Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery

Segmentation and object-oriented processing of single-season and multi-season Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data was utilized for the classification of wetlands in a 1560 km² study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional maximum likelihood algorithm (MLC) in accurately mapping wetlands, with overall accuracies of 90.2% (single-season imagery) and 90.8% (multi-season imagery), compared to overall accuracies for the MLC classifiers of 78.4 and 79.0%, respectively. Kappa coefficients were over 1.5-times greater for the segmentation/object-oriented classifications than for the MLC classifications, and producer and user accuracies were also higher. The producer accuracies of the segmentation/object-oriented classifications were 90.8% (single-season) and 91.6% (multi-season), compared to 70.6 and 74.4%, respectively, for the MLC classifications. User accuracies were 73.9 and 73.5% for the single-season and multi-season segmentation/object-oriented classifications, respectively, compared to 54.1% (single-season) and 55.0% (multi-season) for the MLC classifications. The use of multi-seasonal data resulted in only a slight increase in overall accuracy over the single-season imagery. This small increase was primarily due to better discrimination of riparian wetlands in the multi-season data. Segmentation and object-oriented processing provides a low-cost, high-accuracy method for classification of wetlands on a local, regional, or national basis.     

Land degradation monitoring using multi‐temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China

Land degradation is one of the most pressing problems of environments. This research presents a methodology to monitor land degradation in a transition zone between grassland and cropland of northeast China, where soil salinization and grassland degradation, even desertification, have been observed in the past few decades. Landsat TM/ETM data in 1988, 1996 and 2001 were selected to determine the rate and status of grassland degradation and soil salinization together based on both decision tree (DT) classifier and the field investigation. The thermal radiance values of TM/ETM 6 data, the Normalized Difference Vegetation Index (NDVI), and new variables (brightness, greenness, and wetness) generated by the Kauth–homas Transforms (KT) algorithms from Landsat TM/ETM data served as the feature nodes of a DT classifer and contributed to improving the classification results. It showed an overall accuracy of more than 85% and a Kappa statistic of agreement of about 0.79 in 1996 and 2001 with the exception of about 0.69 in 1988. The statistical areas of land degradation in the observation periods revealed that land degradation, especially the salt‐affected soil, is accelerating. The distribution maps of land degradation in the years of 1988, 1996 and 2001 were generated respectively based on the classification results. Their change maps were created by the difference between the distribution maps from 1988 to 1996 and from 1996 to 2001 respectively. The changes of salt‐affected soil occurred near the water bodies due to variations of water sizes, and most of the degraded grassland appeared around the salt‐affected soil. Although climate variations play an important role in this region, human activities are also crucial to land degradation.     

Estimation of instantaneous air temperature above vegetation and soil surfaces from Landsat 7 ETM+ data in northern Germany

The temperature–vegetation index method (TVX method, also called contextual method) for the area-wide mapping of instantaneous air temperature is adopted for use with Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The method requires multispectral data consisting of bands in the red, near-infrared and thermal spectral regions, and no additional data. The approach is complemented with an iterative filtering routine for eliminating outliers and an interpolation algorithm for filling data gaps. The adopted method is applied to a multi-temporal dataset of nine ETM+?scenes, covering large parts of north-eastern Germany including the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site. Thus, for the first time the TVX method is applied to fine spatial resolution data and a central European region. The satellite-derived air temperatures (60 m spatial resolution) are compared with in situ measurements, showing an average error of about 3 K (root mean square, RMS), whereas the mean error in land surface temperature (LST) estimation is about 2 K. The results compare well with the in situ values throughout all seasons. The accuracy of about 3 K is in line with previously reported results for the TVX method (employing medium spatial resolution data) as well as for physically based approaches (ecosystem- or energy-balance models). Only remote sensing models incorporating in situ air temperature (as training data for neural networks or in multiple regression analysis) are reported to perform better in terms of RMS deviations. In the past, overestimation of air temperature by the TVX method was repeatedly observed. It is shown that the remote sensing approach tends to under- or overestimate the in situ air temperatures, depending on the in situ measurement heights. In conjunction with the attempt to assign the satellite-derived air temperature to a certain height above ground, the possibility of a simple correction for reference height is investigated. Over- and underestimations larger than 2 K seem to reflect existing differences in temperature rather than calculation errors. Furthermore, the dependence of the derived air temperature spatial pattern on different moving window sizes is shown. Possible sources of errors and limitations of the approach are discussed in detail.     

