MODIS Land Cover Research Articles

Human modification of land cover alters net primary productivity, species richness and their relationship

AbstractAimHumans have altered ecosystem productivity and biodiversity worldwide by changing land‐cover types and management. High local species richness is commonly found in geographic areas and ecosystems with high net primary productivity (NPP), but the long‐term effects of modification on productivity and biodiversity change, and particularly on the relationship between the two, are poorly understood. Here we evaluate whether human modification tends to increase biodiversity in low‐productivity ecosystems (where human management is likely to increase productivity) and decrease biodiversity in ecosystems that were originally high‐productive.LocationGlobal.Time Period2001–2013.Major Taxa StudiedPlants, mammals, birds, hexapods, arachnids, other terrestrial invertebrates, amphibians, reptiles and fungi.MethodsWe assembled a large worldwide dataset of NPP and associated species richness from MODIS land cover and NPP products and the PREDICTS biodiversity database, involving 11,849 sites. This enabled comparisons of species richness and NPP differences between samples of relatively natural and human‐modified vegetation within the same geographic regions, considering 102 types of land‐cover transitions.Results(1) Modification from non‐forest vegetation to forests on average increased NPP by 7.76%, and vice versa. (2) Human modification of less productive areas tended to increase NPP and vice versa, with stronger effects of major modification. (3) However, site‐level species richness decreases were associated with nearly all land‐cover transitions from relatively natural to modified vegetation. (4) Despite expectations, we found no significant relationship between species richness differences (between relatively natural and modified vegetation) and productivity differences, when considering conversions across land‐cover types and excluding human‐appropriated productivity in croplands.Main ConclusionsHuman modification tends to increase NPP in low‐productivity ecosystems, but species richness declines are associated with most major human‐induced land‐cover changes. Therefore, increasing or decreasing NPP did not generate corresponding increases or decreases in species richness, contrary to expectations of the general species richness‐productivity relationship.

A cloud-integrated GIS for forest cover loss and land use change monitoring using statistical methods and geospatial technology over northern Algeria

Over the last two decades, forest cover has experienced significant impacts from fires and deforestation worldwide due to direct human activities and climate change. This paper assesses trends in forest cover loss and land use and land cover changes in northern Algeria between 2000 and 2020 using datasets extracted from Google Earth Engine (GEE), such as the Hanssen Global Forest Change and MODIS Land Cover Type products (MCD12Q1). Classification was performed using the pixel-based supervised machine-learning algorithm called Random Forest (RF). Trends were analyzed using methods such as Mann–Kendall and Sen. The study area comprises 17 basins with high rainfall variability. The results indicated that the forest area decreased by 64.96%, from 3718 to 1266 km2, during the 2000–2020 period, while the barren area increased by 40%, from 134,777 to 188,748 km2. The findings revealed that the Constantinois-Seybousse-Mellegue hydrographic basin was the most affected by deforestation and cover loss, exceeding 50% (with an area of 1018 km2), while the Seybouse River basin experienced the highest percentage of cover loss at 40%. Nonparametric tests showed that seven river basins (41%) had significantly increasing trends of forest cover loss. According to the obtained results, the forest loss situation in Algeria, especially in the northeastern part, is very alarming and requires an exceptional and urgent plan to protect forests and the ecological system against wildfires and climate change. The study provides a diagnosis that should encourage better protection and management of forest cover in Algeria.

Effect of Land Use and Land Cover Change on the Changes in Net Primary Productivity in Karst Areas of Southwest China: A Case Study of Huanjiang Maonan Autonomous County

Karst areas in southwest China have experienced significant land cover and land use change (LUCC) due to utilization for human activity and a comprehensive rocky desertification control project (RDCP) since 2008. It is important to quantify the effect of LUCC on ecosystem productivity in this region for assessing the overall benefit of this ecological restoration project. In this study, we used using MODIS land cover and NPP products to investigate the relative contribution of LUCC to the change in net primary productivity (NPP) during 2008–2013 in Huanjiang County, one of first one hundred pilot counties to implement RDCP. Our results show that NPP increased in 95.53% of the county, and the average growth of NPP in non-rocky desertification area was higher than in rocky desertification or potential rocky desertification areas. LUCC has an important contribution (25.23%) to the NPP increase in the county, especially in the LUCC area (70.97%), which increased the average NPP by 3.9% and 10.5%, respectively. Across the six RDCP regions in the county, the average increase in NPP for the vegetation restoration measure of governed karst area is significantly greater than in the ungoverned karst area, and the positive change in NPP increased with the increasing implementation area of the vegetation restoration measure.

