Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada

The boreal forest biome is one of the largest on Earth, covering more than 14% of the total land surface. Fire disturbance plays a dominant role in boreal ecosystems, altering forest succession, biogeochemical cycling, and carbon sequestration. We used two time-series data sets of Advanced Very High Resolution Radiometer (AVHRR) Normalized Differenced Vegetation Index (NDVI) imagery for North America to analyze vegetation recovery after fire. The Canadian Forest Service Large Fire Database was used to identify the location of fires and calculate scaled NDVI statistics from the Pathfinder AVHRR Land (PAL) and the Global Inventory Modeling and Mapping Studies (GIMMS) AVHRR data sets. Unburned areas were also identified, based on interannual variability metrics, in order to reduce the effects of factors other than fire on the temporal behavior of scaled NDVI. Burned and unburned areas were stratified by ecoregion to ensure the presence of comparable land cover types and account for influences of local environmental variability. Temporal anomalies in NDVI for burned and unburned areas show the impacts of fire and the recovery of the forest to pre-burn levels, and indicate changes in variability that might be associated with vegetation compositional changes consistent with early successional species. The rate of recovery varied in the three episodic fire years on which we focused our analysis (1981, 1989, and 1995), but were consistently shorter than previous studies that emphasized the most impacted areas within fires. Temporal variability in the time series, represented by the difference of burned and unburned area anomalies, increased beyond the observed post-fire recovery period. This indicates residual effects of fire disturbance over the regrowth period, perhaps associated with early successional vegetation and increased susceptibility to drought. Distinct differences were noted between the PAL and GIMMS data sets, with evidence for systematic data processing artifacts remaining in the PAL time series.     

MODIS-informed greenness responses to daytime land surface temperature fluctuations and wildfire disturbances in the Alaskan Yukon River Basin

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components.     

Synchronous NDVI and surface air temperature trends in Newfoundland: 1982 to 2003

We investigated two unusual systematic normalized difference vegetation index (NDVI) growing season trends in Newfoundland from 1982 to 1999 within a satellite data record from 1982 to 2003 and attempted to determine their causes. We found direct correlations between increasing NDVI and increasing surface air temperature for the 1982–1990 and 1991–1999 growing seasons, punctuated by sharp simultaneous decreases in both surface temperature and the NDVI in 1991 from 1990 that were associated with the volcanic eruption of Mt. Pinatubo. No covarying trend between NDVI and surface air temperatures was found from 2000 to 2003. The responses of increasing plant growth, as indicated by the increasing NDVI, to increasing surface air temperature from 1982 to 1990 and 1991 to 1999, were also associated with a lengthening of the growing seasons of ～9 and ～17 days for these two periods, respectively. We found no systematic variations in precipitation, the presence of early season and late season snow cover, systematic variation in down‐welling photosynthetically‐active radiation, post‐forest fire succession, regrowth following logging of coniferous forests, and other land use changes that contributed to the 1982–1990 and 1991–1999 increasing NDVI trends. Our results document the influences of surface air temperature upon boreal forest plant growth and the cooling effects of major volcanic eruptions in this ecological system.     

A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry

The bi-directional reflectance distribution function (BRDF) alters the seasonal and inter-annual variations exhibited in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and this hampers the detection and, consequently, the interpretation of temporal variations in land-surface vegetation. The magnitude and sign of bi-directional effects in commonly used AVHRR data sets depend on land-surface properties, atmospheric composition and the type of atmospheric correction that is applied to the data. We develop an approach to estimate BRDF effects in AVHRR NDVI time series using the Moderate Resolution Imaging Spectrometer (MODIS) BRDF kernels and subsequently adjust NDVI time series to a standard illumination and viewing geometry. The approach is tested on NDVI time series that are simulated for representative AVHRR viewing and illumination geometry. These time series are simulated with a canopy radiative transfer model coupled to an atmospheric radiative transfer model for four different land cover types—tropical forest, boreal forest, temperate forest and grassland - and five different atmospheric conditions - turbid and clear top-of-atmosphere, turbid and clear top-of-atmosphere with a correction for ozone absorption and Rayleigh scattering applied (Pathfinder AVHRR Land data) and ground-observations (fully corrected for atmospheric effects). The simulations indicate that the timing of key phenological stages, such as start and end of growing season and time of maximum greenness, is affected by BRDF effects. Moreover, BRDF effects vary with latitude and season and increase over the time of operation of subsequent NOAA satellites because of orbital drift. Application of the MODIS kernels on simulated NVDI data results in a 50% to 85% reduction of BRDF effects. When applied to the global 18-year global Normalized Difference Vegetation Index (NDVI) Pathfinder data we find BRDF effects similar in magnitude to those in the simulations. Our analysis of the global data shows that BRDF effects are especially large in high latitudes; here we find that in at least 20% of the data BRDF errors are too large for accurate detection of seasonal and interannual variability. These large BRDF errors tend to compensate, however, when averaged over latitude.     

Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms

This paper evaluates annual fire maps that were produced from NOAA-14/AVHRR imagery using an algorithm described in a companion paper (Li et al., International Journal of Remote Sensing, 21, 3057-3069, 2000 (this issue)). Burned area masks covering the Canadian boreal forest were created by compositing the daily maps of fire hot spots over the summer and by examining Normalized Difference Vegetation Index (NDVI) changes after burning. Both masks were compared with fire polygons derived by Canadian fire agencies through aerial surveillance. It was found that the majority of fire events were captured by the satellite-based techniques, but burnt area was generally underestimated. The burn boundary formed by the fire pixels detected by satellite were in good agreement with the polygons boundaries within which, however, there were some fires missed by the satellite. The presence of clouds and low sampling frequency of satellite observation are the two major causes for the underestimation. While this problem is alleviated by taking advantage of NDVI changes, a simple combination of a hot spot technique with a NDVI method is not an ideal solution due to the introduction of new sources of uncertainty. In addition, the performance of the algorithm used in the International Geosphere-Biosphere Programme (IGBP) Data and Information System (IGBPDIS) for global fire detection was evaluated by comparing its results with ours and with the fire agency reports. It was found that the IGBP-DIS algorithm is capable of detecting the majority of fires over the boreal forest, but also includes many false fires over old burned scars created by fires taking place in previous years. A step-by-step comparison between the two algorithms revealed the causes of the problem and recommendations are made to rectify them.     

Crseharacterization of land surface thermal structure from NOAA-AVHRR data over a northern ecosystem

This paper focuses on the estimation and analysis of surface thermal parameters (emissivities and surface temperatures) in a nordic environment (Québec, Canada). The land cover in this region varies from boreal forest in the south to tundra in the north. The thermal parameters are estimated from two variants of a new model that combines the radiances of the short wave infrared (SWIR) spectral band [advanced very high resolution radiometer (AVHRR) channel 3: 3.55–3.93 μm] and the thermal bands (AVHRR channel 4: 10.5–11.5 μm; and AVHRR channel 5: 11.5–12.5 μm). The study, carried out for images acquired on different dates, reveals that, in most situations, the two approaches allow the separation of emissivities and surface temperatures. Analysis of the variations of the estimated emissivities in relation to surface patterns shows that they are slightly variable in spectral bands 4, and 5, with values generally greater than 0.95. Variations are more important in the SWIR channel, where values less than 0.90 appear, especially in urban areas. In general, surface emissivities increase with the density of the vegetation cover. Moreover, for densely vegetated areas, SWIR surface reflectivities, which can be derived from emissivities, appear to be well correlated with the reflectivities of the AVHRR visible channel. As with emissivities, variations of the estimated land surface temperatures (LST) in relation to vegetation density, characterized by the normalized difference vegetation index (NDVI), were considered. The relations between the two parameters (LST and NDVI) show essentially two opposite directions of linear variations (positive and negative correlations). In the light of the main results obtained, the synergistic use of the different spectral regions (visible, near, mid-, and thermal infrareds) could be very useful in the parameterization of boreal ecosystems.     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

Subpixel forest cover in central Africa from multisensor, multitemporal data

Seven Landsat Multispectral Scanner (MSS) scenes in central Africa were coregistered with 8 km resolution data from the 1987 AVHRR Pathfinder Land data set. Percent forest cover in each 8 km grid cell was derived from the classified MSS scenes. Linear relationships between percent forest cover and 30 multitemporal metrics derived from all AVHRR optical and thermal channels were determined. Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for those metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest cover in various locations in the study area using multiple linear regression and regression trees. Overall, the multiple linear regression provided more accurate results. Predicted percent forest cover estimates were within 20% of the “actual” percent forest cover (derived from the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest cover were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions.     

An integrated vegetation mapping approach for northern Alaska (1:4 M scale)

A six-step integrated vegetation mapping approach is described for making a small-scale (1:4 million) map of northern Alaska. The method uses two primary maps: (1) a Phytogeographic subzones and Floristic subprovinces Map (PFM) adjusted to Advanced Very High Resolution Radiometer false colour infrared (AVHRR CIR) imagery, and (2) an Integrated Vegetation-Complex Map (IVCM). The IVCM map-polygon boundaries are guided by information from a variety of remote-sensing data (AVHRR imagery, maximum greenness maps and classified images) and hard-copy source maps (surficial geology, bedrock geology, soils, percentage water cover). The map-polygon boundaries are integrated so that they conform to terrain features that are interpretable on the AVHRR CIR. The PFM and IVCM are overlaid in a geographic information system (GIS), and a series of derived maps is created through the use of look-up tables. Northern Alaska is a prototype area for the Circumpolar Arctic Vegetation Mapping (CAVM) project, which has a goal of producing a new vegetation map of the region north of the arctic tree line by the year 2001. The method could be modified and adapted to any region of the Arctic based on locally available information.     

