Moderate Resolution Imaging Spectroradiometer Land Cover Research Articles

China’s Land Cover Fraction Change during 2001–2015 Based on Remote Sensed Data Fusion between MCD12 and CCI-LC

New types of remote sensed land cover datasets provide key evidence for understanding global environmental change. However, low data consistency makes understanding the changes unclear. China has become a hot spot of land cover change in the world due to climate change and a series of human measures, such as ecological engineering, land consolidation, and urbanization. However, due to the inconsistencies in interpretation of signs and thresholds, the understanding of yearly-continued land cover changes in China is still unclear. We aim to produce China’s land cover fraction dataset from 2001 to 2015 by weighted consistency analysis. We compare the Moderate-resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC) datasets, and a new land cover fraction dataset named China-LCFMCD-CCI, produced with a 1 km resolution. The obvious increased forest areas only accounted for 4.6% of the total forest areas, and were mainly distributed in northeast China. Approximately 75.8% of the grassland and shrubland areas decreased in size, and these areas were relatively concentrated in northeast and south China. The obvious increased areas of cropland (3.7%) were equal to the obvious decreased areas (3.6%), and the increased cropland areas were in northwest China. The change in bare land was not obvious, as the obvious increased areas only accounted for 0.75% of the bare land areas. The results not only prove that the data fusion of the weighted consistency method is feasible to form a land cover fraction dataset, but also helps to fully reveal the trends in land cover fraction change in China.

Momentum Drag Effect Over Urbanized Areas in the ACM2 PBL Component of the WRF model

AbstractMesoscale meteorology models, such as the Weather Research and Forecasting (WRF) model, typically use land use categorization data to specify surface characteristics by grid cells. In commonly used land use schemes, such as the 24‐class U.S. Geological Survey land use classification system or the Moderate Resolution Imaging Spectroradiometer landcover product, cities are placed in single urban categories that are characterized by a high roughness length. Monin‐Obunkov similarity theory is commonly used to simulate boundary layer dynamics based on surface energy and momentum fluxes which resulted in an overprediction of wind speed in weather forecasts for urban areas. It is thus crucial to include the momentum drag effects of the buildings, including walls and roofs, in the prediction models. The WRF model contains several momentum flux parameterizations, including the single‐layer urban canopy model, building effect parameterization, and building energy model. The building energy model and building effect parameterization schemes are limited to the Mellor‐Yamada‐Janjic or BouLac planetary boundary layer models and are computationally costly. In this study, a realistic multilayer urban approach is introduced into the Asymmetric Convective Model version 2 planetary boundary layer component of the WRF model. Sensitivity tests are applied to a range of urban morphological parameters in idealized simulations, and the impact of building morphology on atmospheric conditions is examined. The modified Asymmetric Convective Model version 2 is implemented in two real cases in the forecast mode, resulting in significant improvements in wind speed prediction.

Improvement of Countrywide Vegetation Mapping over Japan and Comparison to Existing Maps

This paper presents an improved classification and mapping of vegetation types for all of Japan by utilizing the Moderate-resolution Imaging Spectroradiometer (MODIS) data. The Nadir BRDF-Adjusted Reflectance (MCD43A4 product) data were compared to the conventional Surface Reflectance (MOD09A1/MOY09A1 products) data for the classification of vegetation types: evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest, deciduous broadleaf forest, shrubs, herbaceous, arable; and non-vegetation. Very rich spectral and temporal features were prepared from MCD43A4 and MOD09A1/MOY09A1 products. Random Forests classifier was employed for the classification of vegetation types with the support of ground truth data prepared in the research. Accuracy metrics—confusion matrix, overall accuracy, and kappa coefficient calculated through 10-fold cross-validation approach—were used for quantitative comparison of MCD43A4 and MOD09A1/MOY09A1 products. The cross-validation results indicated better performance of the MCD43A4 (Overall accuracy = 0.73; Kappa coefficient = 0.69) product than conventional MOD09A1/MOY09A1 products (Overall accuracy = 0.70; Kappa coefficient = 0.66) for the classification. McNemar’s test was also used to confirm a significant difference (p-value = 0.0003) between MCD43A4 and MOD09A1/MOY09A1 products. Based on these results, by utilizing the MCD43A4 features, a new vegetation map was produced for all of Japan. The newly produced map showed better accuracy than the extant, MODIS Land Cover Type product (MCD12Q1) and Global Land Cover by National Mapping Organizations (GLCNMO) product in Japan.