Environmental controls on Landsat‐derived phenoregions across an East African megatransect

AbstractSemiarid and savanna‐type (SAST) ecosystems in East Africa have unique plant species compositions and characteristics that make quantifying this biome's seasonality and interannual variability difficult. Phenoregion classification offers a way to use seasonality of vegetation growth to help understand the phenological spatial patterns of complex landscapes. Here, we used Normalized Difference Vegetation Index (NDVI) time series from Landsat 8 to map phenoregions in scenes centered around national parks from Mt. Kenya National Park (Kenya) to Limpopo National Park (Mozambique). We then assessed whether landscape‐scale controls on phenology are consistent across the region or whether they vary across this latitudinal gradient. We compared our phenoregion maps to MODIS Land Cover and geology, and we used multinomial logistic regression to determine the role that elevation, slope, aspect, and geology play in driving phenological differences. The sites' phenoregions showed no unique land cover composition, suggesting that MODIS land cover does not capture the subtle variations identified in phenoregion analysis. Multinomial logistic regression showed that geographic trend (x‐ and y‐directions) was a strong predictor in four of the five landscapes and that, depending on the scene, geology, elevation, or aspect was a strong secondary predictor. Using seasonality of the NDVI time series to generate phenoregions provides different and, in some cases, more ecologically relevant information, compared to vegetation maps that use only land cover from a single season or time period to generate ecoregions. With a significant population of humans and animals that live in and depend on SAST ecosystems, it is important to better understand vegetation processes and factors that affect them as climate change becomes an increasingly pertinent issue in dry systems.

Urbanization effects on vegetation cover in major African cities during 2001-2017

Urbanization effects on vegetation cover (VC) have been analyzed in many regions. However, little attention has been paid to Africa, which has undergone rapid urbanization in recent decades. In this study, MODIS land cover and enhanced vegetation index (EVI) data were used to examine urbanization effects on VC in 59 large cities in Africa during 2001–2017. The ΔEVI (urban EVI minus rural EVI) was used to represent urbanization effects on VC. Major findings include: (1) for 59 cities averaged, annual ΔEVI averaged from 2015 to 2017 was -0.116. Negative annual ΔEVI (i.e. urban EVI lower than rural) were observed in 56 of 59 cities. (2) For 59 cities averaged, urban area increased 17.9% from 2001 (262.8 km2) to 2016 (309.8 km2). (3) Annual ΔEVI decreased significantly (p<0.05) in 44 of 59 cities for the period 2001–2017, and annual average area of urbanization effects on VC increased significantly in 40 of 59 cities. For 59 cities averaged, the percentage of urban area with significant decreasing trends of annual ΔEVI was 60.0%. Spatially, cities near the Gulf of Guinea showed more significant decreasing ΔEVI than cities in other regions. In addition, the trends and spatial distributions of urbanization effects on VC differed little by seasons. These results suggested that urbanization and its effects on VC in Africa should arouse more attentions.

Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification

Classification is a fundamental process in remote sensing used to relate pixel values to land cover classes present on the surface. Over large areas land cover classification is challenging particularly due to the cost and difficulty of collecting representative training data that enable classifiers to be consistent and locally reliable. A novel methodology to classify large volume Landsat data using high quality training data derived from the 500m MODIS land cover product is demonstrated and used to generate a 30m land cover classification for all of North America between 20°N and 50°N. Publically available 30m global monthly Web-enabled Landsat Data (GWELD) products generated from every available Landsat 7 ETM+ and Landsat 5 TM image for a three year period, that are defined aligned to the MODIS land products and are consistently pre-processed data (cloud-screened, saturation flagged, atmospherically corrected and normalized to nadir BRDF adjusted reflectance), were classified. The MODIS 500m land cover product was filtered judiciously, using only good quality pixels that did not change land cover class in 2009, 2010 or 2011, followed by automated selection of spatially corresponding 30m GWELD temporal metric values, to define a large training data set sampled across North America. The training data were sampled so that the class proportions were the same as the North America MODIS land cover product class proportions and corresponded to 1% of the 500m and <0.005% of the 30m pixels. Thirty nine GWELD temporal metrics for every 30m North America pixel location were classified using (a) a single random forest, and (b) a locally adaptive method with a random forest classifier derived and applied locally and the classification results spatially mosaicked together. The land cover classification results appeared geographically plausible and at synoptic scale were similar to the MODIS land cover product. Detailed visual inspection revealed that the locally adaptive random forest classifications and associated classification confidences were generally more coherent than the single random forest classification results. The level of agreement between the 30m classifications and the MODIS land cover product derived training data was assessed by bootstrapping the random forest implementation. The locally adaptive random forest classification had higher overall agreement (95.44%, 0.9443 kappa) than the single random forest classification (93.13%, 0.9195 kappa). The paper concludes with a discussion of future research including the potential for automated global land cover classification.

