasets depicting the same forest characteristic.     

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (T_m) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI × T_m parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R² of 0.51 (p < 0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI × T_m correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R² = 0.68, p = 0.003) and the minimum land surface temperature (LST_min, R² = 0.81, p = 0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055 g C per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6 g C m^? 2 month^? 1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.     

Assessment of environmental impacts of river basin development on the riverine forests of eastern Kenya using multi-temporal satellite data

The utility of Landsat MSS (Multispectral Scanner) and SPOT XS data in monitoring the impacts of river basin development on a riverine forest located in the lower Tana River Basin of eastern Kenya was evaluated. Land cover change maps derived from Landsat MSS indicated little change in total forest area between 1975 and 1984. Land cover change maps derived from SPOT XS data indicated a 27% decline in forest area between 1989 and 1996. Mean patch size and area-perimeter ratio of the closed riverine forest remained virtually unchanged whereas these parameters for the open forest class decreased by 31% and 4% respectively. In addition, the average extent of the open riverine forest from the river channel declined by about 200 m between 1989 and 1996. This decline was attributed to decreased extent of floods along the floodplain following construction of dams in the upper river basin, and increased exploitation of the forests for fuelwood, especially in the vicinity of the established Bura Irrigation and Settlement Project. The greater lateral movement observed in the location of the river channel for the 1975-1985 period, compared to the 1985-1996 period, was also attributed to construction of dams in the upper river basin.     

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin,USA

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R ² = 0.90).     

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.     

Evaluation of forest fire risk by the analysis of environmental data and TM images

The evaluation of forest fire risk is an important issue in Mediterranean areas where the long arid season often creates favourable conditions for the occurrence of fires. In this Letter three indices related to this risk have been produced and compared for the western part of the Elba Island (Central Italy). The first index is based on the analysis of environmental information layers (topography, vegetation and soil type) within a Land Information System, while the other two are derived from a summer Landsat Thematic Mapper (TM) scene processed by unsupervised and supervised procedures, respectively. The results show the effectiveness of all these approaches, and, in particular, a greater accuracy of the supervised spectral index.     

A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.     

A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250 m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu.     

Assessment of forest disturbances by selective logging and forest fires in the Brazilian Amazon using Landsat data

The rapid environmental changes occurring in the Brazilian Amazon due to widespread deforestation have attracted the attention of the scientific community for several decades. A topic of particular interest involves the assessment of the combined impacts of selective logging and forest fires. Forest disturbances by selective logging and forest fires may vary in scale, from local to global changes, mostly related to the increase of carbon dioxide released into the atmosphere. Selective logging activities and forest fires have been reported by several studies as important agents of land-use and land-cover changes. Previous studies have focused on selective logging, but forest fires on a large scale in tropical regions have yet to be properly addressed. This study involved a more comprehensive investigation of temporal and basin-wide changes of forest disturbances by selective logging and forest fires using remotely sensed data acquired in 1992, 1996, and 1999. Landsat imagery and remote-sensing techniques for detecting burned forests and estimating forest canopy cover were applied. We also conducted rigorous ground measurements and observations to validate remote-sensing techniques and to assess canopy-cover impacts by selective logging and forest fires in three different states in the Brazilian Amazon. The results of this study showed a substantial increase in total forested areas impacted by selective logging and forest fires from approximately 11,800 to 35,600 km² in 1992 and 1999, respectively. Selective logging was responsible for 60.4% of this forest disturbance in the studied period. Approximately 33% and 7% of forest disturbances detected in the same period were due to impacts of forest fires only and selective logging and forest fires combined, respectively. Most of the degraded forests (～90%) were detected in the states of Mato Grosso and Pará. Our estimates indicated that approximately 5467, 7618, and 17437 km² were new areas of selective logging and/or forest fires in 1992, 1996, and 1999, respectively. Protected areas seemed to be very effective in constraining these types of forest degradation. Approximately 2.4% and 1.3% of the total detected selectively logged and burned forests, respectively, were geographically located within protected areas. We observed, however, an increasing trend for these anthropogenic activities to occur within the limits of protected areas from 1992 to 1999. Although forest fires impacted the least area of tropical forests in the study region, new areas of burned forests detected in 1996 and 1999 were responsible for the greatest impact on canopy cover, with an estimated canopy loss of 18.8% when compared to undisturbed forests. Selective logging and forest fires combined impacted even more those forest canopies, with an estimated canopy loss of 27.5%. Selectively logged forest only showed the least impact on canopy cover, with an estimated canopy loss of 5%. Finally, we observed that forest canopy cover impacted by selective logging activities can recover faster (up to 3 years) from impact when compared to those forests disturbed by fires (up to 5 years) in the Amazon region.     

On intra-annual EVI variability in the dry season of tropical forest: A case study with MODIS and hyperspectral data

