Multi-temporal Moderate Resolution Imaging Spectroradiometer Data Research Articles

Drought monitoring in cultivated areas of Central America using multi-temporal MODIS data

ABSTRACTDrought is the most pressing problem facing farmers in Central America, and information on drought is thus crucial for agronomic planners to minimize impacts on crop production and food supply. This study assessed the cultivated areas affected by droughts using the Moderate Resolution Imaging Spectroradiometer (MODIS) data during 2001–2014, processed using a simple vegetation health index (VHI). The results, verified with the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation data and TVDI (temperature vegetation dryness index), indicated that the correlation coefficients (r) between the VHI and AMSR2 precipitation data for 2013 and 2014 were 0.81 and 0.78, respectively, and the values between VHI and TVDI were –0.68 and –0.61, respectively. The largest area of severe drought was especially observed for the 2014 primera season (April–August) over the last 14 years. The drought mapping results were aggregated with the cultivated areas for crop monitoring purposes.

A simple method for developing near real-time nationwide forest monitoring for Indonesia using MODIS near- and shortwave infrared bands

ABSTRACTRegarding to the deforestation and forest degradation issue in Indonesia, it is an urgent need to develop near real-time forest monitoring that can be seen by the public to make sure the transparency of forest resources management. Remote-sensing technology seems to be a powerful tool for monitoring and assessing the changes in forest cover immediately. We explored 250 m multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data sets to assign a change of temporal land surface dynamics based on two indices; normalized difference vegetation index (NDVI) and open area index (OAI). The data sets were filtered by two filtering approaches, median moving window and linear interpolation, in order to reduce the overall noise so as not to lose useful information from the time-series data. Our results indicated that the use of MODIS data as a basic information in the near real-time system offers great promise to detect the forest cover change in Indonesia’s forestland, since about 90.07% of area assigned to be a change area had actually changed. Meanwhile, about 34.97% of the forest cover change was not assigned to be a change area by the system. This result showed the need to evaluate the threshold in detecting forest cover change. Although the threshold issue is quite problematic in forest cover change detection, the results show that the methodology proposed in this study provides sufficient and useful information in forest monitoring; this includes the location, time and trajectories of the changes. The results of the detection system developed in this article will be available for potential users.

Mapping US Urban Extents from MODIS Data Using One-Class Classification Method

Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.

Wetlands Mapping in North America by Decision Rule Classification Using MODIS and Ancillary Data

An up-to-date wetlands map based on remote sensing data at a continental scale is urgently needed for estimating global environmental change. In this study, a wetlands map of North America was developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data obtained in 2008 and ancillary data. For this purpose, a decision rule classification method was developed relied upon the hierarchical characteristics of land types and prior knowledge about the geographical location of wetlands. Two hierarchical levels of land types were used to extract wetlands. At the first level, non-vegetation land types including water, snow, urban, and bare areas were separately extracted from vegetation land types using threshold methods. At the second level, wetlands were discriminated from non-wetland vegetation land types with the MODIS tasseled cap (brightness, greenness, and wetness) indices using the decision tree method. In addition, elevation data were used to build the elevation mask and a climate map was used to subdivide the study area into five sub-regions. In the quantitative accuracy assessment, user's and producer's accuracies of wetlands for the whole study area were calculated as 80.3% and 83.7%, respectively. In a comparison with two existing global land cover datasets, GLC2000 and IGBP DISCover, our results show significant improvement in extracting coastal and narrow types of wetlands. This study indicates that decision rule classification, integrated with multi-temporal MODIS data and ancillary data, is useful to develop an improved wetlands map at a continental scale.

Characterizing temporal vegetation dynamics of land use in regional scale of Java Island, Indonesia

Improving the understanding of land use and land cover is a major research challenge for the human-environmental sciences and is essential for many aspects of global environmental research. Considering seasonal vegetation dynamics or phenological dynamics in multi-year series leads to a broader view of land use and land cover. This study is based on the hypothesis that a pixel representing a complex but consistent land use has a typical, distinct and repeated temporal pattern of vegetation index inter-annually, which can be used as characteristic signatures for land use classification. Considering the seasonal events and climatic variability in Indonesia, we characterized the temporal vegetation dynamics of long-term land use by using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index 16-day composite data from 2001 to 2007, and then generated a land use map using those characteristics. Accuracy assessment of the results showed the need to evaluate such methods for land use types that do not have a consistent yearly pattern. On the other hand, the identification of the intensive agriculture lands, such as paddy rice and upland, was satisfactory. Although the mixed pixel issue is quite problematic when using MODIS data, the results indicate that MODIS data offer great promise for characterizing seasonal as well as multi-year variation at large scales. Indeed, the methodology proposed in this research distinguished among many specific land use classes based on temporal land cover information properties. Characterization of temporal vegetation dynamics patterns would provide sufficient, significant and useful information regarding the patterns of land use; consequently it should be possible to consider the actual, subtle nature of inter-annual land use change as well as overall land use.