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem-atmosphere CO₂ exchange in the Amazon region.     

Arctic tundra functional types by classification of single-date and AVHRR bi-weekly NDVI composite datasets

Maps of functional vegetation types are often used to extrapolate in situ measurements of biogeochemical fluxes to make regional-scale or biome-scale estimates. The objective of this study was to determine the optimal spectral-radiometric and temporal features derived from single-date and seasonal time series NOAA AVHRR imagery for classifying three Arctic tundra functional types. A single-date, three-band (VIS, NIR and NDVI) input yielded a map with the highest agreement (86.1% for supervised and 87.8% for unsupervised approaches) compared to a reference map. The map generated with the conventional maximum value NDVI for 10 semi-monthly periods through the growing season was in 82.7% agreement.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Fire-induced changes in green-up and leaf maturity of the Canadian boreal forest

Recent studies of vegetation phenology of northern forests using satellite data suggest that the observed earlier spring increase and peak amplitude of the normalized difference vegetation index (NDVI) are a result of climate warming. In addition to undergoing an increase in temperature, the northern forests of Canada have also seen a dramatic increase in area burned by wildfire over the same time period. Using the Canadian Large Fire Database, we analyzed the impact fire had on the phenological dates derived from fitting a logistical model to yearly data from 2004 for several different subsets of both AVHRR-NDVI and MODIS LAI in wildfire dominated terrestrial ecozones. Fire had a significant but complex effect on estimated phenology dates. The most recently burned areas (1994–2003) had later green-up dates in two ecozones for AVHRR data and all ecozones for MODIS. However, older forested (not burned during 1980–2003) had estimated green-up dates 1 to 9 days earlier than the entire forested area in the MODIS LAI data. These data corroborate studies in Canada and demonstrate that fire history is influencing boreal forest phenology and growing season LAI.     

Variation in NDVI values with change in spatial resolution for semi-arid savanna vegetation: a case study in northwestern South Africa

Natural vegetation and crop-greening patterns in semi-arid savannas are commonly monitored using normalized difference vegetation index (NDVI) values from low spatial resolution sensors such as the Advanced Very High Resolution Radiometer (AVHRR) (1 km, 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m, 500 m). However, because semi-arid savannas characteristically have scattered tree cover, the NDVI values at low spatial resolution suffer from the effect of aggregation of near-infrared and red energy from adjacent vegetated and non-vegetated cover types. This effect is seldom taken into consideration or quantified in NDVI analyses of the vegetation of semi-arid lands. This study examined the effect of pixel size on NDVI values of land-cover features for a semi-arid area, using the 1000 m, 250 m and 10 m pixel sizes. A rainy season Système Pour l'Observation de la Terre 5 (SPOT 5) High Resolution Geometric (HRG) image at 10 m spatial resolution was utilized. Following radiometric and geometric preprocessing, the 10 m pixel size of the image was aggregated to 250 m and 1000 m to simulate imagery at these pixel sizes, and then NDVI images at the spatial resolution scales of 10 m (NDVI_{10 m}), 250 m (NDVI_{250 m}), and 1000 m (NDVI_{1000 m}) derived from the respective images. The simulation of the NDVI_{250 m} image was validated against a concurrent 16 day MODIS NDVI composite (MOD13Q1) image, and the accuracy derived from the validation was generalized to the NDVI_{1000 m} image. With change from low to high spatial resolution, extreme magnitude NDVI values shifted towards the centre (mode) of the resulting approximately Gaussian NDVI distributions. There was a statistically significant difference in NDVI values at the three pixel sizes. Low spatial magnitude vegetation sites (woodland, cropland) had reductions of up to 28% in NDVI value between the NDVI_{10 m} and NDVI_{1000 m} scales. The results indicate that vegetation monitoring using low spatial resolution imagery in semi-arid savannas may only be indicative and needs to be supplemented by higher spatial resolution imagery.     

