This PDF file contains the front matter associated with SPIE Proceedings Volume 9975 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

The Stratospheric Observatory For Infrared Astronomy (SOFIA) is a 2.5-m telescope mounted inside of a Boeing 747SP. Planning and executing astronomical observations from an aircraft moving at 500 miles per hour has its own unique challenges and advantages. Scheduling and optimizing an entire year of science observations is a balancing act with target availability, instrument availability, and operational constraints. A SOFIA flight is well choreographed, and successfully executing observations on SOFIA requires many systems and people to work together- from the telescope assembly compensating for the continual vibration and movement of the plane in order to accurately point the telescope, the expertise of the telescope operators to prepare the telescope for use by the instrument operators, aircraft operations ensuring that the aircraft is ready for flight, and the mission systems control computers keeping track of all the data. In this paper we will discuss what it takes to plan a SOFIA flight, and what we do once we’re in the air. We will share a typical science flight, as well as more challenging and unique observations that require SOFIA being in the right place at the right time.

Remote sensing techniques such as change detection are widely used for mapping and monitoring forest cover to detect the declining health and vigor of forests. These techniques rely on the assumption that the biophysical variation in the forest introduces a corresponding variation in its reflectance. The biophysical variations are assessed by foresters, but these assessment techniques are expensive and cannot be performed frequently to identify a specific level of change in the forest, for example, infection due to gypsy moths that results in forest defoliation. Further, the interaction of atmosphere, sensor characteristics, and phenology that are inherent in the remotely sensed images makes it difficult to separate biophysical changes from observational effects. We have addressed these limitations by developing a method to model the spectral reflectance properties of forests with varying degrees of defoliation using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. This paper discusses the in-canopy radiative approach and the impact of defoliation on the reflectance and radiance observed by sensors such as Landsat. The results indicate that the relative variation in forest reflectance between a non-defoliated and a 30% defoliated deciduous forest can be as high as 10% in the NIR spectral band. A function can be fit to predict the level of defoliation from the relative variation in radiance. The modeling and analysis techniques can be extended to assess the impact of atmospheric factors and sensor characteristics relative to the biophysical changes as well as for assessing other biophysical variables in forests.

kHz optical remote sensing system is implemented to determine melanization and backscatter cross-section in the near infrared (NIR) and shortwave infrared (SWIR) in situ. It is shown that backscatter cross-section in the SWIR is insensitive to melanization and absolute melanization can be derived from the ratio of backscatter cross-section in two bands (SWIR/NIR). We have shown that insects reflect more strongly in the SWIR as compared to NIR and Visible (VIS) in accordance with previous findings. This is illustrated using three different insects (Snow white moth (spilosoma genus), Fox moth (Macrothylacia) and Leather beetle (Odontotaenius genus)) and it is shown that the reflectance of the Leather beetle in the VIS and NIR is more affected by melanization as compared with snow white moth.

Changing the land cover/ land use has serious environmental impacts affecting the ecosystem in Malaysia. The impact of land cover changes on the environmental functions such as surface water, loss water, and soil moisture is considered in this paper on the Kelantan river basin. The study area at the east coast of the peninsular Malaysia has suffered significant land cover changes in the recent years. The current research tried to assess the impact of land cover changes in the study area focused on the surface water, loss water, and soil moisture from different land use classes and the potential impact of land cover changes on the ecosystem of Kelantan river basin. To simulate the impact of land cover changes on the environmental hydrology characteristics, a deterministic regional modeling were employed in this study based on five approaches, i.e. (1) Land cover classification based on Landsat images; (2) assessment of land cover changes during last three decades; (3) Calculation the rate of water Loss/ Infiltration; (4) Assessment of hydrological and mechanical effects of the land cover changes on the surface water; and (5) evaluation the impact of land cover changes on the ecosystem of the study area. Assessment of land cover impact on the environmental hydrology was computed with the improved transient rainfall infiltration and grid based regional model (Improved-TRIGRS) based on the transient infiltration, and subsequently changes in the surface water, due to precipitation events. The results showed the direct increased in surface water from development area, agricultural area, and grassland regions compared with surface water from other land covered areas in the study area. The urban areas or lower planting density areas tend to increase for surface water during the monsoon seasons, whereas the inter flow from forested and secondary jungle areas contributes to the normal surface water.

