High Spatial Resolution Satellite Data Research Articles

Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area

The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km2) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.

Investigation of Kelvin Waves in the Stratosphere Using FORMOSAT-3/COSMIC Temperature Data

Temperature data from Global Positioning System based Radio Occultation (GPS RO) soundings of the Formosa Satellite mission 3/Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC or F-3/C) micro satellites has been investigated in detail to study the Kelvin wave properties. The high temporal and spatial resolution satellite data from August 2006 to August 2009 have enabled the investigation of Kelvin wave activity on each day. The dominant waves of wave numbers 1 and 2 (W1 and W2) have been investigated in detail at three altitudes—19, 25 and 30 km, and it is found that the amplitude of W1 is greater than that of W2 during 60% of the time. A statistical study of the amplitudes of W1 and W2 is also presented and it is found that the dominant amplitudes are 0.5 to 1.0 K for both waves. At lower altitudes (19 km), the amplitudes of W1 are larger and the distribution is also broader. The amplitudes of both waves in the stratosphere are higher during the easterlies of the quasi-biennial oscillation (QBO) and are maximum when the zonal wind changes from easterlies to westerlies. In the lower altitudes near the tropopause they vary in consonance with the outgoing long wave radiation, a proxy of deep convection. Deduction of the Kelvin wave periods and phase velocities has been possible with better accuracy with the use of the F-3/C data. The average periods of W1 for all years are 15 ± 3, 13 ± 4, and 10 ± 3 days at altitudes 19, 25, and 30 km, respectively and the average periods of W2 for all years are 10 ± 2, 7 ± 2, and 6 ± 2 days, respectively. These standard deviations are geophysical and are due to the variation in the periods of the individual Kelvin wave events and identification of the period for a single Kelvin wave event is correct to within ± one day. We found that the Kelvin waves of both the zonal wave numbers are slow in the lower altitudes and fast in the higher altitudes. Also, the periods decrease gradually with height. This is the most important result of the present study.

Spatial relationships between surface sedimentary facies distribution and topography using remotely sensed data: Example from the Ganghwa tidal flat, Korea

Spatial relationships between surface sedimentary facies distribution and topography, including channel networks and an intertidal digital elevation model (DEM), were estimated for the Ganghwa tidal flat, Korea. Tidal channels were extracted from high spatial resolution satellite data and used to derive maps of channel density and distance from channels. The intertidal DEM was generated by a waterline method using Landsat Enhanced Thematic Mapper Plus (ETM+) images, and the map of surface sedimentary facies distribution was produced from grain-size data on sediment samples. A GIS-based frequency ratio model was applied to quantify the relationships between the surface sediment facies and topographic factors. The result indicated that the fine-grained mud flat facies occurs mainly in areas of complex tidal channels and in elevated areas that have a high spectral reflectance in the Landsat ETM+ image. The coarse-grained sand flat facies is concentrated in areas of sparse tidal channels, and the mixed flat facies is distributed where tidal channels have an intermediate level of complexity. The results demonstrate a significant spatial relationship between the distribution of surface sediment in a tidal flat and topographic features. We conclude that tidal channels should be considered an important factor in mapping surface sedimentary facies in tidal flat environments, and that patterns of tidal topography are a useful adjunct to spectral reflectance in classifying sedimentary facies from remotely sensed data with high spatial resolution.

Mapping and interpreting historical land cover/land use changes in a Natura 2000 site using earth observational data: The case of Nestos delta, Greece

