Forest tree species identification and its response to spatial scale based on multispectral and multi-resolution remotely sensed data.

The effect of spatial scale could not be ignored in identification results of forest types generated by multi-resolution images, and the influence of adding texture information from remote sensing data on the accuracy of forest trees species identification at different spatial resolutions has not been clearly addressed. To clarify this situation, we studied the Wangyedian forest farm in Northeast China, by using quasi-synchronous and geographical coordinate matched multi-resolution satellite observations (six spatial resolution levels: 1, 2, 4, 8, 16 and 30 m) which were supported with GF-1 PMS (pan and multi-spectra sensor), GF-2 PMS, GF-1 WFV (wide field view) and Landsat-8 OLI (operational land imager) and could investigate any possible correlations between spatial resolution and the recognition result, besides the influence of adding texture information. Five dominant tree species were classified and identified using Support Vector Machine (SVM) classifier. We also examined the identification results of the dominant forest trees species obtained by using the up-scaling algorithm. The results showed that overall classification accuracy of tree species was significantly influenced by the spatial resolution of images. The highest accuracy at the 4 m resolution, and the accuracy decreased to a minimum as the resolution reduced to 30 m. The addition of texture information increased classification accuracy using multispectral imagery with resolutions from 1 to 8 m, and the overall accuracy of dominant tree species identification created after adding texture information was 2.0%-3.6% higher than that from results of spectral information alone in the study area. However, the improvement of accuracy did not appear to hold for medium resolution imagery (16 and 30 m spatial resolution). In addition, there was a significant difference between the multi-scale classification results provided by up-scaled images and that obtained from native remote-sensing images for each spatial scale. These results indicated that the real satellite images should be used to ensure the accuracy of results when we examine multi-spatial-scale remote sensing observations or applications.

[Analysis on Diurnal Variation of Chlorophyll-a Concentration of Taihu Lake Based on Optical Classification with GOCI Data].

Chlorophyll-a (Chl-a) concentration is one of the most important parameters for the analysis of inland water quality. Remote sensing data with the advantages of wide spatial area and multi-temporal monitoring has been applied as a reliable source of Chl-a concentration. However, as optical characteristics of inland water bodies are complex with high spatial and temporal (diurnal) variations, there are still limitations to estimate Chl-a concentration with traditional remote sensing data and single model. In the proposed solution, the first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), which provides an image per hour (eight images per day from 8:16 to 15:16), was used as a data source of Taihu Lake. Based on hierarchical clustering method, water types were identified from in situ normalized spectral reflectance collected in Taihu Lake (216 samples in different seasons from 2010 to 2012). Then eight GOCI images which were obtained on May 6th, 2012 were classified separately according to different water types by calculating spectral angle distance between each spectrum in GOCI images and the classified spectra. According to the classified remote sensing images and the spectral bands of GOCI data, classed-based models were subsequently developed for the estimation of Chl-a concentration. The results indicated that four water types (Type 1 to Type 4) were identified based on the in situ normalized spectral reflectance in Taihu Lake. The spectra of Type 1 mainly represented the characteristics of floating algae. This type had little significance to in estimating Chl-a concentration because sensors could only receive signal of floating algae. Then Type 1 was usually used as the evidence of algal blooms. Meanwhile, two-band semi-analytical algorithms were established for Type 2­Type 4 waters which were separately dominated by Chl-a concentration, high suspended solid, low Chl-a and low suspended solid. Comparing with the two-band algorithms, band 7 and band 6 combination was more suitable for Type 2 and Type 3 while the correlation between Chl-a concentration and b7/b5 was higher than that between b7/b6 for Type 4. The accuracies of classification models (Type 2­Type 4) were higher than that of the overall model, with the reduced average relative errors of 7%, 12.3% and 15.9%, respectively. Moreover, the inversion results of GOCI data not only reflected the spatial distribution of Chl-a, but also showed the diurnal variation of the Chl-a concentration of Taihu Lake. This study has demonstrated great potential for dynamic monitoring of eutrophication pollution with GOCI data. In addition, the results suggested that optical classification algorithm can improve the accuracy of Chl-a concentration and the application performance of semi-analytical model. GOCI data and the class-based algorithm provide a basis for accurate estimation of diurnal and spatial variation of Chl-a concentration.

