Abstract. Despite its very large territory and the best Landsat archive in the world, Canada has made very limited use of Landsat data for land cover mapping. The primary difficulty has been the prohibitive cost of information extraction and the earlier (and now overcome for Landsat-7 enhanced thematic mapper plus data) high cost of data purchase. The solution to this remaining obstacle lies in decreasing the cost of Landsat data processing and analysis while ensuring the high quality of the extracted information. In this paper, we present an efficient and effective approach to mapping land cover in Canada from Landsat thematic mapper data (single or multiple satellites). The key features of this approach are an increase in the ratio of computer to human analysis and automation for high data volume or large area processing. However, it is essential that the final product quality not suffer because of the greater reliance on computer processing, thus the algorithm performance becomes critical. We describe the overall approach, discuss key challenges, explain the principles behind key algorithms developed to respond to the challenges, present evidence demonstrating the effectiveness of these algorithms in a boreal landscape setting, and consider implementation issues. With a processing system developed to handle large numbers (tens to hundreds) of Landsat scenes, which incorporates most of the algorithms discussed here, the stage is nearly set for large-scale processing leading to a Landsat-based land cover classification product(s) for Canada. Résumé. En dépit de l’étendue de son territoire et de la disponibilité de la meilleure archive Landsat au monde, le Canada a jusqu’à maintenant très peu fait usage des données Landsat pour les besoins de la cartographie du couvert. La difficulté

Multi-Resolution Land Characterization 2000 (MRLC 2000) is a second-generation federal consortium to create an updated pool of nation-wide Landsat 7 imagery, and derive a second-generation National Land Cover Database (NLCD 2000). This multi-layer, multisource database will include a suite of 30-meter resolution data that will serve as standardized ingredients for the production of land cover – both nationally and locally. This database will also provide the framework to allow flexibility in developing and applying suites of independent data layers. These nationally standardized independent data layers or components, will be useful not only within the land-cover classification but as data themes for other applications. This database will consist of the following components: (1) normalized tasseled cap (TC) transformations of Landsat 7 imagery for three time periods per scene (early, peak and late), (2) ancillary data layers, including 30m DEM derivatives of slope, aspect and elevation and three STATSCO soil derivatives, (4) image shape and texture information, (5) image derivatives of percent imperviousness and percent tree canopy per-pixel, (6) classified land-cover data derived from the Tassel Capped imagery, ancillary data and derivatives, (7) classification rules and metadata from the land cover classification, allowing future users the potential to modify rules to derive land cover products tailored to their specific local applications. In a pilot study application of the database concept, two mapping zones (Utah and Virginia) were selected for full generation of the above data components. Three derivative layers including, per-pixel imperviousness, per-pixel canopy and land cover were classified from the database. Cross validation accuracies for land cover ranged from 65-82%, and mean absolute error values of 10-15 % were reported for percent tree canopy and imperviousness.

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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

The modern analog technique typically uses a distance metric to determine the dissimilarity between fossil and modern biological assemblages. Despite this quantitative approach, interpretation of distance metrics is usually qualitative and rules for selection of analogs tend to be ad hoc. We present a statistical tool, the receiver operating characteristic (ROC) curve, which provides a framework for identifying analogs from distance metrics. If modern assemblages are placed into groups (e.g., biomes), this method can (1) evaluate the ability of different distance metrics to distinguish among groups, (2) objectively identify thresholds of the distance metric for determining analogs, and (3) compute a likelihood ratio and a Bayesian probability that a modern group is an analog for an unknown (fossil) assemblage. Applied to a set of 1689 modern pollen assemblages from eastern North America classified into eight biomes, ROC analysis confirmed that the squared-chord distance (SCD) outperforms most other distance metrics. The optimal threshold increased when more dissimilar biomes were compared. The probability of an analog vs no-analog result (a likelihood ratio) increased sharply when SCD decreased below the optimal threshold, indicating a nonlinear relationship between SCD and the probability of analog. Probabilities of analog computed for a postglacial pollen record at Tannersville Bog (Pennsylvania, USA) identified transitions between biomes and periods of no analog.

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The El Niño Southern Oscillation (ENSO) phenomenon produces an important inter-annual variability of oceanic and atmospheric conditions with irregular periods and amplitudes in many regions of the world. Some research showed that the strongest connections between Southern Oscillation and Australian rainfall occur in northern and eastern Australia, especially in Murray-Darling basin. In the present study, we evaluate the ability of Normalized Difference Vegetation Index (NDVI) to monitor the Murray-Darling Basin vegetation behaviour to ENSO induced precipitation anomalies. 10 year time series of Spot/VEGETATION 1km NDVI imagery product (1998-2007) were collected. We calculate the correlation between NDVI and ENSO quantitative indices like the Southern Oscillation Index (SOI). Although the SOI is only a temporal index, we use it as a general indicator for the water stress probability. In this research, we focus on Murray-Darling Basin, at same time, we choose Murray Irrigation Area as our detail analysis region since where we carry out field experiments every year. According to our results, NDVI presents a good correlation with SOI anomalies at seasonal time scale. On the Murray-Darling Basin scale, we clearly show the geographic patterns of the highest sensitivity to El Niño or La Niña. We also illustrate the inter-annual variability of these events in term of magnitude and geographic distribution. This variability is clearly explained by precipitation anomalies in relationship with ENSO events. 1.

The spatial distribution of vegetation in the Qilian Mountain area was quantified with remote sensing data. The MODIS NDVI values

Agricultural practices are continually changing at various spatial and temporal scales in response to local management decisions and environmental factors. However, few regional scale land use/land cover (LULC) classifications have focused on characterizing the agricultural sector, particularly on a regular basis to reflect croprelated land use changes that occur from year to year. More detailed and timely LULC data sets are needed for the Central Great Plains region to better understand the role and consequences of cropping practices on climate change issues that potentially threaten the region’s long-term agriculturally sustainability. Time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-meter NDVI (Normalized Difference Vegetation Index) was evaluated to determine if major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and crop-related land use practices (irrigation, fallow, and double cropping) in the Central Great Plains could be discriminated based on spectral-temporal differences. Median time-series NDVI curves were calculated for each crop class based on hundreds of field sites and the class ’ curves were compared for spectral-temporal differences. Specific crop types and irrigated and non-irrigated crops were discriminated based on differences in their median NDVI curves. Fallow, wheat-summer fallow, and double cropping practices were also identified based on their unique spectral-temporal characteristics. Regional differences in median NDVI curves were found for individual crop types. These regional curves appear to be related to differences in annual precipitation, growing season length, and planting dates. Timeseries MODIS ’ 250-meter data appears to have the spatial, spectral, and temporal resolutions necessary to characterize relatively detailed regional-scale cropping practices on a repetitive basis.