Moderate Resolution Imaging Spectroradiometer Land Cover Product Research Articles

A time series of land cover maps of South Asia from 2001 to 2015 generated using AVHRR GIMMS NDVI3g data.

In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted.

Spatio-temporal distribution of burned areas by ecoregions in Mexico and Central America

ABSTRACTFire activity in Mexico and Central America, and its associated emissions, has impacts across multiple scales. On the local-to-regional scale, fire activity impacts land use, productivity, and biodiversity. On the regional-to-global scale, fire activity impacts hydrological, biogeochemical, and atmospheric processes. A consistent, reliable, large-scale characterization of the spatial and temporal distribution of fire burned area is required to assess its environmental impacts and to support natural resources’ management. The spatial and temporal distributions of fire burned areas in ecoregions of Mexico and Central America are evaluated in this study for the period 2001–2014, using the satellite Moderate Resolution Imaging Spectroradiometer (MODIS) MCD45 Burned Area data set. The methodology combines the 500 m burned area product with a MODIS land cover product and a map of North American land cover to calculate the spatiotemporal variability of fire activity as a function of land-use type.The total burned area over Mexico and Central America over the period 2001–2014 was found to be 614,243.5 km2, but with significant interannual variability over the 14 years included in the study. Indeed, the minimum burned area over the period was 9892.25 km2 in 2014 and the maximum was 37,669.50 km2 in 2011, a fourfold increase. Burned areas were found to be concentrated in northern Mexico and on the Pacific coast, mainly from October to June. Agricultural burned areas accounted for 37% and 43% of total detected burns in Mexico and Central America, respectively. The largest extent of burned surface occurs in May for most land-cover types. The maximum density of burned areas occurred in the tropical dry forests ecoregion during the dry season. Both in Mexico and Central America, burned area anomalies have significant anti-correlation with precipitation anomalies.

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.

Impact of Land-Use and Land-Cover Change on urban air quality in representative cities of China

The atmospheric particulate pollution in China is getting worse. Land-Use and Land-Cover Change (LUCC) is a key factor that affects atmospheric particulate pollution. Understanding the response of particulate pollution to LUCC is necessary for environmental protection. Eight representative cities in China, Qingdao, Jinan, Zhengzhou, Xi’an, Lanzhou, Zhangye, Jiuquan, and Urumqi were selected to analyze the relationship between particulate pollution and LUCC. The MODIS (MODerate-resolution Imaging Spectroradiometer) aerosol product (MOD04) was used to estimate atmospheric particulate pollution for nearly 10 years, from 2001 to 2010. Six land-use types, water, woodland, grassland, cultivated land, urban, and unused land, were obtained from the MODIS land cover product (MOD12), where the LUCC of each category was estimated. The response of particulate pollution to LUCC was analyzed from the above mentioned two types of data. Moreover, the impacts of time-lag and urban type changes on particulate pollution were also considered. Analysis results showed that due to natural factors, or human activities such as urban sprawl or deforestation, etc., the response of particulate pollution to LUCC shows obvious differences in different areas. The correlation between particulate pollution and LUCC is lower in coastal areas but higher in inland areas. The dominant factor affecting urban air quality in LUCC changes from ocean, to woodland, to urban land, and eventually into grassland or unused land when moving from the coast to inland China.

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.

Identification of Dust Hot Spots from Multi-Resolution Remotely Sensed Data in Eastern China and Mongolia