Potato is one of the staple foods and cash crops in Bangladesh. It is widely cultivated in all of the districts and ranks second after rice in production. Bangladesh is the fourth largest potato producer in Asia and is among the world’s top 15 potato producing countries. The weather condition for potato cultivation is favorable during the sowing, growing and harvesting period. It is a winter crop and is cultivated during the period of November to March. Bangladesh is mainly an agricultural based country with respect to agriculture’s contribution to GDP, employment and consumption. Potato is a prominent crop in consideration of production, its internal demand and economic value. Bangladesh has a big economic activities related to potato cultivation and marketing, especially the economic relations among farmers, traders, stockers and cold storage owners. Potato yield prediction before harvest is an important issue for the Government and the stakeholders in managing and controlling the potato market. Advanced very high resolution radiometer (AVHRR) based satellite data product vegetation health indices VCI (vegetation condition index) and TCI (temperature condition index) are used as predictors for early prediction. Artificial neural network (ANN) is used to develop a prediction model. The simulated result from this model is encouraging and the error of prediction is less than 10%.

Different concentrations of charcoal and carbon nanotubes were incorporated in different mix types of soil samples, these were previously chemically characterized, and physically grain standardized, then the water potential was measured by traditional procedures, which need to consider the water composition and the soil salinity to achieve an accurate measurement, and by infrared thermal images where the water potential was correlated with the superficial emissivity. It was observed that the organic incorporation increases the water potential but it depends of soil gradation, a biggest increment of the water potential was observed in a poorly graded soil than that observed in a well graded soil; the nanotubes in low concentrations do not present considerable changes in the water potential, and in high concentrations the cost is not profitable. It was analyzed the minimum concentration changes of charcoal and nanotubes in the soil that can be measured with thermal emissivity, and the deepness at which the infrared thermal images can measure, also it was studied the rate of water drain in the different soils, and the ability of follow this with thermal sequence of images.

Ten years (2005 - 2014) of surface ultraviolet (UV) observations from TOMS-OMI and UVMRP are combined across the continental U.S. via data fusion technique. The combined UV data inherits advantages from both data sources, satellite and ground observations. This research analyzes the combined data both in time and in space, presenting the preliminary statistical results for this ten-year period.

The remotely sensed reflectance spectra of vegetated surfaces contain information relating to the leaf area index (LAI) and the chlorophyll-a and -b concentrations (Cab) in a leaf. Difficulties associated with the retrieval of these two biophysical parameters from a single reflectance spectrum arise mainly from the choice of a suitable set of observation wavelengths and the development of a retrieval algorithm. Efforts have been applied toward the development of new algorithms, such as the numerical inversion of radiative transfer models, in addition to the development of simple approaches based on the spectral vegetation indices. This study explored a different approach: An equation describing band-to-band relationships (vegetation isoline equation) was used to retrieve the LAI and C_ab simultaneously from a reflectance spectrum. The algorithm used three bands, including the red edge region, and an optimization cost function was constructed from two vegetation isoline equations in the red-NIR and red edge-NIR reflectance subspaces. A series of numerical experiments was conducted using the PROSPECT model to explore the numerical challenges associated with the use of the vegetation isoline equation during the parameter retrieval of the LAI and C_ab. Overall, our results indicated the existence of a global minimum (and no local minima) over a wide swath of the LAI-C_ab parameter subspace in most simulation cases. These results suggested that the use of the vegetation isoline equation in the simultaneous retrieval of the LAI and the C_ab provides a viable alternative to the spectral vegetation index algorithms and the direct inversion of the canopy radiative transfer models.