MODIS land cover uncertainty in regional climate simulations

MODIS land cover datasets are used extensively across the climate modeling community, but inherent uncertainties and associated propagating impacts are rarely discussed. This paper modeled uncertainties embedded within the annual MODIS Land Cover Type (MCD12Q1) products and propagated these uncertainties through the Regional Atmospheric Modeling System (RAMS). First, land cover uncertainties were modeled using pixel-based trajectory analyses from a time series of MCD12Q1 for Urumqi, China. Second, alternative land cover maps were produced based on these categorical uncertainties and passed into RAMS. Finally, simulations from RAMS were analyzed temporally and spatially to reveal impacts. Our study found that MCD12Q1 struggles to discriminate between grasslands and croplands or grasslands and barren in this study area. Such categorical uncertainties have significant impacts on regional climate model outputs. All climate variables examined demonstrated impact across the various regions, with latent heat flux affected most with a magnitude of 4.32 W/m2 in domain average. Impacted areas were spatially connected to locations of greater land cover uncertainty. Both biophysical characteristics and soil moisture settings in regard to land cover types contribute to the variations among simulations. These results indicate that formal land cover uncertainty analysis should be included in MCD12Q1-fed climate modeling as a routine procedure.

A new water-resistant snow index for the detection and mapping of snow cover on a global scale

ABSTRACTAn up-to-date spatio-temporal change analysis of global snow cover is essential for better understanding of climate–hydrological interactions. The normalized difference snow index (NDSI) is a widely used algorithm for the detection and estimation of snow cover. However, NDSI cannot discriminate between snow cover and water bodies without use of an external water mask. A stand-alone methodology for robust detection and mapping of global snow cover is presented by avoiding external dependency on the water mask. A new spectral index called water-resistant snow index (WSI) with the capability of exhibiting significant contrast between snow cover and other cover types, including water bodies, was developed. WSI uses the normalized difference between the value and hue obtained by transforming red, green, and blue, (RGB) colour composite images comprising red, green, and near-infrared bands into a hue, saturation, and value (HSV) colour model. The superiority of WSI over NDSI is confirmed by case studies conducted in major snow regions globally. Snow cover was mapped by considering monthly variation in snow cover and availability of satellite data at the global scale. A snow cover map for the year 2013 was produced at the global scale by applying the random walker algorithm in the WSI image supported by the reference data collected from permanent snow-covered and non-snow-covered areas. The resultant snow-cover map was compared to snow cover estimated by existing maps: MODIS Land Cover Type Product (MCD12Q1 v5.1, 2012), Global Land Cover by National Mapping Organizations (GLCNMO v2.0, 2008), and European Space Agency’s GlobCover 2009. A significant variation in snow cover as estimated by different maps was noted, and was was attributed to methodological differences rather than annual variation in snow cover. The resultant map was also validated with reference data, with 89.46% overall accuracy obtained. The WSI proposed in the research is expected to be suitable for seasonal and annual change analysis of global snow cover.

Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis

As an important indicator of anthropogenic impacts on the Earth’s surface, it is of great necessity to accurately map large-scale urbanized areas for various science and policy applications. Although spectral mixture analysis (SMA) can provide spatial distribution and quantitative fractions for better representations of urban areas, this technique is rarely explored with 1-km resolution imagery. This is due mainly to the absence of image endmembers associated with the mixed pixel problem. Consequently, as the most profound source of error in SMA, endmember variability has rarely been considered with coarse resolution imagery. These issues can be acute for fractional land cover mapping due to the significant spectral variations of numerous land covers across a large study area. To solve these two problems, a hierarchically object-based SMA (HOBSMA) was developed (1) to extrapolate local endmembers for regional spectral library construction; and (2) to incorporate endmember variability into linear spectral unmixing of MODIS 1-km imagery for large-scale impervious surface abundance mapping. Results show that by integrating spatial constraints from object-based image segments and endmember extrapolation techniques into multiple endmember SMA (MESMA) of coarse resolution imagery, HOBSMA improves the discriminations between urban impervious surfaces and other land covers with well-known spectral confusions (e.g., bare soil and water), and particularly provides satisfactory representations of urban fringe areas and small settlements. HOBSMA yields promising abundance results at the km-level scale with relatively high precision and small bias, which considerably outperforms the traditional simple mixing model and the aggregated MODIS land cover classification product.