Aeolian dust from hot spots in eastern China and Mongolia can be carried downwind to provinces in China, neighboring countries, the Pacific islands, and cities far beyond the source region. Although dust sources of huge extent have been identified in several countries, few effective countermeasures are available to combat dust emissions in arid regions. We analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) images (1 km spatial resolution) that captured dust emission and dispersion during 2000–2013 to determine dust sources in eastern China and Mongolia. MODIS level 1B data and the brightness temperature difference (BTD) algorithm provided efficient discrimination of dust in this study. The derived dust information, in conjunction with the MODIS land cover product (1 km spatial resolution) and high-resolution Landsat data (30 m spatial resolution; Landsat 8, Operational Land Imager sensor) were used to identify the locations and specific sources of dust. Dust emissions appear to be sporadic in time and space, controlled by both environmental factors and human activity, although past studies have indicated that many dust emissions are from consistently active hot spots. Analysis of MODIS data indicated that three subregions of the eastern China and Mongolia source region are the dominant sources of dust: Horqin Sandy Land, Otintag Sandy Land, and the southeastern Mongolian Gobi; each of these subregions contains dust emission hot spots. We identified the locations of consistent hot spots and verified that some individual dust emissions originated from those hot spots. Our data also indicated that hot spots in southeastern Mongolia have migrated northward since 2006. Our study showed that hot spots such as dry lakes, river beds, mines, and croplands contribute to dust emissions in the eastern China and Mongolia source region. Dust hot spots coincide with regions of expanding industry in Otintag Sandy Land and in some areas of the Mongolian Gobi and with agricultural areas in Horqin Sandy Land and in some parts of the Mongolian Gobi. In Horqin and Otintag sandy lands, dust sources are associated with ephemeral water bodies. Water conservation can be an important countermeasure for initial dust emissions in the Horqin Sandy Land. In the Otintag Sandy Land, attention should be paid to human activities, for example, minimizing the effects of mining disturbances, improving dust suppression in industrial areas, and controlling water use by industry. In Mongolia, protective farming techniques and water conservation in dust emitting basins, and dust suppression and water resource protection in mining zones, must be considered to combat dust emission. MODIS level 1B data can be used to locate dust hot spots and to identify future sources of dust entrainment. Dust hot spots identified from MODIS level 1B data provide small-scale information about dust emission that can be used to locate pollution hot spots, increase understanding of the global dust cycle, and improve dust modeling.

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.

Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery

The fraction of photosynthetically active radiation (FPAR) absorbed by a vegetation canopy is an important variable for global vegetation modelling and is operationally available from data of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor starting from the year 2000. Product validation is ongoing and important for constant product improvement, but few studies have investigated the specific accuracy of MODIS FPAR using in situ measurements and none have focused on agricultural areas. This study therefore presents a validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in western Uzbekistan. High-resolution FPAR maps were compiled via linear regression between in situ FPAR measurements and the RapidEye normalized difference vegetation index (NDVI) for the 2009 season. The data were aggregated to the MODIS scale for comparison. Data on the percentage cover of agricultural crops per MODIS pixel allowed investigation of the impact of spatial heterogeneity on MODIS FPAR accuracy. Overall, the collection 5 MODIS FPAR overestimated RapidEye FPAR between approximately 6% and 15%. MODIS quality flags, the underlying biome classification and spatial heterogeneity were investigated as potential sources of error. MODIS data quality was very good in all cases. A comparison of the MODIS land-cover product with high-resolution land-use classification revealed a significant misclassification by MODIS. Yet, we found that the overestimation of MODIS FPAR is independent of classification accuracy. The results indicate that the amount of background information, present even in the most homogeneous pixels (∼70% crop cover), is most likely the reason for the overestimation. The behaviour of pure pixels could not be investigated due to a lack of appropriate pixels.

Generation and analysis of the 2005 land cover map for Mexico using 250 m MODIS data

Land cover plays a key role in global to regional monitoring and modeling because it affects and is being affected by climate change and thus became one of the essential variables for climate change studies. National and international organizations require timely and accurate land cover information for reporting and management actions. The North American Land Change Monitoring System (NALCMS) is an international cooperation of organizations and entities of Canada, the United States, and Mexico to map land cover change of North America's changing environment. This paper presents the methodology to derive the land cover map of Mexico for the year 2005 which was integrated in the NALCMS continental map. Based on a time series of 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data and an extensive sample data base the complexity of the Mexican landscape required a specific approach to reflect land cover heterogeneity. To estimate the proportion of each land cover class for every pixel several decision tree classifications were combined to obtain class membership maps which were finally converted to a discrete map accompanied by a confidence estimate. The map yielded an overall accuracy of 82.5% (Kappa of 0.79) for pixels with at least 50% map confidence (71.3% of the data). An additional assessment with 780 randomly stratified samples and primary and alternative calls in the reference data to account for ambiguity indicated 83.4% overall accuracy (Kappa of 0.80). A high agreement of 83.6% for all pixels and 92.6% for pixels with a map confidence of more than 50% was found for the comparison between the land cover maps of 2005 and 2006. Further wall-to-wall comparisons to related land cover maps resulted in 56.6% agreement with the MODIS land cover product and a congruence of 49.5 with Globcover.