Development of S-NPP VIIRS global surface type classification map using support vector machines

ABSTRACTWith the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), such as the global land cover map, have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this study, an improved algorithm including the use of a new classifier support vector machines (SVM) classifier was proposed to produce VIIRS surface type maps. In addition to the new classification algorithm, a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented. As a result, the new global International Geosphere-Biosphere Programme (IGBP) map based on the 2014 VIIRS surface reflectance data was generated with a 78.5 ± 0.6% overall classification accuracy. The new map was compared to a previously delivered VIIRS surface type map, and to the MODIS land cover product. Validation results including the error matrix, overall accuracy, and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map, with higher accuracy achieved in agricultural types.

The response of ecosystem service value to land-use change in the upper Zhang River region from 2001 to 2013

Purpose This study aims to analyze the changes in the ecosystem service value (ESV) in response to land use and contribute significantly to ecological construction and sustainable development. Design/methodology/approach The present study was conducted in the upper Zhanghe River region based on the moderate-resolution imaging spectroradiometer Land Cover Type product MCD12Q1 by using geographic information system (GIS) methods to process and re-classify the land-use data and using the Chinese ESV equivalent weight factors to investigate changes in land use and resulting changes in ESV between 2001 and 2013. Findings The results showed significant fluctuations in ESV between 2001 and 2013: there was a decline in ESV from 2001 to 2004, followed by a gradual rise after 2004, and the overall ESV exceeded 2001 levels by the end of 2013. However, the pattern of ESV change differed across geographic locations, and each administrative region contributed differently to the overall trend. The analysis confirmed that the land-use change was closely related to the change in its ESV, and the coefficients of sensitivity of ESV for all types of land use were less than one, indicating that the coefficient value of ESV lacked elasticity. Research limitations/implications Therefore, to promote sustainable development in the upper Zhanghe River region, ESV should be taken into consideration when planning land use, especially for land types with high ESV, such as water bodies and forestlands. Originality/value The results can provide scientific support for the sustainable development of the ecological, economic and societal aspects of the upper Zhanghe River region. In addition, county-level administrative divisions were set as the basic research unit for the analysis and discussion of ESV changes in each unit within the research period and its impact on the overall ESV of the entire area to lay down a foundation for the analysis of the ESV spatial dynamic distribution in the entire research site.

Comparison of regional and global land cover products and the implications for biogenic emission modeling

Accurate estimates of biogenic emissions are required for air quality models that support the development of air quality management plans and attainment demonstrations. Land cover characterization is an essential driving input for most biogenic emissions models. This work contrasted the global Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product against a regional land cover product developed for the Texas Commissions on Environmental Quality (TCEQ) over four climate regions in eastern Texas, where biogenic emissions comprise a large fraction of the total inventory of volatile organic compounds (VOCs) and land cover is highly diverse. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) was utilized to investigate the influences of land cover characterization on modeled isoprene and monoterpene emissions through changes in the standard emission potential and emission activity factor, both separately and simultaneously. In Central Texas, forest coverage was significantly lower in the MODIS land cover product relative to the TCEQ data, which resulted in substantially lower estimates of isoprene and monoterpene emissions by as much as 90%. Differences in predicted isoprene and monoterpene emissions associated with variability in land cover characterization were primarily caused by differences in the standard emission potential, which is dependent on plant functional type. Photochemical modeling was conducted to investigate the effects of differences in estimated biogenic emissions associated with land cover characterization on predicted ozone concentrations using the Comprehensive Air Quality Model with Extensions (CAMx). Mean differences in maximum daily average 8-hour (MDA8) ozone concentrations were 2 to 6 ppb with maximum differences exceeding 20 ppb. Continued focus should be on reducing uncertainties in the representation of land cover through field validation.Implications: Uncertainties in the estimation of biogenic emissions associated with the characterization of land cover in global and regional data products were examined in eastern Texas. Misclassification between trees and low-growing vegetation in central Texas resulted in substantial differences in isoprene and monoterpene emission estimates and predicted ground-level ozone concentrations. Results from this study indicate the importance of land cover validation at regional scales.

Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees

Conducting quantitative studies on the carbon balance or productivity of oil palm is important for understanding the role of this ecosystem in global climate change. The MOD17 algorithm is used for processing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to generate the values of gross primary productivity (GPP) and net primary productivity for input to global carbon cycle modelling. In view of the increasing importance of data on carbon sequestration at regional and national levels, we have studied one important factor affecting the accuracy of the implementation of MOD17 at the sub-global level, namely the database of MODIS land cover (MOD12Q1) used by MOD17. By using a study area of approximately 7 km × 7 km (49 MODIS pixels) in semi-rural Johor in Peninsular Malaysia and using Google Earth 0.75 m resolution images as ground data, we found that the land-cover type for only 16 of these 49 MODIS pixels was correctly identified by MOD12Q1 using its 1 km resolution land-cover database. This leads to errors of 24% to 50% in the maximum light use efficiency, leading to corresponding errors of 24% to 50% in the GPP. We show that by using the Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC) land-cover database developed by Gong et al., this particular error can be essentially eliminated, but at the cost of using extra computing resources.

Assessing MODIS Land Cover Products over China with Probability of Interannual Change

AbstractAccurate and up-to-date land cover data are important for climate-change modeling. Quality assessment is becoming critical, as many satellite-based land cover products of differing scales have been released to meet the needs of scientific studies. In this study, the authors assessed the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products by analyzing the probability of interannual change from 2001 to 2012. The authors found that, cumulatively, 43.0% of MODIS land cover had changed over China from 2001 to 2012 at least once. Of this percentage, 12.1% was considered unreasonable change, 6.1% was considered reasonable change, and areas of confusion accounted for about 24.8%, giving rise to great uncertainty in the products. MODIS Collection 51 products clearly have less uncertainty than the Collection 5 products. Areas of reasonable change occurred in transition zones of ecological, biophysical, and climate gradients, while areas of unreasonable change appeared in heterogeneous ...

On the upstream inputs into the MODIS primary productivity products using biometric data from oil palm plantations

This study evaluated the influence of upstream inputs into the Moderate Resolution Imaging Spectroradiometer (MODIS) primary productivity products, termed the MOD17, at tropical oil palm plantations (Elaeis guineensis Jacq.). Evaluation of MOD17 using oil palm plantations as test sites is ideal because the plantations are cultivated on large areas which are comparable with the size of MODIS pixels. It is difficult to find test sites covered by other single species in a whole pixel. The upstream inputs studied included (1) MODIS land cover, (2) the National Centers for Environmental Prediction–Department of Energy (NCEP-DOE) Reanalysis 2 meteorological data set, (3) MODIS leaf area index/fraction of photosynthetically active radiation (LAI/fPAR), and (4) MODIS maximum light-use efficiency (maximum LUE). Oil palm biometric and local meteorological data were utilized as ground data. Furthermore, scaling up oil palm LAI and fPAR from plot scale to regional scale (Peninsular Malaysia) was done empirically by correlating oil palm LAI derived from the hemispherical photography technique with radiance information from the Disaster Monitoring Constellation 2 satellite (UK-DMC 2). The upscaled LAI/fPAR developed in this study was used to evaluate the MODIS LAI/fPAR. The results showed that the MODIS land-cover product has an overall accuracy of 78.8% when compared to the Peninsular Malaysia land-use map produced by the Department of Agriculture, Malaysia. Regarding the NCEP-DOE Reanalysis 2 data set, vapour pressure deficit (VPD) and photosynthetically active radiation (PAR) contain large uncertainties in our study area. However, MODIS LAI and fPAR were correlated relatively well with the upscaled LAI (R2 = 0.50) and the upscaled fPAR (R2 = 0.60), respectively. The constant values of maximum LUE for croplands and evergreen broadleaf forest ecosystems are lower than the maximum LUE of oil palm. The relative predictive error assessment showed that the MOD17 net primary productivity (NPP) overestimated oil palm NPP derived from biometric methods by 142–204%. We replaced the upstream inputs of MOD17 by the local inputs for estimating oil palm GPP and NPP in Peninsular Malaysia. This was done by (1) assigning maximum LUE for oil palm plantations as a constant at 1.68 g C m−2 day−1, (2) utilizing meteorological data from local meteorological stations, and (3) using the upscaled fPAR of oil palm plantations. The amount of oil palm GPP and NPP for Peninsular Malaysia in 2010 were estimated to be ~0.09 Pg C year−1 (or equivalent to ~0.33 Pg CO2 year−1) and ~0.03 Pg C year−1 (~0.11 Pg CO2 year−1), respectively, indicating that oil palm plantations in Peninsular Malaysia can play an important role in global carbon sequestration. In the future there is likely to be a demand for MODIS GPP and NPP products that are more accurate than those currently generated by MOD17. We recommend future developments of the MOD17 processing system to allow improvements in the upstream input parameters, in the manner described in this article, both for global processing and for the production of more accurate values for GPP and NPP at regional and local scales.

Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery

Conducting quantitative studies on the carbon balance or productivity of oil palm is important in understanding the role of this ecosystem in global climate change. In this study, we evaluated the accuracy of MODIS (Moderate Resolution Imaging Spectroradiometer) annual gross primary productivity (GPP) (the product termed MOD-17) and its upstream products, especially the MODIS land cover product (the product termed MOD-12). We used high-resolution Google Earth images to classify the land cover classes and their percentage cover within each 1 km spatial resolution MODIS pixel. We used field-based annual GPP for 2006 to estimate GPP for each pixel based on percentage cover. Both land cover and GPP were then compared to MODIS land cover and GPP products. The results show that for pure pixels that are 100% covered by mature oil palm trees, the RMSE (root mean square error) between MODIS and field-based annual GPP is 18%, and that this is increased to 27% for pixels containing mostly oil palm. Overall, for an area of about 42 km2 the RMSE is 26%. We conclude that land cover classification (at 1 km resolution) is one of the main factors for the discrepancy between MODIS and field-based GPP. We also conclude that the accuracy of the MODIS GPP product could be improved significantly by using higher-resolution land cover maps.

Dynamics of vegetation indices in tropical and subtropical savannas defined by ecoregions and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover

In this study, we explored the capacity of vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products to characterize global savannas in Australia, Africa and South America. The savannas were spatially defined and subdivided using the World Wildlife Fund (WWF) global ecoregions and MODIS land cover classes. Average annual profiles of Normalized Difference Vegetation Index, shortwave infrared ratio (SWIR32), White Sky Albedo (WSA) and the Structural Scattering Index (SSI) were created. Metrics derived from average annual profiles of vegetation indices were used to classify savanna ecoregions. The response spaces between vegetation indices were used to examine the potential to derive structural and fractional cover measures. The ecoregions showed distinct temporal profiles and formed groups with similar structural properties, including higher levels of woody vegetation, similar forest–savanna mixtures and similar grassland predominance. The potential benefits from the use of combinations of indices to characterize savannas are discussed.

Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.

Forest Cover Classification With MODIS Images in Northeastern Asia

The forest ecosystem in the Northeastern Asia (NEA) has been undergoing dramatic changes because of forest fires and massive logging. MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indexes product MOD13A1 from 2000 to 2006 were re-composited for forest mapping circa year 2000 in the region. The study region was divided into four sub-regions with distinct natural climate regimes, and a TM/ETM+ scene was selected as a test site for each of the sub-regions. The process of mapping forest from MODIS data consists of two steps that follow the logic sequence of class definition. First, a 2-D Feature Space Grid Split (FSGS) algorithm was developed to identify forested areas by use of its dark object attributes. The producer and user accuracies of forest/non-forest mapping reached over 90% at test sites, and 72.22% and 88.26% comparing with the national LCLU map in the areas within China. The forested areas were then stratified into four forest types by a decision tree classifier from temporal MODIS data for each of the sub-regions. The forest classification was validated for pure forests using the results from TM/ETM+ classification. The comparison showed high producer and user accuracies: 86.78% and 91.14% for evergreen needle forest, 90.6% and 92.4% for deciduous needle forest, and 82.99% and 97.19% for deciduous broadleaf forest, although confusion existed between mixed forest and deciduous broadleaf forest. The forest map was also compared with MODIS land cover and Global Land Cover 2000 (GLC2000) products.

