Data For Land Cover Classification Research Articles

Estimating historical changes in global land cover: Croplands from 1700 to 1992

Human activities over the last three centuries have significantly transformed the Earth's environment, primarily through the conversion of natural ecosystems to agriculture. This study presents a simple approach to derive geographically explicit changes in global croplands from 1700 to 1992. By calibrating a remotely sensed land cover classification data set against cropland inventory data, we derived a global representation of permanent croplands in 1992, at 5 min spatial resolution [Ramankutty and Foley, 1998]. To reconstruct historical croplands, we first compile an extensive database of historical cropland inventory data, at the national and subnational level, from a variety of sources. Then we use our 1992 cropland data within a simple land cover change model, along with the historical inventory data, to reconstruct global 5 min resolution data on permanent cropland areas from 1992 back to 1700. The reconstructed changes in historical croplands are consistent with the history of human settlement and patterns of economic development. By overlaying our historical cropland data set over a newly derived potential vegetation data set, we analyze our results in terms of the extent to which different natural vegetation types have been converted for agriculture. We further examine the extent to which croplands have been abandoned in different parts of the world. Our data sets could be used within global climate models and global ecosystem models to understand the impacts of land cover change on climate and on the cycling of carbon and water. Such an analysis is a crucial aid to sharpen our thinking about a sustainable future.

A GLOBE Collaboration To Develop Land Cover Data Collection and Analysis Protocols.

Global Learning and Observations to Benefit the Environment (GLOBE) is an international environmental education and science partnership which coordinates the work of students (aged 5 to 18), teachers and scientists from 48 countries on five continents to study and better understand the global environment. Accurate ground reference data is fundamental to the use of remotely sensed data for land cover classification and mapping. Because very little ground reference data has been collected, the accuracy of many land cover maps may be questioned, thus accurate land cover ground reference data is an important need that could be addressed through GLOBE scientist-student collaboration. If earth systems scientists are to use student data, it is important that those data be as accurate as possible to ensure reliability of research results. Thus a key question for this research is whether student collected data are accurate enough to support rigorous scientific investigations. This paper describes results of the GLOBE Science-Education Team on Data Validation and Accuracy Assessment's collaboration with teachers and students to: (1) design and test the pre-protocol learning activities; (2) test the protocols intended to guide the collection and analysis of data; and (3) implement the learning activities and protocols to determine the relative accuracy of student collected versus professionally collected land cover data. To ensure the most accurate classification of land cover possible, a new international hierarchical land cover classification system, the Modified Unesco Classification (MUC) system was developed. GLOBE Data Collection Protocols and methods were designed and implemented to test the accuracy of student collected reference data were designed and implemented. Students who collected land cover reference data using GLOBE protocols, obtained data which are at least as accurate as that collected by professionals.

UNITED STATES LAND USE INVENTORY FOR ESTIMATING BIOGENIC OZONE PRECURSOR EMISSIONS

The U.S. Geological Survey’s (USGS) EROS (Earth Resources Observation System) Data Center’s (EDC) 1-km classified land cover data are combined with other land use data using a Geographic Information System (GIS) to create the Biogenic Emissions Landcover Database (BELD). The land cover data are being used to estimate biogenic emissions in the contiguous United States. These emissions include volatile organic compound (VOC) emissions from vegetation and nitric oxide (NO) from soils. The EDC data are used predominately in the western United States, while other sources, such as the U.S. Department of Agriculture’s Census of Agriculture and the U.S. Forest Service Eastwide Forest Inventory and Analysis Database (EWDB), are used in the eastern United States. The EROS Data Center land cover classifications must be used with caution in heterogenous areas. Emission factors vary drastically by specific crop and tree genera, and mixed classes in the EDC scheme may not always accurately reflect the actual crop/genus mix. However, future use of satellite-derived vegetation indices and other land cover characteristics may prove useful in understanding geographic distributions of foliar mass and seasonal variation in Leaf Area Index (LAI), which are important drivers in biogenic emission models.