A comparative assessment of different methods for Landsat 7/ETM+ pansharpening

This article compares a set of relevant methods, based on different mathematical approaches, for Landsat 7 Enhanced Thematic Mapper Plus (ETM+) pansharpening. These are classical procedures such as principal component analysis and fast intensity hue saturation; methods based on wavelet transforms, such as wavelet à trous, additive wavelet luminance proportional and multidirectional–multiresolution methods; a method of a geostatistical nature, called downscaling cokriging (DCK); and finally, a Bayesian method (1cor). The comparison of the fused images is based on the qualitative and quantitative evaluation of their spatial and spectral characteristics by calculating statistical indices and parameters that measure the quality and coherence of the images. Moreover, the quality of the spectral information is studied indirectly by means of the Iterative Self-Organizing Data Analysis Technique (ISODATA) classification of the products of fusion. The results show that DCK and 1cor methods yielded better results than the wavelet-based methods. Particularly, DCK does not introduce artefacts in the estimation of the digital numbers corresponding with the source multispectral image and, therefore, it can be considered as the most coherent method.     

A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

To map Arctic lithology in central Victoria Island, Canada, the relative performance of advanced classifiers (Neural Network (NN), Support Vector Machine (SVM), and Random Forest (RF)) were compared to Maximum Likelihood Classifier (MLC) results using Landsat-7 and Landsat-8 imagery. A ten-repetition cross-validation classification approach was applied. Classification performance was evaluated visually and statistically using the global classification accuracy, producer’s and user’s accuracies for each individual lithological/spectral class, and cross-comparison agreement. The advanced classifiers outperformed MLC, especially when training data were not normally distributed. The Landsat-8 classification results were comparable to Landsat-7 using the advanced classifiers but differences were more pronounced when using MLC. Rescaling the Landsat-8 data from 16 bit to 8 bit substantially increased classification accuracy when MLC was applied but had little impact on results from the advanced classifiers.     

Estimation of the North–South Transect of Eastern China forest biomass using remote sensing and forest inventory data

The assessment of forest biomass is required for the estimation of carbon sinks and a myriad other ecological and environmental factors. In this article, we combined satellite data (Thematic Mapper (TM) and Moderate Resolution Imaging Spectrometer (MODIS)), forest inventory data, and meteorological data to estimate forest biomass across the North–South Transect of Eastern China (NSTEC). We estimate that the total regional forest biomass was 2.306 × 10⁹ Megagrams (Mg) in 2007, with a mean coniferous forest biomass density of 132.78 Mg ha^?1 and a mean broadleaved forest biomass density of 142.32 Mg ha^?1. The mean biomass density of the entire NSTEC was 129 Mg ha^?1. Furthermore, we analysed the spatial distribution pattern of the forest biomass and the distribution of biomass along the latitudinal and longitudinal gradients. The biomass was higher in the south and east and lower in the north and west of the transect. In the northern part of the NSTEC, the forest biomass was positively correlated with longitude. However, in the southern part of the transect, the forest biomass was negatively correlated with latitude but positively correlated with longitude. The biomass had an increasing trend with increases in precipitation and temperature. The results of the study can provide useful information for future studies, including quantifying the regional carbon budget.     

Spatial–temporal patterns of urban anthropogenic heat discharge in Fuzhou,China, observed from sensible heat flux using Landsat TM/ETM+ data

The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment. We used a heat balance model and satellite images (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images acquired in 1989 and 2001), together with meteorological station data to assess the urban thermal environment in the city of Fuzhou, China. The objective of this study was to estimate the anthropogenic heat discharge in the form of sensible heat flux in complex urban environments. In order to increase the accuracy of the anthropogenic heat flux analysis, the sub-pixel fractional vegetation cover (FVC) was calculated by linear spectral unmixing. The results were then used to estimate latent heat flux in urban areas and to separate anthropogenic heat discharge from heat radiation due to insolation. Spatial and temporal distributions of anthropogenic heat flux were analysed as a function of land-cover type, percentage of impervious surface area, and FVC. The accuracy of heat fluxes was assessed using the ratios of sensible heat flux (H), latent heat flux (L), and ground heat flux (G) to net radiation (R _n), which were compared to the results from other studies. It is apparent that the contribution of anthropogenic heat is smaller in suburban areas and larger in high-density urban areas. However, seasonal disparities of anthropogenic heat discharge are small, and the variance of anthropogenic heat discharge is influenced by urban expansion, land-cover change, and increasing energy consumption. The results suggest that anthropogenic heat release probably plays a significant role in the UHI effect, and must be considered in urban climate change adaptation strategies. Remote sensing can play a role in mapping the spatial and temporal patterns of UHIs and can differentiate the anthropogenic heat from the solar radiative fluxes. The findings presented here have important implications for urban development planning.     