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T(a)) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for T(a) estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed T(a) based on MODIS land surface temperature (LST) data. The verification results of maximum T(a), minimum T(a), GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale.

Monitoring rangeland ground cover vegetation using multitemporal MODIS data

The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R 2 value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.

Method for Detecting Snow Lines From MODIS Data and Assessment of Changes in the Nianqingtanglha Mountains of the Tibet Plateau

Change in the snow line elevation (SLE) in the mountains, which is generally sensitive to climate change, is one of the indicators used to monitor climatic behavior. We propose a method to extract the snow line of the minimum snow cover extent using the multi-temporal MODerate-resolution Imaging Spectroradiometer (MODIS) reflectance data combining with higher-spatial-resolution Digital Elevation Model (DEM) data in high mountain area. The changes in the SLE of minimum snow cover and their response to climate warming were assessed from 2000 to 2009 in the Nianqingtanglha mountains in the Tibet Plateau. The results show that the annual mean SLE rose from 5733 m in 2004 to 5828 m in 2009. The variability of SLE, moreover, demonstrated a significant correlation with temperature <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$({\rm R}^{2}=0.82)$</tex> </formula> and precipitation <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$({\rm R}^{2}=0.65)$</tex></formula> . Our study indicates that the SLE of the minimum snow cover every year can be better detected taking the advantage of MODIS multi-temporal data and combining with higher spatial resolution DEM data which guarantees enough snow line pixels to perform sound statistical analyses.

Multitemporal MODIS Data to Mapping Rice Field Distribution in Bali Province of Indonesia Based on the Temporal Dynamic Characteristics of the Rice Plant

Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data has been widely employed for many applications. It has fine temporal, spectral and spatial resolution. This feature can be used for monitoring earth condition continuously, such as the rice field distribution. Rice fields can be known by detecting the rice plant in that area. Enhanced Vegetation Index 2 (EVI2) is one of the indexes, which describe vegetation conditions and was employed to mapping the rice field. Rice field area was identified using growth curve recognition of EVI2 MODIS data based on rice plant temporal dynamic characteristics. The rice field distributions in Bali that were estimated from MODIS data in 2009 show reasonable spatial agreement with rice field distribution from land use map of 2008. The total area of rice field from MODIS data is 101,218.75 hectare (accuracy 88.21% of reference data). The southern part of Bali has wider rice coverage compared to northern part of Bali because of the topographic condition in southern Bali is suitable for rice cultivation. The regency and district level comparison of rice field area in Bali Province showed a good spatial agreement of accuracy. This indicates that MODIS EVI2 250 m data can be used to mapping homogeneous areas in the small region of district scale. The accuracy identify rice field are affected by several factors, such as spatial resolution and cloud cover of satellite data, elevation of rice field area, and type of rice field.

Rice heading date retrieval based on multi-temporal MODIS data and polynomial fitting

The objective of the study is to identify the rice heading date and analyse its spatial characteristics on a regional scale using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) normalized differential vegetation index (NDVI) data and a new approach: quadratic polynomial fitting. The cloud-contaminated NDVI value was identified by reliability data and linearly interpolated with values before and after the cloudy one. The discrete Fourier transformation (DFT) and quadratic polynomial fitting were implemented to generate new time series curves. Rice heading date was retrieved by calculating the day for maximum NDVI. Comparing with DFT, the proposed quadratic polynomial fitting significantly improves the computation efficiency, while providing approximate precision of estimation. In regional analysis, the rice heading date retrieved from polynomial fitting is more consistent than that from DFT. The study also suggests that multi-temporal MODIS NDVI data combined with different methods can retrieve crop phenology information on a large scale.