Satellite‐based near‐real‐time estimation of irrigated crop water consumption

Estimation of evapotranspiration using satellite sensors offers the potential for improved water management in irrigated areas. However, previous applications of remote sensing to estimate crop water use have been retrospective in nature, whereas the spatial resolution of sensors that provide information in real time has been thought to be too coarse for such purposes. We describe application of a variation of the Moderate Resolution Imaging Spectroradiometer (MODIS) standard evapotranspiration algorithm for near‐real‐time method for estimation of actual evapotranspiration based entirely on satellite data. The latency of the approach is typically 3 days to 1 week, although the lag could be reduced. The method works best over areas where there is substantial diversity in vegetation types within the remote sensing window; the contrast in vegetation between irrigated and adjacent unirrigated areas meets this requirement well. The satellite sensors/products are MODIS land cover (MOD12Q1), surface reflectance (MOD09GQ), vegetation indices (MOD13Q1), land surface temperature/emissivity (MOD11A1), and albedo (MCD43A3) and NOAA/NESDIS surface radiation budget (SRB) products derived from the Geostationary Operational Environmental Satellites. The MODIS/SRB evapotranspiration estimates agree favorably with ground flux tower observations and evapotranspiration estimates from a much higher resolution Landsat‐based (METRIC) method over irrigated areas of the Klamath River Basin, with instantaneous evapotranspiration biases less than 10% and daily evapotranspiration biases less than 15%. There is a tendency for the MODIS/SRB approach to underestimate, and for the METRIC‐based algorithm to overestimate, seasonal evapotranspiration relative to the tower flux observations.

Land cover in East Asia classified using Terra MODIS and DMSP OLS products

Land cover is classified over East Asia using 250‐m Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance, MODIS snow cover and Operational Linescan System (OLS) human settlement data. The classification method includes a decision tree classification scheme that considers 11 kinds of land surface features derived from the OLS product and the time series of two MODIS products in 2000. The decision tree was defined manually based on the experiment because of insufficient training data, ease of tuning by visual interpretation, and extensibility to further research. The resulting classification is compared to three kinds of reference data, i.e. MODIS land cover product, Chinese digital land cover map, and Chinese census. The land cover classification can be input into a hydrological model applied to the Yellow River in China.

Effects of Implementing MODIS Land Cover and Albedo in MM5 at Two Contrasting U.S. Regions

Abstract This study implements a new land-cover classification and surface albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and investigates its effects on regional near-surface atmospheric state variables as well as the planetary boundary layer evolution for two dissimilar U.S. regions. Surface parameter datasets are determined by translating the 17-category MODIS classes into the U.S. Geological Survey (USGS) and Simple Biosphere (SiB) categories available for use in MM5. Changes in land-cover specification or associated parameters affected surface wind, temperature, and humidity fields, which, in turn, resulted in perceivable alterations in the evolving structure of the planetary boundary layer. Inclusion of the MODIS albedo into the simulations enhanced these impacts further. Area-averaged comparisons with ground measurements showed remarkable improvements in near-surface temperature and humidity at both study areas when MM5 is initialized with MODIS land-cover and albedo data. Influence of both MODIS surface datasets is more significant at a semiarid location in the southwest of the United States than it is in a humid location in the mid-Atlantic region. Intense summertime surface heating at the semiarid location creates favorable conditions for strong land surface forcing. For example, when the simulations include MODIS land cover and MODIS albedo, respective error reduction rates were 6% and 11% in temperature and 2% and 2.5% in humidity in the southwest of the United States. Error reduction rates in near-surface atmospheric fields are considered important in the design of mesoscale weather simulations.

The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA's Earth Observing System

One of the products to be derived from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) will identify locations where land cover changes attributable to human activities are occurring. The product aims to serve as an alarm where rapid land cover conversion can subsequently be analysed with higher resolution sensors such as Landsat 7. This paper describes the production procedure and change detection algorithms for the 250 m MODIS land cover change product. Multiple change detection algorithms, including three spectral methods and two texture methods, are utilized to generate the product from the two 250 m MODIS bands. The change detection methods are implemented with look-up tables (LUTs) which are initially generated using data from the Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic Mapper (TM) until MODIS data become available. The results from the five methods are combined to improve confidence in the product. We test the performance of each method against several sets of simulated MODIS data derived from Landsat TM image pairs. The test data represent tropical deforestation, agricultural expansion and urbanization. The commission errors of the five methods and the combination are approximately 10%, with reasonable omission errors less than 25%.