Estimation of chlorophyll‐a concentration in Lake Tai,China using in situ hyperspectral data

Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll‐a (chl‐a) concentration. Twenty‐eight spectral reflectance curves were standardized and correlated with chl‐a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl‐a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R ₆₆₇), chl‐a concentration was not accurately estimated at R ² = 0.494. Accuracy of estimation was improved to R ² = 0.817 using the maximum reflectance. A higher accuracy of 0.837 was achieved using the peak reflectance at 719 nm (R ₇₁₉) because it does not drift with the level of chl‐a concentration. The highest accuracy of estimation was achieved at R ² = 0.868 using R ₇₁₉/R ₆₆₇.     

Characterizing post‐fire vegetation recovery of California chaparral using TM/ETM+ time‐series data

A time series of normalized difference vegetation index (NDVI) data derived from 11 TM/ETM+ images was used to examine the recovery characteristics of chaparral vegetation in a small watershed near Santa Barbara, California following a fire event in 1985. The NDVI recovery trajectory was compared to a generalized recovery trajectory of leaf area index (LAI) for the same region, which was established using a chronosequence approach and TM/ETM+ data. Post‐fire NDVI recovery trajectories were derived for the entire catchment and for individual vegetation types. Post‐fire NDVI spatial patterns on each image date were compared to the pre‐fire pattern to determine the extent to which the pre‐fire pattern was re‐established, and the rate of this recovery. Results indicated that the post‐fire recovery trajectory for the catchment area average NDVI was similar to the previously established regional LAI trajectory based on a chronosequence approach. The NDVI recovery was disrupted by drought stress and attained pre‐fire levels approximately 10 years after the fire. Individual vegetation types did not exhibit different rates of recovery and the recovery trajectories were only distinguished by the maximum post‐fire NDVI observed after 10 years. The post‐fire NDVI spatial pattern also showed a systematic return to pre‐fire conditions, but exhibited a more substantial disruption due to drought stress than was the case for the average NDVI recovery trajectory.     

Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Land‐cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land‐cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet‐merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey‐level co‐occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum‐likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land‐cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8–6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land‐cover classification accuracies.     

Eco‐environmental evaluation with remote sensing in the middle and upper areas of the Yellow River drainage basin

Based on remote sensing data and investigations, this paper focuses mainly on a general evaluation of environmental quality in the middle‐upper areas of the Yellow River region using GIS technology, Arc/Info, and ArcView. In this paper, the upper areas of the Yellow River regions are divided into three parts: the north‐east Qinghai‐Tibet Plateau ecosystem areas, the Erdos Plateau‐Hetao Plain ecosystem areas, and the Loess‐Plateau ecosystem areas. The study analyses the current environmental situation and the overall environmental quality in each ecosystem area and determines the law of distribution of environmental quality in the three areas. Finally, the paper provides some suggestions to improve the ecological environment in each area, in the hope of yielding a scientific foundation based on planning ecological environment protection in the Yellow River basin.     

Land‐cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling

A land‐cover classification is needed to deduce surface boundary conditions for a soil–vegetation–atmosphere transfer (SVAT) scheme that is operated by a geoecological research unit working in the Andes of southern Ecuador. Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to classify distinct vegetation types in the tropical mountain forest. Besides a hard classification, a soft classification technique is applied. Dempster–Shafer evidence theory is used to analyse the quality of the spectral training sites and a modified linear spectral unmixing technique is selected to produce abundancies of the spectral endmembers. The hard classification provides very good results, with a Kappa value of 0.86. The Dempster–Shafer ambiguity underlines the good quality of the training sites and the probability guided spectral unmixing is chosen for the determination of plant functional types for the land model. A similar model run with a spatial distribution of land cover from both the hard and the soft classification processes clearly points to more realistic model results by using the land surface based on the probability guided spectral unmixing technique.     