Information on the relationships between pairs of wavelength bands is useful when analyzing multispectral sensor data. The soil isoline is one such relationship that is obtained under a constant soil spectrum. However, numerical determination of the soil isoline in the red and NIR reflectance subspace is problematic because of singularities encountered during polynomial fitting. In our previous work, this difficulty was effectively overcome by rotating the original red-NIR subspace by an angle identical to a soil line slope. In the context of hyperspectral data analysis, the applicability of this approach should be investigated thoroughly for band combinations other than red-NIR. The objective of the present study was to expand the applicable range of band combinations to 400–2500 nm by conducting a set of numerical simulations of radiative transfer. Soil isolines were determined numerically by varying soil reflectance and biophysical parameters. The results demonstrated that, as shown previously for the red-NIR band combination, singularities can be avoided for most band combinations through use of the rotation approach. However, for some combinations, especially those involving the shortwave infrared range, the rotation approach gave rise to a further numerical singularity. The present findings thus indicate that special caution should be exercised in the numerical determination of soil isoline equations when one of the chosen bands is in the shortwave infrared region.

The difficulty of in-situ calibration on the 940 nm channel of Multi-Filter Rotating Shadowband Radiometer (MFRSR) stems from the distinctive non-linear relationship between the amount of precipitable water vapor (PW) and its optical depth (i.e. curve of growth) compared to the counterpart of aerosols. Previous approaches, the modified Langley methods (MLM), require exact aerosol optical depth (AOD) values and a constant PW value at all points participating the regression. Instead, we propose a new method that substitutes the PW optical depth derived from collocated GPS zenith wet delay retrieval in conjunction with meteorology data and requires a constant AOD value at all points participating the regression. The main benefits of the new method include: (1) Aerosol stability is easier to fulfill than PW stability; (2) AOD stability could be inferred from adjacent channels (e.g. 672 and 870 nm) of MFRSR itself without measurements of a collocated AERONET sun photometer; and (3) When applicable, the time interval of GPS derived PW (i.e. 3 minutes) is more compatible with the MFRSR sampling interval (i.e. 3 minutes) than AERONET interpolated AOD (i.e. 15 minutes). Both MLM and the new method were applied to the MFRSR of USDA UV-B Monitoring and Research Program at the station in Billings, Oklahoma (active for 18 years so far) on July 28, 2015. The performances of the two methods are compared in order to assess their accuracy and the advantages and disadvantages.

This paper is directed to the widening of cumulative experience in the development of effective and reliable information modeling technologies for the equipping of microwave and optical monitoring of hydrological systems having different spatial scales. The problem urgency is caused by the necessity of perfecting the existing information technologies including Geospatial Information Systems (GIS) in the direction of broadening their functions and optimization of instrumental tools for hydrological monitoring with the orientation on the detection of extreme hydrological processes. GIMS - technology develops GIS technology towards the realization of the formula: GIMS = GIS + model. In other words, the functions of GIS enlivened by the introduction of a new grid-time scale. The result is a forecasting tool and, therefore, may carry out a dynamic integration of environmental information.

This study calculated the land water storage using the time-varying monthly gravity data from the GRACE (Gravity Recovery and Climate Experience) gravity satellite combined with Gaussian smoothing filter. The characteristics of spatiotemporal variations of long-term regional land water storage derived from the linear fitting method were then examined from January 2003 to December 2013. The results showed that the water storage over the huang-huai-hai plain showed an overall declining trend from 2003 to 2013 and the average declining rate is about 2.86 mm/a. The comparison between the GEACE calculation results with the soil moisture content products from the global land data assimilation system (GLDAS) showed that they are very highly consistent. The variations of regional mean soil moisture over the huang-huai-hai plain also exhibited a downward trend from 2003 to 2013 with an average declining rate about 0.74 mm/a. Based on water balance equation, we obtained the change of average groundwater storage and it showed a decreasing variability with a general declining trend with an average rate about 2.22 mm/a. In addition, the retrieved groundwater data was proven to be accurate compared to observations from groundwater wells measurement with high consistency and correlations. . Further investigations focused on analyzing the impacts of precipitation factors on groundwater variations, implying that the human influences are the main reasons for the decline in groundwater.