Generating confidence intervals for composition-based landscape indexes

Many landscape indexes with ecological relevance have been proposed, including diversity indexes, dominance, fractal dimension, and patch size distribution. Classified land cover data in a geographic information system (GIS) are frequently used to calculate these indexes. However, a lack of methods for quantifying uncertainty in these measures makes it difficult to test hypothesized relations among landscape indexes and ecological processes. One source of uncertainty in landscape indexes is classification error in land cover data, which can be reported in the form of an error matrix. Some researchers have used error matrices to adjust extent estimates derived from classified land cover data. Because landscape diversity indexes depend only on landscape composition – the extent of each cover in a landscape – adjusted extent estimates may be used to calculate diversity indexes. We used a bootstrap procedure to extend this approach and generate confidence intervals for diversity indexes. Bootstrapping is a technique that allows one to estimate sample variability by resampling from the empirical probability distribution defined by a single sample. Using the empirical distribution defined by an error matrix, we generated a bootstrap sample of error matrixes. The sample of error matrixes was used to generate a sample of adjusted diversity indexes from which estimated confidence intervals for the diversity indexes were calculated. We also note that present methods for accuracy assessment are not sufficient for quantifying the uncertainty in landscape indexes that are sensitive to the size, shape, and spatial arrangement of patches. More information about the spatial structure of error is needed to calculate uncertainty for these indexes. Alternative approaches should be considered, including combining traditional accuracy assessments with other probability data generated during the classification procedure.

Land cover classification with AVHRR multichannel composites in northern environments

The objectives of this study were to test the usefulness of various spectral channel combinations of AVHRR multitemporal composites for deriving land cover information in northern environments, and to assess the effect of AVHRR spatial resolution on the classification accuracy. A sequence of operations was carried out to remove radiometric distortions from AVHRR composites (1 km pixel size) prepared for the landmass of Canada using multidate NOAA-I1 data for the 1993 growing season: atmospheric corrections for AVHRR Channels 1, 2, and 4; identification and replacement of cloud-contaminated pixels; bidirectional reflectance corrections of Channels 1 and 2; and principal component (PC) calculations to retain significant independent PC channels. Input principal components were classified using an unsupervised clustering algorithm, and accuracies were assessed through a comparison to 30 m Landsat TM pixels at five different sites in three biomes. We found that the normalized difference vegetation index (NDVI) was the most effective single spectral dimension to derive land cover types, but other channels (especially 1 and 2) were needed to obtain highest accuracies. Overall, classification accuracies for the 30 m pixels were between 45% and 60%. Mixes of land cover classes within AVHRR pixels were the principal reason for the low accuracies. When considering only AVHRR pixels with one dominant land cover type, the accuracy increased up to 80% or more in proportion to the mixed types retained. The accuracy also increased when a dispersed class (mixed forest) was combined with the more ubiquitous coniferous forest class. The intrinsic AVHRR resolution and the compositing process are the major factors influencing the impact of mixed cover types on the classification accuracy. The impact of these factors is discussed and strategies for optimizing the use of multitemporal AVHRR data in land cover classification are suggested.

特集 応用地質における画像処理・解析 土木地質におけるリモートセンシングの活用に関する一考察 リニアメント判読と地すべり危険域の評価

Application method of satellite remote sensing for engineering geology were examined. Literature survey revealed the present condition of application of remote sensing for engineering geology.Two examples were studied on the bassis of literature survey. One was the study of identify of lineaments using remote sensing data, while the other was the prediction of landslide.1) To identify of lineaments: The lineaments which were identified through interpretation of LANDSAT TM images were coincide with those identified through aerial photographs. It was found more effective to survey lineaments using both data.2) To predict landslides: The regions suspectible to landslide were predicted using LANDSAT TM data and digital geographic information. Land cover classification data and vegitation index were caluculated from remote sensing data. It was confirmed more effective to predict the regions suspectible to landslide.

Classification of SPOT HRV imagery and texture features

Abstract Spatial co-occurrence matrices were computed for a SPOT HRV multispectral image for a moderate-relief environment in eastern Canada. The texture features entropy and inverse difference moment were used with the spectral data in landcover classification, and substantive increases in accuracy were noted. These range from 10 per cent for exposed bedrock to over 40 per cent in forest and wetland classes. The average classification accuracies were increased from 511 per cent (spectral data alone) to 86.7 per cent (spectral data plus entropy measured in band 2 and inverse difference moment in band 3). Classes that are homogeneous on the ground were characterized adequately by spectral tone alone, but classes containing mixed vegetation patterns or strongly related to structure were characterized more accurately by using a mixture of spectral tone and texture.