Spectral analysis and classification accuracy of coffee crops using Landsat and a topographic‐environmental model

Coffee is an extremely important cash crop, yet previous work indicates that satellite mapping of coffee has produced low classification accuracy. This research examines spectral band combinations and ancillary data for evaluating the classification accuracy and the nature of spectral confusion between coffee and other cover types in a Costa Rican study area. Supervised classification using Landsat Enhanced Thematic Mapper (ETM+) with only red, near‐infrared, and mid‐infrared bands had significantly lower classification accuracy compared to datasets that included more spectral bands and ancillary data. The highest overall accuracy achieved was 65%, including a coffee environmental stratification model (CESM). Producer's and user's accuracy was highest for shade coffee plantations (91.8 and 61.1%) and sun coffee (86.2 and 68.4%) with band combination ETM+ 34567, NDVI, cos (i), and including the use of the CESM. Post‐classification stratification of the optimal coffee growing zone based on elevation and precipitation data did not show significant improvement in land cover classification accuracy when band combinations included both the thermal band and NDVI. A forward stepwise discriminant analysis indicated that ETM+ 5 (mid‐infrared band) had the highest discriminatory power. The best discriminatory subset for all woody cover types including coffee excluded ETM+ 3 and 7; however, the land cover accuracy assessment indicated that overall accuracy, as well as producer's and user's accuracy of shade and sun coffee, were slightly improved with the inclusion of these bands. Although spectral separation between coffee crops and woodland areas was only moderately successful in the Costa Rica study, the overall accuracy, as well as the sun and shade coffee producer's and user's accuracy, were higher than reported in previous research.     

Land‐cover mixing and spectral vegetation indices

Vegetation indices have been widely used as indicators of seasonal and inter‐annual variations in vegetation caused by either human activities or climate, with the overall goal of observing and documenting changes in the Earth system. While existing satellite remote sensing systems, such as NASA's Multi‐angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS), are providing improved vegetation index data products through correcting for the distortions in surface reflectance caused by atmospheric particles as well as ground covers below vegetation canopy, the impact of land‐cover mixing on vegetation indices has not been fully addressed. In this study, based on real image spectral samples for two‐component mixtures of forest and common nonforest land‐cover types directly extracted from a 1.1?km MISR image by referencing a 30?m land‐cover classification, the effect of land‐cover mixing on the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) has been quantitatively evaluated. When the areal fraction of forest was lower than 80%, both NDVI and EVI varied greatly with mixed land‐cover types, although EVI varied less than NDVI. Such a phenomenon can cause errors in applications based on use of these vegetation indices. This study suggests that methods that reduce land‐cover mixing effects should be introduced when developing new spectral vegetation indices.     

A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVI's calculation and the index's sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI- and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy (< 3% difference), spatially depicted patterns (> 90% pixel-level thematic agreement) and classified crop areas between the series of EVI- and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.     

Multi-index-based soil moisture estimation using MODIS images

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

Land cover assessment with MODIS imagery in southern African Miombo ecosystems

Global land use and land cover products in highly dynamic tropical ecosystems lack the detail needed for natural resource management and monitoring at the national and provincial level. The MODIS sensor provides improved opportunities to combine multispectral and multitemporal data for land use and land cover mapping. In this paper we compare the MODIS Global Land Cover Classification Product with recent land use and land cover maps at the national level over a characteristic location of Miombo woodlands in the province of Zambezia, Mozambique. The performances of three land cover-mapping approaches were assessed: single-date supervised classification, principal component analysis of band-pair difference images, and multitemporal NDVI analysis. Extensive recent field data were used for the definition of the test sites and accuracy assessment. Encouraging results were achieved with the three approaches. The classification results were refined with the help of a digital elevation model. The most consistent results were achieved using principal component analysis of band-pair difference images. This method provided the most accurate classifications for agriculture, wetlands, grasslands, thicket and open forest. The overall classification accuracy reached 90%. The multitemporal NDVI provided a more accurate classification for the dense forest cover class. The selection of the right image dates proved to be critical for all the cases evaluated. The flexibility of these alternatives makes them promising options for rapid and inexpensive land cover mapping in regions of high environmental variability such as tropical developing countries.     

Monitoring Sahelian floodplains using Fourier analysis of MODIS time‐series data and artificial neural networks

Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time‐series data was applied to monitor the flooding extent of the Waza‐Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.     

Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for large-area crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral-temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region's major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop's multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state's climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season.     

A multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa

Recent developments in global land-cover mapping have focused on spatial resolution improvement with more heterogeneous features to integrate spatial, spectral and temporal information. In this study, hundreds of features derived from four seasonal Landsat 8 OLI (Operational Land Imager) spectral bands, Moderate Resolution Imaging Spectroradiometer (MODIS) time series vegetation index (VI) data, night-time light (NTL), digital elevation models (DEM) and climatic variables were used for land cover mapping with a target 30-m resolution for the whole African continent. In total, 49,007 training samples (from 11,231 locations) and 23,803 validation samples (from 5,414 locations) interpreted from seasonal Landsat, MODIS Normalized Difference Vegetation Index (NDVI) time series and high-resolution images in Google Earth were used for classifier training (Random Forest) and map validation. Overall accuracy was 76% at 30-m spatial resolution, which is better than previous land cover mapping for the African continent. Besides, accuracies for cropland were improved dramatically by more than 10%. Our method also addressed many remaining issues for 30-m mapping (e.g. boundary effects and declines in resolution). This framework is promising for automatic and efficient global land cover mapping resulting in better visual effects and classification accuracy.     

Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data

Temperate East Asia (TEA) is characterized by diverse land cover types, including forest and agricultural lands, one of the world's largest temperate grasslands, and extensive desert and barren landscapes. In this paper, we explored the potential of SPOT-4 VEGETATION (VGT) data for the classification of land cover types in TEA. An unsupervised classification was performed using multi-temporal (March–November 2000) VGT-derived spectral indices (Land Surface Water Index [LSWI] and Enhanced Vegetation Index [EVI]) to generate a land cover map of TEA (called VGT-TEA). Land cover classes from VGT-TEA were aggregated to broad, general class types, and then compared and validated with classifications derived from fine-resolution (Landsat) data. VGT-TEA produced reasonable results when compared to the Landsat products. Analysis of the seasonal dynamics of LSWI and EVI allows for the identification of distinct growth patterns between different vegetation types. We suggest that LSWI seasonal curves can be used to define the growing season for temperate deciduous vegetation, including grassland types. Seasonal curves of EVI tend to have a slightly greater dynamic range than LSWI during the peak growing season and can be useful in discriminating between vegetation types. By using these two complementary spectral indices, VGT data can be used to produce timely and detailed land cover and phenology maps with limited ancillary data needed.     

Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol‐free Vegetation Index (AFRI) on MODIS images

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R ² = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.     

Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems

Northern Arizona ecosystems are particularly sensitive to plant-available moisture and have experienced a severe drought with considerable impacts on ecosystems from desert shrub and grasslands to pinyon-juniper and conifer forests. Long-term time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used to monitor recent regional vegetation activity and temporal patterns across various ecosystems. Surface air temperature, solar radiation and precipitation are used to represent meteorological anomalies and to investigate associated impacts on vegetation greenness. Vegetation index anomalies in the northern Arizona ecosystem have a decreasing trend with increasing surface air temperature and decreasing precipitation. MODIS NDVI and EVI anomalies are likely sensitive to the amount of rainfall for northern Arizona ecosystem conditions, whereas inter-annual variability of surface air temperature accounts for MODIS NDVI anomaly variation. The higher elevation area shows the slow vegetation recovery through trend analysis from MODIS vegetation indices for 2000–2011 within the study domain and along elevation.     

A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert,USA

Sonoran Desert bighorn sheep (Ovis canadensis mexicana) occupy rugged upland areas that experience irregular periods of vegetation growth associated with precipitation events. These episodic and often spatially limited events provide important forage and preformed water resources that may be important drivers of animal movement and habitat use. Habitat-use models that incorporate forage phenology would broaden our understanding of desert bighorn ecology and have considerable potential to inform conservation efforts for the species. Field-based methods are of limited utility to characterize vegetation phenology across large areas. Vegetation indices (VI) derived from satellite imagery are a viable alternative, but may be confounded by areas of high relief and shadow effects that can degrade VI values. The varying spatial and temporal resolutions of readily available satellite sensors, such as the Landsat thematic mapper (TM) and moderate-resolution imaging spectrometer (MODIS), present additional challenges. In this study, we sought to minimize degrading effects of terrain on TM- and MODIS-based estimates of vegetation phenology. We compared effects of high topographic relief on time series MODIS- and TM-based VI such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) using VI departures from average (DA) in shaded and unshaded areas. Sun elevation angle negatively impacted TM-derived NDVI and EVI values in areas of steep terrain. In contrast, MODIS-derived NDVI values were insensitive to sun elevation and terrain effects, whereas MODIS-derived EVI was degraded in areas of steep terrain. Time series MODIS NDVI and EVI DA values differed significantly during months of low sun elevation angle. Average MODIS EVI departure values were ≥20% lower than NDVI under these conditions, confounding time series estimates of plant phenology. Our best results were obtained from MODIS 16-day composited NDVI. These remote-sensing-based VI estimates of seasonal plant phenology and productivity can be used to inform models of habitat use and movements of desert bighorn over large areas.     

Estimating tree‐cover change in Australia: challenges of using the MODIS vegetation index product

Time series of the vegetation index product MOD13Q1 from the Moderate Resolution Imagery Spectroradiometer (MODIS) were assessed for estimating tree foliage projective cover (FPC) and cover change from 2000 to 2006. The MOD13Q1 product consists of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). There were four challenges in using the MOD13Q1 product to derive tree FPC: assessing the impact of the sensor's varying view geometry on the vegetation index values; decoupling tree and grass cover contributions to the vegetation index signal; devising a method to relate the temporally composited vegetation index pixels to Lidar estimates of tree FPC for calibration; and estimating the accuracy of the FPC and FPC change measurements using independently derived Lidar, Landsat and MODIS cover estimates. The results show that, for complex canopies, the varying view geometry influenced the vegetation indices. The EVI was more sensitive to the view angle than the NDVI, indicating that it is sensitive to vegetation structure. An existing time series method successfully extracted the evergreen vegetation index signal while simultaneously minimizing the impact of varying view geometry. The vegetation indices were better suited to monitoring tree cover change than deriving accurate single‐date estimates of cover at regional to continental scales. The EVI was more suited to monitoring change in high‐biomass regions (cover >50%) where the NDVI begins to saturate.     