Mapping the concentrations of total suspended matter in Lake Taihu,China, using Landsat‐5 TM data

Remote sensing techniques can be used to estimate and map the concentrations of suspended matter in inland water, providing both spatial and temporal information. Although an empirical approach to remote sensing of inland waters has been carried out frequently, satellite imagery has not been incorporated into routine lake monitoring programmes due in part to the lack of a standard prediction equation with multi‐temporal capacity for suspended matter. Empirical and physical models must be developed for each lake and its corresponding turbidity composition if they are to be compared over time, or with other bodies of water.

This study aimed to develop and apply multi‐temporal models to estimate and map the concentrations of total suspended matter (TSM) in Lake Taihu, China. Two Landsat‐5 Thematic Mapper (TM) images and nearly contemporaneous in situ measurements of TSM were used. A modified Dark‐Object Subtraction (DOS) method was used, and appeared to be adequate for atmospheric correction. The relationships were examined between TSM concentrations and atmospherically corrected TM band and band ratios. Results of this study show that the ratio TM4/TM1 has a strong relationship with TSM concentrations for lake waters with relatively low concentrations of phytoplankton algae. However, TM3 provided a strong predictive relationship with TSM concentrations despite varied water quality conditions. Different prediction models were developed and compared using multiple regression analysis. The Akaike Information Criteria (AIC) approach was used to choose the best models. The validation of the multi‐temporal capability of the best models indicated that it is feasible to apply the linear regression model using TM3 to estimate TSM concentrations across time in Lake Taihu, even if no in situ data were available.     

Biomass measurements and relationships with Landsat‐7/ETM+ and JERS‐1/SAR data over Canada's western sub‐arctic and low arctic

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10–100 t ha^?1 for woodlands, 1–100 t ha^?1 for shrub sites, and 0.5–10 t ha^?1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r ² = 0.72 when using a one‐step approach (i.e. using all points) and 0.78 when using a two‐step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two‐step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two‐step approach over the one‐step approach, however, is not significant for the validation dataset, suggesting that the one‐step approach is as good as the two‐step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report.     

Observations of volcanic ash clouds in the 10-12 μm window using AVHRR/2 data

Abstract

Volcanic aerosols are always present in the atmosphere, but because of the nature of volcanic activity their abundance varies greatly with time. The problem of detecting and monitoring volcanic ash clouds using radiance measurements from the AVHRR/2 (Advanced Very High Resolution Radiometer) instrument is discussed and some results are presented for the Galunggung eruptions of July 1982. It is shown that during the first few hours of an explosive eruption AVHRR/2 thermal channel measurements can be used to detect and discriminate volcanic clouds. Once the eruption cloud has spread and thinned out however, the problem of detection is difficult because of the similarity between dispersed volcanic cloud and semi-transparent cirrus. In these cases, if the volcanic cloud consists of liquid H₂SO₄ droplets, then it is possible to discriminate them from water/ice clouds because of the reverse absorption effect in channel-4 and channel-5. Some evidence is presented showing this effect. It is proposed that the temperature difference image be used operationally to warn of the presence of volcanic clouds.     

Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District,Vietnam

Shrimp culture is a sector of aquaculture that has a high potential for poverty alleviation and rural development in Vietnam. However, the development of this activity induces changes that potentially have negative impacts on the environment, one of which is wetland deterioration. This paper describes the use of a proposed change detection methodology in the assessment of mangrove forest alterations caused by aquaculture development, as well as the effectiveness of the measures taken to mitigate deforestation in the district of Giao Thuy, Vietnam, between 1986, 1992 and 2001. Geometric and radiometric corrections were applied to Landsat images prior to identifying changes through comparison of unsupervised classifications. Changes were afterwards validated using a thresholding method based on Tasselled Cap feature image differencing and a rule‐based feature selection matrix. The matrix is used to identify the feature that is most efficient at detecting the presence of change between given land‐cover classes. The proposed approach aims to minimize commission errors in the post‐classification change detection process. The results suggest that 63% of mangrove areas apparent in 1986 had been replaced by shrimp ponds in 2001. Between 1986 and 1992, 440 ha of adult mangrove trees had disappeared, whereas the mangrove extent increased by 441 ha between 1992 and 2001. This recovery is attributed to reforestation projects and conservation efforts that promoted natural regeneration.     