The aim of this work was to identify the coastal wetland plants between Bayes and BP neural network using hyperspectral data in order to optimize the classification method. For this purpose, we chose two dominant plants (invasive S. alterniflora and native P. australis) in the Yangtze Estuary, the leaf spectral reflectance of P. australis and S. alterniflora were measured by ASD field spectral machine. We tested the Bayes method and BP neural network for the identification of these two species. Results showed that three different bands (i.e., 555 nm，711 nm and 920 nm) could be identified as the sensitive bands for the input parameters for the two methods. Bayes method and BP neural network prediction model both performed well (Bayes prediction for 88.57% accuracy, BP neural network model prediction for about 80% accuracy), but Bayes theorem method could give higher accuracy and stability.

A tidal flat, the important potential land resource, is the sensitive area of intersection between the sea and the land. With Chinese HJ-1A/B remote sensing images of 2014 as data sources, based on the definition of a tidal flat, using DSAS software and Jenks Natural Breaks classification method synthetically, a more reasonable and accurate method of extracting tidal flat was imposed. In addition, the Bohai Rim was taken as an example to carry out investigation on the current situation of tidal flat. This paper can provide basic date and scientific evidence for rational utilization and sustainable development of tidal flat.

Snow parameters are important physical quantities of climatology and hydrology research, improving the accuracy of snow parameters is important for climatology, hydrology and disaster prevention and reduction. The western Jilin Province of China has obvious salinization problem. Meanwhile, it belongs to a typical snow-covered area. In this paper, the western Jilin Province is selected as the study area and the main research focuses on analyzing the snow cover conditions. The FY3B-MWRI passive microwave remote sensing data from year 2011 to 2016 are selected as experimental data. Compared with optical remote sensing data, using MWRI data can better obtain snow information, and it is also the preliminary work to retrieve snow depth and snow water equivalent. Furthermore, a new decision tree algorithm for snow cover identification was built to distinguish different snow cover conditions. Compared with the existing three algorithms reported in other literatures, the proposed algorithm improves the identification accuracy of snow cover up to 95.06%. While the accuracy for Singh’s algorithm, Pan’s algorithm and Li’s algorithm were about 80.19%, 78.79% and 90.13%, respectively. This study provides important information to the research of snow cover in saline-alkali land.

In this paper, the green tide (Large green algae-Ulva prolifera) in the Yellow Sea in 2015 is monitored which is based on remote sensing and geographic information system technology, using GF-1 WFV data, combined with the virtual baseline floating algae height index (VB-FAH) and manual assisted interpretation method. The results show that GF-1 data with high spatial resolution can accurately monitoring the Yellow Sea Ulva prolifera disaster, the Ulva prolifera was first discovered in the eastern waters of Yancheng in May 12th, afterwards drifted from the south to the north and affected the neighboring waters of Shandong Peninsula. In early July, the Ulva prolifera began to enter into a recession, the coverage area began to decrease, by the end of August 6th, the Ulva prolifera all died.

Reclamation caused a significant dynamic change in the coastal zone, the tidal flat zone is an unstable reserve land resource, it has important significance for its research. In order to realize the efficient extraction of the tidal flat area information, this paper takes Rudong County in Jiangsu Province as the research area, using the HJ1A/1B images as the data source, on the basis of previous research experience and literature review, the paper chooses the method of object-oriented classification as a semi-automatic extraction method to generate waterlines. Then waterlines are analyzed by DSAS software to obtain tide points, automatic extraction of outer boundary points are followed under the use of Python to determine the extent of tidal flats in 2014 of Rudong County, the extraction area was 55182hm², the confusion matrix is used to verify the accuracy and the result shows that the kappa coefficient is 0.945. The method could improve deficiencies of previous studies and its available free nature on the Internet makes a generalization.

The response of vegetation phenology change to climate change effects in the Northern China has been reported in the past several decades. Phenological change is a critical understanding in terrestrial carbon cycling. This study aims to investigate linear and nonlinear change trends and nonlinear response change trends in climate in vegetation phenology over Northern China in the last three decades. We analyzed the vegetation phenology over the Northern China by the new released Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVIg) dataset from 1982 to 2006.Results show that based on linear method, we can found that SOS was gradually advanced, EOS gradually delayed, and then LOS gradually lengthened. But on the basis of nonlinear method, phenological trends in the SOS, EOS and LOS are not continuous, we can found extended LOS with advanced SOS and delayed EOS before the turning point (TP) of spring SOS and autumn EOS trends and shortened LOS with delayed SOS and advanced EOS after the turning point (TP) of spring SOS and autumn EOS trends.