Remote sensing classification of grass seed cropping practices in western Oregon

Our primary objective was extending knowledge of major crop rotations and stand establishment conditions present in 4800 grass seed fields surveyed over three years in western Oregon to the entire Willamette Valley through classification of multiband Landsat images and multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day composite Normalized Difference Vegetation Index (NDVI). Mismatch in resolution between MODIS and Landsat data was resolved by edging of training and test validation areas using 3 by 3 neighbourhood tests for class uniformity, resampling of MODIS data to 50-m resolution followed by 3 by 3 neighbourhood smoothing to artificially enhance resolution, and resampling to 30 m for stacking data in groups of up to 64, 55 and 81 bands in 2004–2005, 2005–2006 and 2006–2007. Imposing several object-based rules raised final classification accuracies to 84.7, 77.1 and 87.6% for 16 categories of cropping practices in 2005, 2006 and 2007. Total grass seed area was under-predicted by 3.9, 5.4 and 1.8% compared to yearly Cooperative Extension Service estimates, with Italian ryegrass overestimated by an average of 8.4% and perennial ryegrass, orchardgrass and tall fescue underestimated by 10.4, 3.3 and 2.1%. Knowledge of field disturbance patterns will be crucial in future landscape-level analyses of relationships among ecosystem services.

Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers

Multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to estimate the spatial distribution of heading date and rice-cropping system employed in the Mekong Delta relative to seasonal changes in water resources in 2002 and 2003. We improved a Wavelet-based Filter for determining Crop Phenology (WFCP) and developed a Wavelet-based Filter for evaluating the spatial distribution of Cropping Systems (WFCS) to the interpretation of MODIS time-series data to determine the spatial distribution of rice phenology and various rice-cropping systems from the seasonal Enhanced Vegetation Index (EVI) data. The findings correspond well the physical characteristics of the cropping system in the Mekong Delta, which have changed over time in response to localized and seasonal changes in water resources. One such example is the double-irrigated rice-cropping system commonly employed in the upper Mekong Delta in the dry season to avoid damage due to the subsequent floods. The shortage of suitable irrigation water and intrusion of saline water in the coastal regions during the dry season has constrained the practice dry-season cropping and has meant that the double- and single-rainfed rice-cropping systems are employed in the rainy season. A triple-irrigated rice-cropping system is used in the central part of the Mekong Delta which is located midway between the flood-prone and salinity intrusion areas. Analysis of annual changes in the rice cropping systems between 2002 and 2003 showed that the triple-cropped rice expanded to the flood- and salinity-intrusion areas. This expansion indicates that the implementation of measures to limit the extent of flooding and salinity intrusion by improved farming technologies and improvements in land management. The heading dates in the upper Mekong Delta in 2003 were earlier than in 2002 by approximately 20 to 30 days. The reasons for this would be due to decreased flood runoff in 2002 compared to 2001, and implementation of government policies regarding early sowing of dry-season crops. Subsequent analysis of the MODIS data confirmed that the spatial distribution of rice-cropping systems was closely related to seasonal changes in river runoff regime in the Mekong Delta.

Multisensor comparisons and validation of modis vegetation indices at the semiarid jornada experimental range

Vegetation indices (VIs) are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Earth Observing System (EOS) Terra platform, launched in December 1999. An important requirement of MODIS science products is that they be rigorously validated. In this study, we conducted a site-intensive MODIS VI product validation at the semiarid Jornada Experimental Range, New Mexico, an EOS Land Validation Core Site. Our validation approach involved scaling up independent fine-grained datasets, including ground and airborne radiometry, and high spatial resolution imagery [Enhanced Thematic Mapper Plus (ETM+)], to the coarser MODIS spatial resolutions. The MODIS VIs were evaluated with respect to their radiometric performances, the uncertainties of the compositing methodology, and their capabilities to depict seasonal variations in vegetation. The MODIS Quick Airborne Looks (MQUALS) radiometric package was found useful in up-scaling field in situ measurements to coarser spatial resolutions. Both single-day nadir-view and 16-day composited MODIS reflectances and VIs matched well with the nadir-based atmosphere-free MQUALS observations for all the land cover types found at Jornada, with the root mean squared deviations less than 0.03. The MODIS 16-day composited products also performed well with the single-day nadir-view MODIS data, despite some off-nadir view angles and uncertainties with the cloud mask algorithm. The quality assurance (QA)-based constrained view angle-maximum value composite (CV-MVC) algorithm successfully filtered out much of the cloud and aerosol contaminated observations and helped to minimize view angle-related problems. The MODIS seasonal VI profiles also matched quite well with the other multiple sensor datasets obtained at the finer spatial resolutions. The QA information was found to be crucial in achieving consistent spatial and temporal comparisons of global vegetation conditions and for deriving accurate depictions of important phenological features in multitemporal MODIS data. The results of this validation study over the Jornada Experimental Range demonstrated the accuracy, reliability, and science utility of the MODIS VI products in arid and semiarid areas.