Mapping Biophysical Parameters for Land Surface Modeling over the Continental US Using MODIS and Landsat

In terms of the space cities occupy, urbanization appears as a minor land transformation. However, it permanently modifies land’s ecological functions, altering its carbon, energy, and water fluxes. It is therefore necessary to develop a land cover characterization at fine spatial and temporal scales to capture urbanization’s effects on surface fluxes. We develop a series of biophysical vegetation parameters such as the fraction of photosynthetically active radiation, leaf area index, vegetation greenness fraction, and roughness length over the continental US using MODIS and Landsat products for 2001. A 13-class land cover map was developed at a climate modeling grid (CMG) merging the 500 m MODIS land cover and the 30 m impervious surface area from the National Land Cover Database. The landscape subgrid heterogeneity was preserved using fractions of each class from the 500 m and 30 m into the CMG. Biophysical parameters were computed using the 8-day composite Normalized Difference Vegetation Index produced by the North American Carbon Program. In addition to urban impact assessments, this dataset is useful for the computation of surface fluxes in land, vegetation, and urban models and is expected to be widely used in different land cover and land use change applications.

Assessment of Coarse-Resolution Land Cover Products Using CASI Hyperspectral Data in an Arid Zone in Northwestern China

The accuracy of different coarse-resolution land cover products is an important consideration for product users at the regional or global scale, and different evaluation methods inevitably result in discrepancies in accuracy for the same land cover product. The remote sensing community has responded to this increased interest by improving methodologies for more accurately evaluating the correctness of land cover information. In this study, a pixel-based hierarchical classification strategy followed by an object-based classification method was applied to compact airborne spectrographic imager (CASI) hyperspectral data in order to produce highly accurate, high spatial resolution classification reference data. Some aspects of the fuzzy/conventional evaluation of MODIS land cover (MODISLC) (500 m) and GlobCover (300 m) data based on sub-pixel class fractions derived from high spatial resolution reference data at different thematic resolutions are also discussed. Relationships between homogeneity and fuzzy accuracy for two land cover products were obtained at different thematic resolutions. Additionally, the influences on the relationship resulting from the thematic resolution were also studied, and these are reported in this paper. Attempts were made to establish fuzzy/conventional evaluation rules for fuzzy classes, and the different performances of the fuzzy and conventional evaluations for hard/fuzzy labels were compared. The adjusted GlobCover accuracy after theoretical removal of the effect caused by spatial resolution was calculated based on the relationship between homogeneity and accuracy; the result was a higher accuracy than for MODISLC at the same thematic resolution. In addition, the different performance characteristics of the relationships between homogeneity and adjusted GlobCover accuracy/MODISLC accuracy at different thematic resolutions were compared and analyzed over the area where the CASI transects were obtained.

Land cover dynamics monitoring with Landsat data in Kunming, China: a cost-effective sampling and modelling scheme using Google Earth imagery and random forests

Changes in forest composition impact ecological services, and are considered important factors driving global climate change. A hybrid sampling method along with a modelling approach to map current and past land cover in Kunming, China is reported. MODIS land cover (2001–2011) data-sets were used to detect pixels with no apparent change. Around 3000 ‘no change points’ were systematically selected and sampled using Google Earth’s high-resolution imagery. Thirty-five per cent of these points were verified and used for training and validation. We used Random forests to classify multi-temporal Landsat imagery. Results show that forest cover has had a net decrease of 14385 ha (1.3% of forest area), which was primary converted to shrublands (11%), urban and barren land (2.7%) and agriculture (2.5%). Our validation indicates an overall accuracy (Kappa) of 82%. Our methodology can be used to consistently map the dynamics of land cover change in similar areas with minimum costs.