Land cover/land use (LCLU) transitions and landscape changes were mapped and interpreted in a deltaic Natura 2000 habitat site in northern Greece. Multi-temporal maps created over a period of about 65 years and based on panchromatic aerial photographs and high spatial resolution satellite data, helped to assess LCLU transformations. These changes were associated with human interference and major socio-economic processes occurring in the area during the study period. In 1945, forests and wetlands dominated the delta area, occupying 63% of the total land cover. Subsequently, these land cover types severely reduced in coverage, and agricultural areas dominated the landscape as a result of policies introduced to accommodate the settlement of refugees from Asia Minor. This transition increased landscape fragmentation in 1960. Due to relative increase of swap changes, the landscape became less heterogeneous in 1992. Finally, in 2002, agricultural areas exhibit a small decrease in coverage having converted back to natural areas following the introduction of EU Common Agricultural Policy. About 77% of the deltaic ecosystem experienced LCLU changes during the first period of the analysis (1945–1960). Almost two thirds of this change is attributed to intercategorical transitions, while the rest is related to swap changes. During the second period of the analysis (1960–1992), half the study area was transformed due to net and swap changes. Finally, in the third, most recent time period (1992–2002), 85% of the landscape showed persistence while net changes were twice as frequent as swap changes. Human interference was the key factor beyond the landscape transformations and intercategorical transitions observed in the delta. During the previous century natural habitats were pressured by major socio-economic processes that transformed their composition and structure. Assessing these changes is critical to develop area-specific policies that minimize negative influences and provide a framework for restoring habitats to their natural conditions.

Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data

Continuing, severe outbreaks of mountain pine beetle ( Dendroctonus ponderosae) across western North America have resulted in widespread mortality of lodgepole pine ( Pinus contorta). Multiple studies have used high spatial resolution satellite data to map areas of beetle kill; these studies have largely focused on mapping red canopy cover associated with recent tree mortality and have not examined mapping gray canopy cover that occurs after red needles have dropped. The work presented here examines the use of newly available GeoEye-1 data for mapping both red and gray canopy area in southeastern Wyoming lodgepole pine forest. A 0.5 m spatial resolution, pan-sharpened GeoEye-1 image was used to classify areas of green, red, and gray canopy cover. Reference data were collected at twelve 500 m 2 field plots. Shadow-normalized green, red, and gray canopy area from classified GeoEye-1 data closely agreed with field-estimated green, red, and gray canopy area. Mean absolute error in canopy cover for the twelve sample plots was 8.3% for the green class, 5.4% for the red class, and 7.2% for the gray class. When all twelve plots were aggregated, remotely sensed estimates of green, red, and gray cover were within 1.7% of the field-estimated cover. Our results demonstrate that high spatial resolution spaceborne multispectral data are a promising tool for mapping canopy mortality caused by mountain pine beetle outbreaks.

Object-based feature extraction using high spatial resolution satellite data of urban areas

Urban morphology is characterized by a complex and variable coexistence of diverse, spatially and spectrally heterogeneous objects. Built-up areas are among the most rapidly changing and expanding elements of the landscape. Thus, remote sensing becomes an essential field for up-to-date and area-wide data acquisition, especially in explosively sprawling cities of developing countries. The urban heterogeneity requires high spatial resolution image data for an accurate geometric differentiation of the small-scale physical features. This study proposes an object-based, multi-level, hierarchical classification framework combining shape, spectral, hierarchical and contextual information for the extraction of urban features. The particular focus is on high class accuracies and stable transferability by fast and easy adjustments on varying urban structures or sensor characteristics. The framework is based on a modular concept following a chronological workflow from a bottom-up segmentation optimization to a hierarchical, fuzzy-based decision fusion top-down classification. The workflow has been developed on IKONOS data for the megacity Istanbul, Turkey. Transferability is tested based on Quickbird data from the various urban structures of the incipient megacity Hyderabad, India. The validation of both land-cover classifications shows an overall accuracy of more than 81 percent.

Mapping diversified peri-urban agriculture – potential of object-based versus per-field land cover/land use classification

High spatial resolution satellite data contribute to improving land cover/land use (LCLU) classification in agriculture. A classification procedure based on Quickbird satellite image data was developed to map LCLU of diversified agriculture at sub-communal and communal level (7 km2). Segmentation performance of the panchromatic band in combination with high pass filters (HPF) was tested first. Accuracy of field boundary delineation was evaluated by an object-based segmentation, a per-field and a manual classification, along with a quantitative accuracy assessment. Sub-communal classification revealed an overall accuracy of 84% with a κ coefficient of 0.77 for the per-field vector segmentation compared to an overall accuracy of 56–60% and a κ coefficient of 0.37–0.42 for object-based approaches. Per-field vector segmentation was thus superior and used for LCLU classification at communal level. Overall accuracy scored 83% and the κ coefficient 0.7. In diversified agriculture, per-field vector segmentation and classification achieved higher classification results.