不同等级石漠化区MODIS-NDVI与MODIS-EVI对比分析

利用2001～2010年10 a的MODIS资料,比较分析广西喀斯特不同等级石漠化区MODIS\|NDVI和MODIS\|EVI的时间变化特征差异,利用全时间序列及16 d10 a均值序列分析NDVI和EVI之间的相关关系,比较线性及对数相关模型对两种植被指数相关关系的拟合效果,结果表明:石漠化等级由重度到潜在,两者之间的差值也随着植被覆盖度的增加而增大,植被覆盖度越低,NDVI和EVI所表征的植被变化特征越相似。NDVI的峰值出现时间多晚于EVI且其反映的植被变化趋势与实况更吻合,但其NDVI偏高;各等级石漠化的两种时间序列NDVI与EVI的对数相关关系优于线性相关,两种植被指数的相关性随着植被覆盖度的降低而增大,但全时间序列中轻度、中度石漠化相关性变化规律与16 d 10 a均值序列相反。     

Annual Total Gross Primary Production Estimation based on Vegetation Indices Seasonal Variation Curve

For the estimation of annual Gross Primary Productivity(GPP),it is proposed an estimation method with simple parameters and small errors.By taking each type of vegetation in the area of Three-North Shelterbelt Program(TNSP) as the research subject,the MODIS vegetation indices were obtained,and the seasonal variation curve of vegetation indices were built.Then,the fitting relation between the integral of time series vegetation indices(ΣVIs) and GPP products of MODIS was established,so as to realize a simple GPP estimation method and study the applicable ΣVIs for estimating the GPP of all vegetation types.The results show that：(1) ΣVIs is suitable for estimating the annual total GPP in research area and significantly correlated with MODIS GPP at the confidence level of p<0.01;(2) ΣEVI2 is applicable to estimate the GPP of evergreen needleleaf forest,decidious needleleaf forest,decidious broadleaf forest,mixed forest,woody savannas,savannas,permanent wetlands,croplands,croplands/natural vegetation mosaic,while the effect of ΣNDVI for estimating the GPP of closed shrublands,open shrublands,grasslands,croplands,and barren or sparsely vegetated is superior to ΣEVI andΣEVI2;(3) Since the NDVI itself is saturated in the area of high Leaf Area Index(LAI),the error of estimating the GPP of high LAI vegetation type by ΣNDVI is larger,while using ΣEVI and ΣEVI2 to estimate them has better accuracy,and the limitation from blue band of EVI2 reduces compared with EVI,which can be applied to the GPP research of long time series better.     

面向宏观地表分类的特征选择算法比较研究

为了成功将土地覆盖进行分类,选择合适的特征是至关重要的。针对利用MODIS数据进行宏观土地覆盖的分类问题,对三种典型的特征选择方法进行了比较研究。研究结果表明：分支定界法（BB）最适合于该土地覆盖分类问题,与此同时,ReliefF和mRMR方法在目标应用中的精度非常接近。研究结果同样表明进行特征选择是非常必要的,它不仅能够大大地降低计算复杂度,而且分类精度能够保持不变,甚至更高。     

Development of a two-band enhanced vegetation index without a blue band

The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor β is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, β and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within ± 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied.     

MODIS time-series imagery for forest disturbance detection and quantification of patch size effects

The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m single day surface reflectance (MOD09GQK) and 16-day composite gridded vegetation index data (MOD13Q1) were used to detect forest harvest disturbance between 2000 and 2004 in northern Maine. A MODIS multi-date Normalized Difference Vegetation Index (NDVI) forest change detection map was developed from each MODIS data set. A Landsat TM/ETM+ change detection map was developed as a reference to assess the effect of disturbed forest patch size on classification accuracy (agreement) and disturbed area estimates of MODIS. The MODIS single day and 16-day composite data showed no significant difference in overall classification accuracies. However, the 16-day NDVI change detection map had marginally higher overall classification accuracy (at 85%), but had significantly lower detection accuracy related to disturbed patch size than the single day NDVI change detection map. The 16-day composite NDVI data achieved 69% detection accuracy and the single day NDVI achieved 76% when the disturbed patch size was greater than 20 ha. The detection accuracy increased to approximately 90% for both data sets when the patch size exceeded 50 ha. The R² (range 0.6 to 0.9) and slope (range 0.5 to 0.9) of regression lines between Landsat and MODIS data (based on forest disturbance percent of township) increased with the mean disturbed patch size of each township. The 95% confidence intervals of forest disturbance percent estimate for each township were narrow with less than 1% of each township at the mean MODIS forest disturbance level.