[Impact of Vegetation Structure on Drought Indices Based on MODIS Spectrum].

The drought indices based on MODIS spectral reflectance data are widely used for drought characterization and monitoring in agricultural context. Based on the PROSAIL model and MODIS observational data in Shandong in 2010, the present paper studied the impact of vegetation structure of leaf area index and physiological growth cycle on MODIS spectral drought index. The results showed that the reflectance of three MODIS bands in spectrum of near-infrared and shortwave infrared changes significantly with leaf water content of vegetation. Therefore, the five kinds of MODIS spectral drought index constructed by those MODIS bands can be used to monitor the leaf water content of vegetation. However, all drought indices are affected by leaf area index. In general, the impact is serious in the case of low LAI values and is weakened with the increase in LAI value. The study found that physiological vegetation growth cycle also affects the magnitude of MODIS spectral drought indices. In conclusion, the impact of vegetation structure must be carefully considered when using MODIS spectral drought indices to monitor drought. The conclusion of this study provides a theoretical basis for remote sensing of drought monitoring.

The Extraction Model of Paddy Rice Information Based on GF-1 Satellite WFV Images.

In the present, using the characteristics of paddy rice at different phenophase to identify it by remote sensing images is an efficient way in the information extraction. According to the remarkably properties of paddy rice different from other vegetation, which the surface of paddy fields is with a large number of water in the early stage, NDWI (normalized difference water index) which is used to extract water information can reasonably be applied in the extraction of paddy rice at the early stage of the growth. And using NDWI ratio of two phenophase can expand the difference between paddy rice and other surface features, which is an important part for the extraction of paddy rice with high accuracy. Then using the variation of NDVI (normalized differential vegetation index) in different phenophase can further enhance accuracy of paddy rice information extraction. This study finds that making full advantage of the particularity of paddy rice in different phenophase and combining two indices (NDWI and NDVI) associated with paddy rice can establish a reasonable, accurate and effective extraction model of paddy rice. This is also the main way to improve the accuracy of paddy rice extraction. The present paper takes Lai'an in Anhui Province as the research area, and rice as the research object. It constructs the extraction model of paddy rice information using NDVI and NDWI between tillering stage and heading stage. Then the model was applied to GF1-WFV remote sensing image on July 12, 2013 and August 30, 2013. And it effectively extracted out of paddy rice distribution in Lai'an and carried on the mapping. At last, the result of extraction was verified and evaluated combined with field investigation data in the study area. The result shows that using the extraction model can quickly and accurately obtain the distribution of rice information, and it has the very good universality.

[Modeling continuous scaling of NDVI based on fractal theory].

Scale effect was one of the very important scientific problems of remote sensing. The scale effect of quantitative remote sensing can be used to study retrievals' relationship between different-resolution images, and its research became an effective way to confront the challenges, such as validation of quantitative remote sensing products et al. Traditional up-scaling methods cannot describe scale changing features of retrievals on entire series of scales; meanwhile, they are faced with serious parameters correction issues because of imaging parameters' variation of different sensors, such as geometrical correction, spectral correction, etc. Utilizing single sensor image, fractal methodology was utilized to solve these problems. Taking NDVI (computed by land surface radiance) as example and based on Enhanced Thematic Mapper Plus (ETM+) image, a scheme was proposed to model continuous scaling of retrievals. Then the experimental results indicated that: (a) For NDVI, scale effect existed, and it could be described by fractal model of continuous scaling; (2) The fractal method was suitable for validation of NDVI. All of these proved that fractal was an effective methodology of studying scaling of quantitative remote sensing.

[NDVI difference rate recognition model of deciduous broad-leaved forest based on HJ-CCD remote sensing data].

The present paper takes Chuzhou in Anhui Province as the research area, and deciduous broad-leaved forest as the research object. Then it constructs the recognition model about deciduous broad-leaved forest was constructed using NDVI difference rate between leaf expansion and flowering and fruit-bearing, and the model was applied to HJ-CCD remote sensing image on April 1, 2012 and May 4, 2012. At last, the spatial distribution map of deciduous broad-leaved forest was extracted effectively, and the results of extraction were verified and evaluated. The result shows the validity of NDVI difference rate extraction method proposed in this paper and also verifies the applicability of using HJ-CCD data for vegetation classification and recognition.