Quantitative estimation of intertidal sediment characteristics using remote sensing and GIS

High spatial resolution satellite data (IKONOS) combined with in situ data was used to quantitatively estimate the spatial distribution of tidal flat characteristics for the Hwangdo tidal flat, Cheonsu Bay, Korea. The classification result was accurate in terms of a comparison with the in situ data, and the overall accuracy was 90.7%, which confirmed the validity of the classification. GIS analysis based on a probabilistic model was applied to a quantitative estimation of the relationship between each surface sediment facies and the spectral reflectance. Mud flat facies showed a high positive correlation (R 2 = 0.91), and sand flat facies showed a high negative correlation (R 2 = 1.00), which was a good reflection of the sedimentary characteristics of Hwangdo tidal flat. Relationships between each sediment facies and DEM also showed good agreement with the topographic characteristics in the study area. The study revealed that intertidal surface sediment classification using high resolution remote sensing imagery and in situ data successfully shows spectral and topographic characteristics of the study area. It was noted that spectral reflectance was affected by a combination of environmental factors, including grain size, topography, and remnant surface water. It is possible to determine the type of tidal flat through quantitative estimates of the spatial distribution of surface sediments according to their spectral reflectance.

A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ∼1% for ANN and ∼6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

Gross forest cover loss in temperate forests: biome-wide monitoring results using MODIS and Landsat data

The temperate forest is a complex biome due to the diversity of forest types, forest cover change dynamics and forest use management practices. While temperate forests play an important role in the global carbon cycle, their net carbon exchange is uncertain. Quantifying forest cover change is an important step in documenting disturbance regimes and carbon exchange estimates. Biome-wide gross forest cover loss was estimated using a probability-based sampling approach that integrated moderate and high spatial resolution satellite data sets. Area of gross forest cover loss from 2000 to 2005 within the temperate forest biome is estimated to be 1.03% of the total biome area, or 18.41 Mha. Estimated forest cover loss represented a 3.5% reduction in year 2000 forest area. About 68% of the total forest cover loss occurred in Eastern North America and in Europe. The mid-latitude forests of the United States exhibited the highest forest cover loss rates within the biome. Biome-wide rate of gross forest cover loss gradually increased from 2001 to 2005. The lowest change was detected in 2004, followed by the year of the highest change over the 5-year period. The regional forest cover change dynamics were confirmed by official forest fire and timber production statistics. The validation of the MODIS-based product demonstrated its efficiency in forest cover mapping and monitoring. Forest cover change monitoring using the approach presented should bring greater understanding on forest cover dynamics in temperate forests and enable improved carbon accounting.

The potential of optical high resolution data for the assessment of leaf area index in East African rainforest ecosystems

Operational standard products of biophysical variables, such as leaf area index (LAI) derived from satellite observations (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS) or VEGETATION), serve the need for adequate input data for quantitative modelling of ecosystem dynamics on regional to global scales. For the validation of those products and for local applications, higher resolution LAI maps based on field and high spatial resolution satellite data are crucial. As validation sites in tropical rainforests are rare, a study was undertaken in a moist semi-deciduous tropical rainforest in East Africa. In situ LAI measurements were carried out with digital hemispherical photography on 30 test sites. Spectral vegetation indices (SVIs) and texture measures calculated from top-of-canopy Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Satellite Pour l'Observation de la Terre (SPOT)-4 High Resolution Visible and Infrared (HRVIR) reflectance data were compared with respect to their ability to predict effective LAI. Regression analyses showed that ASTER data were generally better suited, with simple ratio (SR) performing best for early and intermediate forest stages and texture measures (grey level co-occurrence matrix (GLCM) variance) derived from SWIR information rendering superior results in later forest stages. Finally, a high resolution LAI map could be constructed with an accuracy of 0.39.