Incorporating Sub-Dominant Classes in the Accuracy Assessment of Large-Area Land Cover Products: Application to GlobCover, MODISLC, GLC2000 and CORINE in Spain

Various global land cover (LC) datasets have been produced from remote sensing data in response to the need for information about LC. Nevertheless, the potential use of global LC products is often very limited by the lack of detailed accuracy information at regional to national level. This paper proposes a methodology for performing accuracy assessment of large-area LC products, which takes into account a number of factors arising from intrinsic characteristics of LC, such as thematic uncertainty that results from the partial overlap in legend definitions and lack of homogeneity within reference and classification data. The approach compares the LC pixel label not only with the dominant reference label but also with sub-dominant LC types within the extent of the sampling unit. The methodology was illustrated in Spain using four LC datasets (GlobCover, MODIS land cover (MODISLC), GLC2000 and CORINE). The variety of reference label data offered by a detailed national database, namely SIOSE, supported several different fuzzy agreement definitions in order to derive unbiased estimates of accuracy measures. CORINE followed by GLC2000 showed the highest accuracy scores, whereas GlobCover and MODISLC showed the lowest scores. Nevertheless, the fuzzy approach revealed that a great amount of disagreement in GlobCover and MODISLC datasets does not actually correspond to classification errors, but it can be associated to legend ambiguity and mixed coverage pixels.

Evapotranspiration and water yield over China's landmass from 2000 to 2010

Abstract. Terrestrial carbon and water cycles are interactively linked at various spatial and temporal scales. Evapotranspiration (ET) plays a key role in the terrestrial water cycle, altering carbon sequestration of terrestrial ecosystems. The study of ET and its response to climate and vegetation changes is critical in China because water availability is a limiting factor for the functioning of terrestrial ecosystems in vast arid and semiarid regions. To constrain uncertainties in ET estimation, the process-based Boreal Ecosystem Productivity Simulator (BEPS) model was employed in conjunction with a newly developed leaf area index (LAI) data set, MODIS land cover, meteorological, and soil data to simulate daily ET and water yield at a spatial resolution of 500 m over China for the period from 2000 to 2010. The spatial and temporal variations of ET and water yield were analyzed. The influences of climatic factors (temperature and precipitation) and vegetation (land cover types and LAI) on these variations were assessed. Validations against ET measured at five ChinaFLUX sites showed that the BEPS model was able to simulate daily and annual ET well at site scales. Simulated annual ET exhibited a distinguishable southeast to northwest decreasing gradient, corresponding to climate conditions and vegetation types. It increased with the increase of LAI in 74% of China's landmass and was positively correlated with temperature in most areas of southwest, south, east, and central China. The correlation between annual ET and precipitation was positive in the arid and semiarid areas of northwest and north China, but negative in the Tibetan Plateau and humid southeast China. The national annual ET varied from 345.5 mm in 2001 to 387.8 mm in 2005, with an average of 369.8 mm during the study period. The overall rate of increase, 1.7 mm yr−1 (R2 = 0.18, p = 0.19), was mainly driven by the increase of total ET in forests. During 2006–2009, precipitation and LAI decreased widely and consequently caused a detectable decrease in national total ET. Annual ET increased over 62.2% of China's landmass, especially in the cropland areas of the southern Haihe River basin, most of the Huaihe River basin, and the southeastern Yangtze River basin. It decreased in parts of northeast, north, northwest, south China, especially in eastern Qinghai-Tibetan Plateau, the south of Yunnan Province, and Hainan Province. Reduction in precipitation and increase in ET caused vast regions in China, especially the regions south of Yangtze River, to experience significant decreases in water yield, while some sporadically distributed areas experienced increases in water yield. This study shows that the terrestrial water cycles in China's terrestrial ecosystems appear to have been intensified by recent climatic variability and human induced vegetation changes.

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Global land cover data could provide continuously updated cropland acreage and distribution information, which is essential to a wide range of applications over large geographical regions. Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products: MODIS land cover (MODISLC) at 500-m resolution in 2010, GlobCover at 300-m resolution in 2009, FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data. Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products, which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation (MDev) and RMSE, but were less effective on GlobCover product. We found that, in the USA, the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage, while FROM-GLC-agg gave the least deviation from the survey at the state level. Correlation between land cover map estimates and survey estimates is significant, but stronger at the state level than at the county level. In regions where most mismatches happen at the county level, MODIS tends to underestimate, whereas MERIS and Landsat images incline to overestimate. Those uncertainties should be taken into consideration in relevant applications. Excluding interannual and seasonal effects, R2 of the FROM-GLC regression model increased from 0.1 to 0.4, and the slope is much closer to one. Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.