[Mangrove canopy species discrimination based on spectral features of Geoeye-1 imagery].

Based on Geoeye-1 high spatial resolution images and field canopy hyperspectral reflectance of five mangrove communities located at Beibu Gulf, this paper verified the capability of Geoeye-1 imagery for mangrove canopy species identification by utilizing spectral information. The research investigated that: (1) in the spectrum range between 350 and 1 100 nm, the mangrove canopy reflectance exhibited optimum separability in six wavebands (435, 469, 523, 677, 751 and 761 nm); (2) three mangrove species could be effectively identified through the pixel-based spectrum operation, while the Bruguiera acquired the highest producer accuracy up to 93.03%; (3) species end member of Bruguiera extracted directly from the combined imagery conducted on hyperspectral hypothesis performed higher precision than the field training samples behaved. The spectrum significance of the high spatial resolution image was approved, and reference and basis were also illustrated for the further mangrove species distinguishing from the object-oriented perspective.

[Dynamic monitoring of summer maize planting information for spatial and temporal variations in Huanghuaihai plain during 2000-2010].

In order to give consideration to the change information of summer maize planting area and spatial distribution in Huanghuaihai plain, the present paper combined statistical analysis and remotely sensed classification technology to extract summer maize based on MODIS EVI images. The results showed high accuracy (> 67.35%) with the TM-derived in spatial distribution and high correlation coefficient with the agriculture statistics in planting areas at city level (R2 > 0.497 7). On this basis, change imagery-was computed using image overlying algorithm based on binaryzated images derived from classification results in Huanghuaihai plain during 2000-2010. The change detection feature was analysed according to the plates in the region. The results show that the summer maize planting area increased significantly in huanghuaihai plain from 2000 to 2010. The planting area increased steadily in southern part during study year. In the northern part, it discreased from 2000 to 2003 and increased from 2003 to 2010. The most huge change occurred in the northern part in the period of 11 years. The planting proportion increased in the north of plain but decreased in the north. The new method can be widely used in regional dynamic detection and has a good applicability and accuracy.

[Retrieval of dust fraction of atmospheric aerosols based on spectra characteristics of refractive indices obtained from remote sensing measurements].

Mineral dust is an important chemical component of aerosol, which has a significant impact on the climate and environmental changes. The spectral behavior of aerosol refractive indices at four wavelengths from 440 to 1 020 nm was analyzed based on one year observation obtained from Beijing AERONET site. The real parts of refractive index (n) in each band did not differ greatly, however the imaginary parts (k) showed a significant difference due to the absorption of mineral dust in aerosol. From 440 to 670 nm k decreased rapidly, while from 670 to 1 020 nm featured a lower, constant value. Accordingly, k(440 nm) could be considered separately with other three bands. Hence, we added mineral dust into the currently used three-component aerosol chemical model to form a new four-component model (i. e. BC, AS, dust and water) which is more suitable to represent the aerosol chemical composition. Then we presented a method to retrieve dust content in aerosols using this four-component model and refractive indices obtained from the sunphotometer measurements. Finally the dust content in aerosol was investigated under different weather conditions, i. e. clear, haze and dust in Beijing. The results showed that volume fractions of the dust component were 88%, 37% and 48% for clear, hazy and dusty day respectively, which was consistent with the coarse mode proportion in aerosols calculated from aerosol size distributions.

[Experimental study of offshore oil thickness hyperspectral inversion based on bio-optical model].

Study on the regularity of thin oil film thickness and its reflectance plays an important role in understanding the mechanism of offshore oil slick and ocean hydrocarbon resources exploration. In this work, the thin oil film thickness of biological optical model is established, and introduced the simplified model of inversion thin oil film thickness information by using one single-band or by using two-band ratio image data. With the quantitative inversion test of thin oil film thickness through the natural shallow water and the crude oil sample, the variation rules of between oil spectral parameters and the thin oil film thickness are obtained. The study show that, the oil reflectance in visible and near infrared spectrum (450-800 nm) and the thin film thickness has high inverse correlation, and showed as negative exponent form decline with the increase of oil film thickness. Regarding the shallow water environment, the double band ratio inversion model of using ETM1/ETM3 band ratio can used to be eliminate the impact of sky scattering influence, and to overcome the single-band model fault of Inversion instability when used in different water quality regions, as the inversion result of the model's correlation coefficient can reach 0.98, which is considered to be the ideal hydrocarbon content remote sensing surveying band, and combined with other types of remote sensing technology (such as ultraviolet-laser or SAR), it would provide more economic and precision services of oil total amount infromation for offshore oil exploration and oil spill monitoring.