Mapping urban and peri-urban agriculture using high spatial resolution satellite data

In rapidly changing peri-urban environments where biophysical and socio- economic processes lead to spatial fragmentation of agricultural land, remote sensing offers an efficient tool to collect land cover/land use (LCLU) data for decision-making. Compared to traditional pixel-based approaches, remote sensing with object-based classification methods is reported to achieve improved classification results in complex heterogeneous landscapes. This study assessed the usefulness of object-oriented analysis of Quickbird high spatial resolution satellite data to classify urban and peri-urban agriculture in a limited peri-urban area of Hanoi, Vietnam. The results revealed that segmentation was essential in developing the object-oriented classification approach. Accurate segmentation of shape and size of an object enhanced classification with spectral, textural, morphological, and topological features. A qualitative, visual comparison of the classification results showed successful localisation and identification of most LCLU classes. Quantitative evaluation was conducted with a classification error matrix reaching an overall accuracy of 67% and a kappa coefficient of 0.61. In general, object-oriented classification of high spatial resolution satellite data proved the promising approach for LCLU analysis at village level. Capturing small-scale urban and peri-urban agricultural diversity offers a considerable potential for environmental monitoring. Challenges remain with the delineation of field boundaries and LCLU diversity on more spatially extensive datasets.

Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data

A semi-physical fusion approach that uses the MODIS BRDF/Albedo land surface characterization product and Landsat ETM+ data to predict ETM+ reflectance on the same, an antecedent, or subsequent date is presented. The method may be used for ETM+ cloud/cloud shadow and SLC-off gap filling and for relative radiometric normalization. It is demonstrated over three study sites, one in Africa and two in the U.S. (Oregon and Idaho) that were selected to encompass a range of land cover land use types and temporal variations in solar illumination, land cover, land use, and phenology. Specifically, the 30 m ETM+ spectral reflectance is predicted for a desired date as the product of observed ETM+ reflectance and the ratio of the 500 m surface reflectance modeled using the MODIS BRDF spectral model parameters and the sun-sensor geometry on the predicted and observed Landsat dates. The difference between the predicted and observed ETM+ reflectance (prediction residual) is compared with the difference between the ETM+ reflectance observed on the two dates (temporal residual) and with respect to the MODIS BRDF model parameter quality. For all three scenes, and all but the shortest wavelength band, the mean prediction residual is smaller than the mean temporal residual, by up to a factor of three. The accuracy is typically higher at ETM+ pixel locations where the MODIS BRDF model parameters are derived using the best quality inversions. The method is most accurate for the ETM+ near-infrared (NIR) band; mean NIR prediction residuals are 9%, 12% and 14% of the mean NIR scene reflectance of the African, Oregon and Idaho sites respectively. The developed fusion approach may be applied to any high spatial resolution satellite data, does not require any tuning parameters and so may be automated, is applied on a per-pixel basis and is unaffected by the presence of missing or contaminated neighboring Landsat pixels, accommodates for temporal variations due to surface changes (e.g., phenological, land cover/land use variations) observable at the 500 m MODIS BRDF/Albedo product resolution, and allows for future improvements through BRDF model refinement and error assessment.

Per-pixel Classification of High Spatial Resolution Satellite Imagery for Urban Land-cover Mapping

Commercial high spatial resolution satellite data now provide a synoptic and consistent source of digital imagery with detail comparable to that of aerial photography. In the work described here, per-pixel classification, image fusion, and GIS-based map refinement techniques were tailored to pan-sharpened 0.61 m QuickBird imagery to develop a six-category urban land-cover map with 89.3 percent overall accuracy ( �� 0.87). The study area was a rapidly developing 71.5 km 2 part of suburban Raleigh, North Carolina, U.S.A., within the Neuse River basin. “Edge pixels” were a source of classification error as was spectral overlap between bare soil and impervious surfaces and among vegetated cover types. Shadows were not a significant source of classification error. These findings demonstrate that conventional spectral-based classification methods can be used to generate highly accurate maps of urban landscapes using high spatial resolution imagery.

Pixel Level Fusion of Panchromatic and Multispectral Images Based on Correspondence Analysis

A pixel level data fusion approach based on correspondence analysis (CA) is introduced for high spatial and spectral resolution satellite data. Principal component analysis (PCA) is a well-known multivariate data analysis and fusion technique in the remote sensing community. Related to PCA but a more recent multivariate technique, correspondence analysis, is applied to fuse panchromatic data with multispectral data in order to improve the quality of the final fused image. In the CA-based fusion approach, fusion takes place in the last component as opposed to the first component of the PCA-based approach. This new approach is then quantitatively compared to the PCA fusion approach using Landsat ETM� , QuickBird, and two Ikonos (with and without dynamic range adjustment) test imagery. The new approach provided an excellent spectral accuracy when synthesizing images from multispectral and high spatial resolution panchromatic imagery.