南方丘陵地区水稻种植面积遥感信息提取的可行性分析*

以浙江省为试验区,在地理信息系统支持下综合利用多种地理信息,探讨丘陵地区大面积提取水稻种植面积信息的可行性。开展了分类识别方法的比较试验及训练样点相对稳定性试验。针对丘陵地区的复杂地形,在数字化地形图的基础上,建立数字地形模型(DTM),并衍生出地面坡度等地貌因子的数字化图像,结合NOAA/AVHRR数据,进行分类。试验结果表明,传统的分类识别方法中,最大似然法的分类精度可满足业务化运行的要求;建立在混合像元分解基础上的模糊监督分类,有较高的分类精度和较好的稳定性,具有较强的适应性;地貌因子参与遥感影像的分类,不仅可以有效地提高丘陵地区水稻种植面积信息的提取精度,而且还可以使面积信息提取精度保持一定的稳定性,提高空间精度;为探讨丘陵地区水稻种植面积信息遥感提取的可靠性和客观性,在训练样点保持相对稳定的前提下,对1996年和1997年浙江省水稻种植面积进行测算,两年的数量精度均在92%以上。     

Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado: An analysis within the Large-Scale Biosphere-Atmosphere Experiment in Amazônia (LBA)

The Brazilian Cerrado biome comprises a vertically structured mosaic of grassland, shrubland, and woodland physiognomies with distinct phenology patterns. In this study, we investigated the utility of spectral vegetation indices in differentiating these physiognomies and in monitoring their seasonal dynamics. We obtained high spectral resolution reflectances, during the 2000 wet and dry seasons, over the major Cerrado types at Brasilia National Park (BNP) using the light aircraft-based Modland Quick Airborne Looks (MQUALS) package, consisting of a spectroradiometer and digital camera. Site-intensive biophysical and canopy structural measurements were made simultaneously at each of the Cerrado types including Cerrado grassland, shrub Cerrado, wooded Cerrado, Cerrado woodland, and gallery forest. We analyzed the spectral reflectance signatures, their first derivative analogs, and convolved spectral vegetation indices (VI) over all the Cerrado physiognomies. The high spectral resolution data were convolved to the MODIS, AVHRR, and ETM⁺ bandpasses and converted to the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) to simulate their respective sensors. Dry and wet season comparisons of the measured biophysical attributes were made with the reflectance and VI data for the different Cerrado physiognomies. We found that three major domains of Cerrado could be distinguished with the dry and wet season spectral signatures and vegetation indices. The EVI showed a higher sensitivity to seasonality than the NDVI; however, both indices displayed seasonal variations that were approximately one-half that found with the measured landscape green cover dynamics. Inter-sensor comparisons of seasonal dynamics, based on spectral bandpass properties, revealed the ETM⁺-simulated VIs had the best seasonal discrimination capability, followed by MODIS and AVHRR. Differences between sensor bandpass-derived VI values, however, varied with Cerrado type and between dry and wet seasons, indicating the need for inter-sensor VI translation equations for effective multi-sensor applications.     

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r² values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI “cancellation effect” where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes.     

南方丘陵地区水稻种植面积遥感信息提取的可行性分析

以浙江省为试验区，在地理信息系统支持下综合利用多种地理信息，探讨丘陵地区大面积提取水稻种植面积信息的可行性。开展了分类识别方法的比较试验及训练样点相对稳定性试验。针对丘陵地区的复杂地形，在数字化地形图的基础上，建立数字地形模型（ＤＴＭ），并衍生出地面坡度等地貌因子的数字化图像，结合ＮＯＡＡ／ＡＶＨＲＲ数据，进行分类。试验结果表明，传统的分类识别方法中，最大似然法的分类精度可满足业务化运行的要求；建立在混合像元分解基础上的模糊监督分类，有较高的分类精度和较好的稳定性，具有较强的适应性；地貌因子参与遥感影像的分类，不仅可以有效地提高丘陵地区水稻种植面积信息的提取精度，而且还可以使面积信息提取精度保持一定的稳定性，提高空间精度；为探讨丘陵地区水稻种植面积信息遥感提取的可靠性和客观性，在训练样点保持相对稳定的前提下，对１９９６年和１９９７年浙江省水稻种植面积进行测算，两年的数量精度均在９２％以上。     

Suivi du développement végétal au cours de l'été 1984 dans le Sahel Malien

A ground data-collection programme was initiated to establish a calibration between the normalized difference vegetation index (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and grassland biomass. Thirty sites were selected representing a range of Sahclian vegetation communities in the Gourma region of Mali and monitored during the 1984 growing season. The sites were 1?km square and located within larger areas of homogeneous terrain. The herbaceous and woody strata were sampled every fourteen days, and above-ground green biomass and rainfall data were collected. Ground and airborne radiometer data were recorded to facilitate interpretation of the satellite data, and aerial photographs were taken to provide estimates of tree and shrub density. AVHRR LAC and GAC data were acquired and a thermal cloud mask was applied to the data. NDVI values were extracted for the ground sites and correlation analysis performed. Low correlation coefficients were calculated for the ground measured green biomass and satellite NDVI (0,67). The correlation between the maximum NDVI and the total biomass produced during the season was 0,73. A value of 0,05 was determined as the NDVI associated with the minimum vegetation cover identifiable by the satellite (100 kg/ha). Explanation is given for the possible causes for such low correlations, including the very low biomass production associated with the 1984 drought conditions, atmospheric haze and dust and poor locational accuracy of the satellite data