[Analysis of directional characteristics of winter wheat canopy spectra].

The bidirectional reflectance factors vary as the incidence directions and the view angles change. At present the remote sensing is almost at nadir, therefore it is possible to improve the accuracy of remote sensing application by reasonably selecting the looking angle, solar zenith angle, and so on. Based on the multidirectional spectra of winter wheat canopy at several critical growth stages, the paper quantitatively analyzed the sensitivity of narrowband bidirectional reflectance to view planes, view zenith angle, solar zenith angle, growth stage, and band by using anisotropy factor (ANIF) and anisotropy index (ANIX). The change of NDVI with view zenith angle, solar zenith angle and growth stage was also studied. The results show that the anisotropy characteristics of bidirectional reflectance factors at solar principal plane was stronger than that at the other planes, and orthogonal principal plane was the weakest. The ANIX at solar principal plane was the biggest. The reason was that the shadow of canopy changed more dramatically at solar principal plane than at the other planes. The sensitivity of bidirectional reflectance factor at visible bands to zenith angles was stronger than in near infrared regions, the reason for which was that the shadow effect in visible regions was stronger than in near infrared regions. The ANIX in visible regions was bigger than in near infrared regions. The sensitivity of bidirectional reflectance factor to solar zenith angles increased as the view zenith angle increased. The NDVIs at every looking zenith angle all increased with the leaf area index increasing. The NDVIs at forward direction were larger than at backward direction, which resulted from that the shadow effect in visible regions was stronger than in near infrared regions. The solar principal plane implies rich internal structure information on object. In order to reduce the uncertainty from the observing method, the near infrared bands and small solar zenith angle should be chosen. The retrieve of structure parameters ought to select solar principal plane, and avoid hot spot region when inversing biological parameters using NDVI.

[Error analysis of the land surface temperature retrieval using HJ-1B thermal infrared remote sensing data].

Error analysis is playing an important role in the application of the remote sensing data and model. A theoretical analysis of error sensitivities in land surface temperature (LST) retrieval using radiance transfer model (RT) is introduced, which was applied to a new thermal infrared remote sensing data of HJ-1B satellite(IRS4). The modification of the RT model with MODTRAN 4 for IRS4 data is mentioned. Error sensitivities of the model are exhibited by analyzing the derivatives of parameters. It is shown that the greater the water vapor content and smaller the emissivity and temperature, the greater the LST retrieval error. The main error origin is from equivalent noise, uncertainty of water vapor content and emissivity, which lead to an error of 0.7, 0.6 and 0.5 K on LST in typical condition, respectively. Hence, a total error of 1 K for LST has been found. It is confirmed that the LST retrieved from HJ-1B data is incredible when application requirement is more than 1K, unless more accurate in situ measurements for atmospheric parameters and emissivity are applied.

[Wavelet analysis and its application in denoising the spectrum of hyperspectral image].

In order to remove the sawtoothed noise in the spectrum of hyperspectral remote sensing and improve the accuracy of information extraction using spectrum in the present research, the spectrum of vegetation in the USGS (United States Geological Survey) spectrum library was used to simulate the performance of wavelet denoising. These spectra were measured by a custom-modified and computer-controlled Beckman spectrometer at the USGS Denver Spectroscopy Lab. The wavelength accuracy is about 5 nm in the NIR and 2 nm in the visible. In the experiment, noise with signal to noise ratio (SNR) 30 was first added to the spectrum, and then removed by the wavelet denoising approach. For the purpose of finding the optimal parameters combinations, the SNR, mean squared error (MSE), spectral angle (SA) and integrated evaluation coefficient eta were used to evaluate the approach's denoising effects. Denoising effect is directly proportional to SNR, and inversely proportional to MSE, SA and the integrated evaluation coefficient eta. Denoising results show that the sawtoothed noise in noisy spectrum was basically eliminated, and the denoised spectrum basically coincides with the original spectrum, maintaining a good spectral characteristic of the curve. Evaluation results show that the optimal denoising can be achieved by firstly decomposing the noisy spectrum into 3-7 levels using db12, db10, sym9 and sym6 wavelets, then processing the wavelet transform coefficients by soft-threshold functions, and finally estimating the thresholds by heursure threshold selection rule and rescaling using a single estimation of level noise based on first-level coefficients. However, this approach depends on the noise level, which means that for different noise level the optimal parameters combination is also diverse.