高分解能衛星データの森林モニタリングへの利用

高分解能衛星データの森林モニタリングへの利用

Remote sensing application for property tax evaluation

This paper presents a study for linking remotely sensed data with property tax related issues. First, it discusses the key attributes required for property taxation and evaluates the capabilities of remote sensing technology to measure these attributes accurately at parcel level. Next, it presents a detailed case study of six representative wards of different characteristics in Dehradun, India, that illustrates how measurements of several of these attributes supported by field survey can be combined to address the issues related to property taxation. Information derived for various factors quantifies the property taxation contributed by an average dwelling unit of the different income groups. Results show that the property tax calculated in different wards varies between 55% for the high-income group, 32% for the middle-income group, 12% for the low-income group and 1% for squatter units. The study concludes that higher spatial resolution satellite data and integrates social survey helps to assess the socio-economic status of the population for tax contribution purposes.

Using object‐oriented classification and high‐resolution imagery to map fuel types in a Mediterranean region

Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium‐resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object‐oriented classification. Fuel maps were derived from QuickBird imagery, which offers a panchromatic and four multispectral bands ranging from 0.61 to 2.44 m resolution. The image used for this paper dated from July 2002 and is located in the NW region of Madrid, Spain. The Prometheus system, a fuel type classification adapted to the ecological characteristics of the European Mediterranean basin, was adopted for this study. Viewed with high‐resolution imagery, fuel‐related features are often aggregations of pixels exhibiting a variety of spectral properties. Correct identification and classification of these objects requires an explicit consideration of spatial context. We used an object‐oriented approach, which allowed context consideration during the classification process, as a complement to traditional pixel‐based methods. The map created with this approach was assessed to have greater than 80% accuracy for the prediction of six fuel classes. Results suggested that object‐oriented classification of high‐resolution imagery has the potential to create accurate and spatially precise fuel maps.

Monitoring of environmental change in Dzungar Basin by the analysis of multi-temporal satellite data sets

The purpose of this study is to investigate the environmental change and its cause in Dzungar Basin of the Central Asia through an analysis of land cover condition and its change using new created multi-temporal satellite data sets. The multi-temporal and multi-stage satellite data sets were firstly produced by using high spatial resolution satellite data and wide observation satellite data. Next, fluctuations of the past about 50 years in the water area of closed lakes were investigated in detail by analyzing the data sets. Changes in the irrigated agricultural lands along the inflow rivers to the lakes, and the snow and glacier area covering the mountainous district were also investigated. A change in the long-term scale of a lake was estimated by using the DEM data based on ASTER image with the historical record of terraces distributed around the former water area of the lake. Finally, natural and artificial causes of the change in the object area were examined by comparing the satellite data analysis results with meteorological data and auxiliary sources. The study results showed most of closed lakes in Dzungar Basin have shown the rapid shrinkages in the past about 50 years. However, it changed into the remarkable expansion of the water area since 2002. As for a cause of the change in closed lakes due to the analysis result for changes in the irrigated agricultural lands, snow and glacier extents, it was shown that the influence of human activities such as development of irrigation lands was bigger than the one caused by natural fluctuation based on the climate change.

高解像度衛星データを用いた小規模溜池の水文立地に関する画像判読

タイ国東北部の小規模溜池を対象として，フィールドにおける水文・水利学の知見に基づいた画像判読について紹介する．高解像度衛星データであるQuickBirdを用いた判読の結果，1）立地に応じた溜池の判読・分類が可能である，2）溜池の取水口・配置から集水域の接続方向を推定することが可能，3）こうした手法は，DEMなどから作成された落水網の精度検証に役立つ可能性があることが示唆された．複数の画像判読を通じて，フィールドでの調査・研究に対する高解像度衛星データの画像判読の有効性が確認された．