Achieving high sensing in 0.5 nA for the driving pixel currents in AMOLEDs with settling time of 7 µs by a new external current sensing circuit

A new external current sensing circuit with baseline compensation for the active matrix organic light emitting diode (AMOLED) display is developed herein to achieve the sensing precision of 0.5 nA in pixel with 7 µs of settling time. Current sensing circuit incorporates a new push–pull transient current feedforward whereas the current analog to digital converter (CADC) based digital baseline current compensation incorporates an 11-bit current digital-to-analog converter, a current comparator and a digital control circuit with an 11-bit successive approximation register. The proposed integrated mixed signal IC drives a 6T1C pixel-based AMOLED panel with one horizontal time of 7.7 µs at a scan frequency of 60 Hz. The design readout chip can simultaneously sense and compensate TFT baseline current variation. The readout circuit and the baseline compensation circuit are implemented in the integrated chip with chip area of 125 μm × 46 μm and fabricated via TSMC T18 process. With the standard 3.3 V supply, experimental result shows that the overall power consumption of the chip is 988 µW watt. The minimum LSB current for the CADC is 10 nA and the maximum achievable sampling rate is 500 KS/s. The measured INL and DNL of CADC is 0.84 and 0.98 respectively. Despite of heavy data line parasitic capacitances (2.6 KΩ/20 pF) of the AMOLED display, experimental results show that the proposed circuit can sense 0.5 nA current within 7 µs of settling time. The sensing precision of 0.5 nA within 7 µs are the best among all reported literature to date whereas the current sense range (0.5–500 nA), system sampling rate (142 KS/s), INL (0.84) and DNL (0.98) of the CADC is approximately comparable among all reported.

     

Reliable estimation of evapotranspiration on agricultural fields predicted by the Priestley–Taylor model using soil moisture data from ground and remote sensing observations compared with the Common Land Model

Evapotranspiration (ET) is a crucial factor in understanding the hydrological cycle and is essential to many applications in hydrology, ecology and water resources management. However, reliable ET measurements and predictions for a range of temporal and spatial scales are difficult. This study focused on the comparison of ET estimates using a relatively simple model, the Priestley–Taylor (P-T) approach, and the physically based Common Land Model (CLM) using ground and remotely sensed soil moisture data as input. The results from both models were compared directly with hourly eddy covariance measurements at two agricultural field sites during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in the corn soybean production region in the Upper Midwest, USA. The P-T model showed a significant overestimation of the potential ET compared to the measurements, with a root mean square error (RMSE) between 115 and 130 W m^–2. Actual ET was better predicted by the CLM, with the RMSE ranging between 50 and 75 W m^–2. However, actual ET from the P-T model constrained with a soil moisture dependency parameterization showed improved results when compared to the measurements, with a significantly reduced bias and RMSE values between 60 and 65 W m^–2. This study suggests that even with a simple semi-empirical ET model, similar performance in estimating actual ET for agricultural crops compared to more complex land surface–atmosphere models (i.e. the CLM) can be achieved when constrained with the soil moisture function. This suggests that remote sensing soil moisture estimates from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and others such as the Soil Moisture and Ocean Salinity (SMOS) mission may be effective alternatives under certain environmental conditions for estimating actual ET of agricultural crops using a fairly simple algorithm.     

Analysis of green space in Chongqing and Nanjing,cities of China with ASTER images using object‐oriented image classification and landscape metric analysis

Green space is an important urban land use which can enhance the livability of cities. Chinese cities develop rapidly, and increasingly strong emphasis has been put on the provision of better landscape and more green space. We used an object‐oriented approach to classify different land covers in Chongqing and Nanjing, two historical Chinese cities. Suitable segmentation levels were selected by locating break points along the variation of selected object variables. Three segmentation levels were identified for each city. Object variables with good discriminatory power were selected to identify different land covers by making use of their spectral, textural and shape properties. Decision tree classifiers were formulated for classifying images into eight land cover classes. Accuracy of object‐oriented classification was the highest in Chongqing and ranked second in Nanjing. The result was compared to those of maximum likelihood classification, fuzzy classification and linear unmixing classification. Land covers were then generalized as green space for landscape metric analysis. The fragmented nature of green space was discussed. It was revealed that there existed a general lack of green space in old urban centres. With an increasing distance from city centres, more large patches were found.