[Spectral response analysis of offshore thin oil slicks].

Hyperspectral data of thin oil slicks have some spectral response characteristics, and an experiment of offshore thin oil slicks was designed to measure and analyze their reflectance by using ASD hyperspectral instrument. With the oil slick thickness changing, its color varied from rainbow sheen slick to fuchsine sheen slick, kelly sheen slick, aqua sheen slick, silver sheen slick and light sheen slick. The result suggested that different thin oil slicks have different characteristics, some thin oil slicks could change the reflectance at 440 nm which is the spectral absorption peak value of chlorophyll, and the best hyperspectral band to distinguish the different offshore thin oil slicks is located in the range between 350 and 440 nm. The reflectance of thin oil slicks is higher than that of seawater in the hyperspectral data range from 440 to 900 nm, but has no absorption or reflection characteristics compared to seawater's. It could not be detected in the range of near infrared from 900 to 2,500 nm of offshore thin oil slicks because of high noise, low signal and influence of the atmosphere. Finally, the spectral response theory of offshore thin oil slicks was analyzed, and the results indicated that the interference phenomenon of offshore thin oil slicks could add reflected light and change the reflectance of seawater, hence, increase the feasibility of offshore oil slick remote sensing.

[A target-highlighting method in multispectral remote sensing].

In order to highlight target in multispectral remote sensing and overcome the human error caused by threshold, a new method is proposed here. Image of target similarity is firstly calculated by spectral energy level matching (SEM) algorithm and as a band added to original image; Then, band normalization is performed on the new image to reduce the effects caused by the order of magnitude in different bands; Finally, a false color image that highlights the target is made by RGB composed of the first three bands (3, 2, 1) in MNF transformation. Results from the experiment of highlighting the main rock-type tuffaceous siltstone in Hatu area, Xinjiang province, China show that (1) the new method can highlight target for the increment of target's information and weights during the process of transformation by adding a band representing target's similarity to the original image. Therefore, it overcomes the shortcomings existing in the common transformations on space information-although different objects corresponding to special information space are distinguished, targets the authors wanted can not be highlighted yet; (2) The new method can distinguish more objects than original maximum noise fraction (MNF) transformation because it unifies the tone for the same object's type by suppressing none target information using SEM method; (3) In addition to highlighting tuffaceous siltstone in the study area, the new method can be used widely in other fields such as soil, concrete, altered mineral etc.

Feasibility of reflectance spectroscopy for the assessment of soil mercury contamination.

Conventional methods for investigating soil Hg contamination based on raster sampling and chemical analysis are time-consuming and relatively expensive. The objective of this study was to develop a rapid method for investigating Hg concentration in suburban agricultural soils of the Nanjing region using reflectance spectra within the visible-near-infrared (VNIR) region. Several spectral pretreatments (absorbance, Kubelka-Munk transformations and their derivatives) were applied to the reflectance spectra to optimize the accuracy of prediction. The prediction of Hg concentration was achieved by univariate regression and principal component regression (PCR) approaches. The optimal model (R= 0.69, RMSEP = 0.15) for predicting Hg was achieved using the PCR method with the Kubelka-Munktransformation asthe spectral predictor. Comparison of three wavelength ranges (0.38-1.1, 1.0-2.5, and 0.38-2.5 microm) on the effect of prediction accuracy showed that the best results were acquired using the 1.0-2.5 microm spectral region. Correlation analysis revealed that Hg concentration was negatively correlated with soil reflectance while positively correlated with the absorption depths of goethite at 0.496 microm and clay minerals at 2.21 microm, suggesting that Hg-sorption by clay-size mineral assemblages in soils was the mechanism by which to predict spectrally featureless Hg. These results indicate that it is feasible to predict Hg levels in agricultural soils using the rapid and cost-effective reflectance spectroscopy. Future study with operational remote sensing techniques and field measurements is strongly recommended.