Integrating global land cover products for improved forest cover characterization: an application in North America

Six widely used coarse-resolution global land cover data-sets – Global Land Cover Characterization (GLCC), Global Land Cover 2000 (GLC2000), GlobCover land cover product (GlobCover), MODIS land cover product (MODIS LC), the University of Maryland land cover product (UMD LC), and the MODIS Vegetation Continuous Fields tree cover layer (MODIS VCF) disagree substantially in their estimates of forest cover. Employing a regression tree model trained on higher-resolution, Landsat-based data, these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America. Evaluated using a withheld test sample, the integrated percent forest cover (IPFC) data-set has a root mean square error of 11.75% – substantially better than the 17.37% of GLCC, 17.61% of GLC2000, 17.96% of GlobCover, 15.23% of MODIS LC, 19.25% of MODIS VCF, and 15.15% of UMD LC, respectively. Although demonstrated for forest, this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.

MODIS Product를 활용한 FDB 속성 갱신 대상지역 선정 연구

FDB(Feature DataBase) attributes have been produced by using the resource data prior to the year 2002. Due to this reason, the attributes need to be updated to the up-to-date ones. In this regards, this study focuses on the way of finding areas whose attributes need to be updated. Forest and crop classes were chosen as target classes among FDB features. MODIS Landcover data and FDB are, first, compared to detect the changed forest and crop areas from 2001 to 2008. Then, vegetation vitality changes are analyzed using MODIS annual NDVI data. Based on the change detection and the vegetation vitality analysis, the index of area selection for updating FDB attributes is proposed in this study.Keywords : FDB, MODIS(모디스), NDVI(정규식생지수), Vegetation Vitality(식생활력도), Attributes Update(속성 갱신)

Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe

One of the major drawbacks of land cover products is the lack of interoperability among them. Since their development was driven by different national or international initiatives, they were developed for different purposes and hold diverse technical characteristics. Thus, comparison among products and quality monitoring is necessary in assessing their usefulness. This paper provides a methodology to compare global land cover maps that allows for differences in legend definitions among products.Two different approaches were considered for map comparison, a Boolean approach and a new methodology based on fuzzy set theory in which the Land Cover Classification System (LCCS) acted as a general bridging system. The fuzzy approach let us reconcile legends in terms of a set of nine selected attributes. The methodology is illustrated over Europe using four different land cover products: CORINE, GLC2000, MODIS land cover (MODISLC) and GlobCover.Overall accuracies between datasets based on a generalized nomenclature ranged from 35% (GlobCover–MODISLC) to 57% (CORINE–GLC2000). A further assessment based on a flexible Boolean comparison allowed us to minimize part of the uncertainty introduced by ambiguity of legends, resulting in an overall increase of agreement of around 10% in absolute terms. Reduction of positional errors produced an agreement increase of around 5%. The fuzzy approach allowed us to examine certain criteria used in classification systems to better understand the causes of discrepancies among datasets and express them in terms of relevant land attributes, irrespective of class names. This method served to highlight the consistency of the land cover maps under consideration, especially for datasets that showed higher divergences. In particular, it allows the identification of areas that are very close in land attributes, even when a Boolean comparison shows them as different.

A Comparative Analysis of the MODIS Land Cover Data Sets and Globcover Land Cover Data Sets in Heilongjiang Basin

A Comparative Analysis of the MODIS Land Cover Data Sets and Globcover Land Cover Data Sets in Heilongjiang Basin

Global Land Surface Temperature Retrieval with AMSR-E Brightness Temperature and MODIS Land Cover Type Products

AMSR-E brightness temperature data and MODIS land cover type data are combined to retrieval land surface temperature(LST).Using the MODIS data,we classified the global land surface into 16 types,and make assumption that in each land surface type,the emissivity of each channel is constant separately.Based on this assumption and the land surface types,we build land surface retrieval algorithms for each land surface types.Before the algorithms were built,we chose the pixels that with above 90% single land type area and pixels that the MODIS land surface temperature exceed 273 K.This is because the emissivity is dubious in snow cover,ice cover,and mixed-pixels.Coequally,considering the precipitation effect,we winkled the rainfall pixel from our dataset.Finally,we employed the algorithms to retrieval global land surface temperature of Jan to Oct,2004.Compared with the MODIS land surface temperature product,the result is fine,with RMSE of 2~4 K.