Integrating geographical data and phenological characteristics derived from MODIS data for improving land cover mapping

The study developed a feasible method for large-area land cover mapping with combination of geographical data and phenological characteristics, taking Northeast China (NEC) as the study area. First, with the monthly average of precipitation and temperature datasets, the spatial clustering method was used to divide the NEC into four ecoclimate regions. For each ecoclimate region, geographical variables (annual mean precipitation and temperature, elevation, slope and aspect) were combined with phenological variables derived from the moderate resolution imaging spectroradiometer (MODIS) data (enhanced vegetation index (EVI) and land surface water index (LSWI)), which were taken as input variables of land cover classification. Decision Tree (DT) classifiers were then performed to produce land cover maps for each region. Finally, four resultant land cover maps were mosaicked for the entire NEC (NEC_MODIS), and the land use and land cover data of NEC (NEC_LULC) interpreted from Landsat-TM images was used to evaluate the NEC_MODIS and MODIS land cover product (MODIS_IGBP) in terms of areal and spatial agreement. The results showed that the phenological information derived from EVI and LSWI time series well discriminated land cover classes in NEC, and the overall accuracy was significantly improved by 5.29% with addition of geographical variables. Compared with NEC_LULC for seven aggregation classes, the area errors of NEC_MODIS were much smaller and more stable than that of MODIS_IGBP for most of classes, and the wall-to-wall spatial comparisons at pixel level indicated that NEC_MODIS agreed with NEC_LULC for 71.26% of the NEC, whereas only 62.16% for MODIS_IGBP. The good performance of NEC_MODIS demonstrates that the methodology developed in the study has great potential for timely and detailed land cover mapping in temperate and boreal regions.

Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images

Global maps of land cover and leaf area index (LAI) from the moderate resolution imaging spectroradiometer (MODIS) are available from the earth resources observation system data center. Validation of these MODIS terrestrial products is crucial for scientific research. Numerous pre- and postlaunch validation activities have been completed during the last several years. However, such work was mainly located in North America, Europe, and Africa. The validated vegetation types were principally focused on woodland and savanna. In this paper, we evaluated the Collection 3 MODIS land cover (MOD12Q1) and the Collection 4 MODIS LAI (MOD15A2) products in the North China Plain (NCP). We placed the emphasis on croplands by: (1) developing an accurate land cover map, based on the MODIS LAI/fraction of photosynthetically active radiation land cover classification scheme; (2) deriving empirical LAI validation maps, based on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ </i> measurement and landsat thematic mapper imagery; and (3) conducting an initial MODIS validation exercise to assess the quality of MODIS products. The results indicate that an apparent misclassification exists between grasses/cereal crop and broadleaf crop biomes in the MODIS land cover product. Although MODIS LAI could resemble the general pattern of the LAI seasonal vegetation variation in the NCP, the MODIS LAI algorithm markedly underestimated LAI values (down to 2-3 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for the April 2004 winter wheat cropland. Biome misclassification and the atmospheric effect of cloud or aerosol were proven to decrease the value of MODIS LAI, but these impacts cannot fully explain the significant underestimation. Additional studies are needed to understand the main reasons for this underestimation in the study area.

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.

Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles, France

This paper presents results of validating the Collection 4 Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product using LAI data collected in a 3 × 3 km agricultural (grasses and cereal crops) area near Avignon, France, and 30 m resolution Enhanced Thematic Mapper (ETM+) image. Estimates of the accuracy, precision, and uncertainty with which the ETM+ data convey information about LAI underlie the derivation of a 30 m resolution reference LAI map by accounting for both field measurement and satellite observation errors. The 30 m reference LAI was then extrapolated from sampling points to a 58 km2 area without loss in the quality and was degraded to a 1 km resolution LAI map. The latter was taken as a reference to assess the quality of the MODIS LAI product. Comparison of the reference and corresponding MODIS retrievals suggests that Collection 4 MODIS LAI is accurate to within an accuracy of 0.3 with a precision and uncertainty of 0.23 and 0.38, respectively. It was found that the Collection 3 MODIS land cover product, input to the Collection 4 operational LAI algorithm, misclassified the 58 km2 area as broadleaf crops. The use of correct biome type in the operational processing improves the accuracy in LAI by a factor of 2 with an almost unchanged precision and uncertainty. Our results also indicate that the retrieval of LAI from satellite data is an ill‐posed problem; that is, small variations in input due to observation errors result in a very low precision of the desired parameter. Any retrieval technique based on a simple model inversion or empirical relationships is unable to generate stable retrievals. The use of information on input errors in the retrieval technique is necessary to generate solutions to the ill‐posed problem. The MODIS operational LAI algorithm meets this requirement.