Considerable controversy is associated with dry season increases in the Enhanced Vegetation Index (EVI), observed using the Moderate Resolution Imaging Spectroradiometer (MODIS), compared with field-based estimates of decreasing plant productivity. Here, we investigate potential causes of intra-annual variability by comparing EVI from mature forest with field-measured Leaf Area Index (LAI) to validate space-based observations. EVI was calculated from 19 nadir and off-nadir Hyperion images in the 2005 dry season, and inspected for consistency with MODIS observations from 2004 to 2009. The objective was to evaluate the possible influence of the view-illumination geometry and of canopy foliage and leaf flush on the EVI. Spectral mixture models were used to evaluate the relationship between EVI and the shade fraction, a measure that varies with pixel brightness. MODIS LAI values were compared with LAI estimated using hemispherical photographs taken in two field campaigns in the dry season. To keep LAI and leaf flush conditions as constant variables and vary solar illumination, we used airborne Hyperspectral Mapper (Hymap) data acquired over mature forest from another region on the same day but with two distinct solar zenith angles (SZA) (29° and 53°). Results showed that intra-annual variability in MODIS and nadir Hyperion EVI in the dry season of tropical forest were driven by solar illumination effects rather than changes in LAI. The reflectance of the MODIS and Hyperion blue, red and near infrared (NIR) bands was higher at the end of the dry season because of the predominance of sunlit canopy components for the sensors due to decreasing SZA from June (44°) to September (26°). Because EVI was highly correlated with the reflectance of the NIR band used to generate it (r of + 0.98 for MODIS and + 0.88 for Hyperion), this vegetation index followed the general NIR pattern, increasing with smaller SZA towards the end of the dry season. Hyperion EVI was inversely correlated with the shade fraction (r = − 0.93). Changes in canopy foliage detected from MODIS LAI data were not consistent with LAI estimates from hemispherical photographs. Although further research is necessary to measure the impact of leaf flush on intra-annual EVI variability in the Querência region, analysis of Hymap data with fixed LAI and leaf flush conditions confirmed the influence of the illumination effects on the EVI.     

An object-oriented daytime land-fog-detection approach based on the mean-shift and full lambda-schedule algorithms using EOS/MODIS data

A new algorithm is presented for land-fog detection using daytime imagery from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data. MODIS images constitute an ideal data source for fog detection due to their outstanding spatial and spectral resolution. In this article, a parameter named the Normalized Difference Fog Index (NDFI) is proposed, based on analysing the spectral character of fog and cloud by utilizing the Streamer radiative-transfer model and MODIS data. A mean-shift segmentation method is used to preliminary segment the NDFI image, and a full lambda-schedule algorithm is then iteratively applied to merge adjacent segments based on the combination of spectral and spatial information. Then, some properties (e.g. mean value of brightness temperature) are calculated for each segment, and each object is identified as either fog or not. The algorithm's performance is evaluated against ground-based measurements over China in winter, and the algorithm is proved to be effective in detecting fog accurately based on three cases.     

Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data

The overarching goal of this study was to map irrigated areas in the Ganges and Indus river basins using near-continuous time-series (8-day), 500-m resolution, 7-band MODIS land data for 2001-2002. A multitemporal analysis was conducted, based on a mega file of 294 wavebands, made from 42 MODIS images each of 7 bands. Complementary field data were gathered from 196 locations. The study began with the development of two cloud removal algorithms (CRAs) for MODIS 7-band reflectivity data, named: (a) blue-band minimum reflectivity threshold and (b) visible-band minimum reflectivity threshold.A series of innovative methods and approaches were introduced to analyze time-series MODIS data and consisted of: (a) brightness-greenness-wetness (BGW) RED-NIR 2-dimensional feature space (2-d FS) plots for each of the 42 dates, (b) end-member (spectral angle) analysis using RED-NIR single date (RN-SD) plots, (c) combining several RN-SDs in a single plot to develop RED-NIR multidate (RN-MDs) plots in order to help track changes in magnitude and direction of spectral classes in 2-d FS, (d) introduction of a unique concept of space-time spiral curves (ST-SCs) to continuously track class dynamics over time and space and to determine class separability at various time periods within and across seasons, and (e) to establish unique class signatures based on NDVI (CS-NDVI) and/or multiband reflectivity (CS-MBR), for each class, and demonstrate their intra- and inter-seasonal and intra- and inter-year characteristics. The results from these techniques and methods enabled us to gather precise information on onset-peak-senescence-duration of each irrigated and rainfed classes.The resulting 29 land use/land cover (LULC) map consisted of 6 unique irrigated area classes in the total study area of 133,021,156 ha within the Ganges and Indus basins. Of this, the net irrigated area was estimated as 33.08 million hectares—26.6% by canals and 73.4z5 by groundwater. Of the 33.08 Mha, 98.4% of the area was irrigated during khariff (Southwest monsoonal rainy season during June-October), 92.5% irrigated during Rabi (Northeast monsoonal rainy season during November-February), and only 3.5% continuously through the year.Quantitative Fuzzy Classification Accuracy Assessment (QFCAA) showed that the accuracies of the 29 classes varied from 56% to 100%—with 17 classes above 80% accurate and 23 classes above 70% accurate.The MODIS band 5 centered at 1240 nm provided the best separability in mapping irrigated area classes, followed by bands 2 (centered at 859 nm), 7 (2130 nm) and 6 (1640 nm).     

Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing

A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (?_n) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of ?_n suggested in this study can be used in grassland regions where no carbon flux measurements are accessible.     

Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser

Regression models relating variables derived from airborne laser scanning (ALS) to above-ground and below-ground biomass were estimated for 1395 sample plots in young and mature coniferous forest located in ten different areas within the boreal forest zone of Norway. The sample plots were measured as part of large-scale operational forest inventories. Four different ALS instruments were used and point density varied from 0.7 to 1.2 m^− 2. One variable related to canopy height and one related to canopy density were used as independent variables in the regressions. The statistical effects of area and age class were assessed by including dummy variables in the models. Tree species composition was treated as continuous variables. The proportion of explained variability was 88% for above- and 85% for below-ground biomass models. For given combinations of ALS-derived variables, the differences between the areas were up to 32% for above-ground biomass and 38% for below-ground biomass. The proportion of spruce had a significant impact on both the estimated models. The proportion of broadleaves had a significant effect on above-ground biomass only, while the effect of age class was significant only in the below-ground biomass model. Because of local effects on the biomass-ALS data relationships, it is indicated by this study that sample plots distributed over the entire area would be needed when using ALS for regional or national biomass monitoring.