This paper builds a green tide disaster monitoring system based on remote sensing monitoring platform, UAV (Unmanned aerial vehicle) monitoring platform and ship monitoring platform. The system aims at multi-faceted monitoring green tide disasters with remote sensing data, UAV data and ship monitoring data. With this system, the author has continuously monitored the green tide outbreak of Chinese Yellow Sea in 2016. Research conclusions were achieved as follows. The system can quickly get spatial distribution information of green tide disaster, obtain high-resolution remote sensing data and field verification data of key monitoring areas; It can cover the shortage of a single data source by green tide monitoring, significantly improve time resolution and spatial resolution of green tide monitoring data, thus providing data support for dynamic monitoring of green tide; The system can provide data support for the prevention and control of green tide in three different scales.

In recent years, MODIS data were widely used in dynamic monitoring of Green Tide. However, the images may contain lots of mixed pixels because of coarse resolution ,which will cause the error of the monitor result1,2. In this paper, the monitoring error was quantitatively analyzed with the help of GF-1 WFV data, which has a high resolution of 16 merers and the monitoring result of which were considered to be accurate. The conclusions are as follows: there are errors in both dense and sparse Enteromorpha monitoring using MODIS data, and the error in sparse Enteromorpha is larger. Most of the error is concentrated on the edge of the floating Enteromorpha patch. MODIS has a good ability in observing the location of Enteromorpha , and it can play an important role in the dynamic monitoring of multi source data.

The results of studies of effect of heavy metals (HM) on small mammals’ bone tissue using Raman spectroscopy method are presented. The characteristics of Raman spectra of small mammals’ bone tissue, living in conditions of content HM gradient, were obtained. The main changes were detected at 960 cm^-1 and 1247 cm^-1 wavenumbers, corresponding to phosphate and amide III. Optical criteria determining the impact of HM on bone tissue were introduced.

Marine environment protection is an important support for sustainable development of marine ranching. Based on the geographic information system(GIS) and remote sensing(RS), this study developed a 3S system, which integrate Sea surface temperature, chlorophyll concentration, turbidity of sea water and other factors into system. And these factors are important components of marine environment. The system provided data service including loading, browsing, information inquiry, cartography, and also supported the analysis of remote sensing image. In the implementation of the system, the key points of the related technologies have been paid much attention. The practical application shows that it can provide assistance for the environmental protection of marine ranching.

The Wide Field View (WFV), a space borne multi-spectral sensor onboard the Chinese GaoFen-1 (GF-1) satellite from the China High-resolution Earth Observation System, is operating in orbit dedicating to providing Earth observation with decametric spatial resolution, high temporal resolution and wide coverage for environment monitoring purpose. The objective of this study is to present an integrated image processing and environment monitoring platform specifically for GF-1 WFV data. The platform is developed with a multi-layer architecture and C/S structure, which primarily consists of image pre-processing, environment monitoring, data visualization, and results output modules. The client application was created by using C# whereas IDL was used to develop image processing and other relevant algorithms. This paper focuses mainly on the overall design of the platform and related key techniques. The platform has been implemented as a stand-alone application, and successfully implemented in real world environment monitoring studies.

The results of studies of influence of oil on vegetative biological objects using Raman spectroscopy method are presented. The characteristics of Raman spectra of plants growing under the influence of oil fractions were obtained. The main changes were detected at 605 cm^-1 , 840 cm^-1 , 2120 cm^-1 wavenumbers, which is associated with increasing concentration of bromine, aromatic carbons and methane in plant leaves.

A typical heavy rainfall event occurred in Shanghai on September 13, 2009 was simulated using the Weather Research and Forecasting Model (WRF) to study the impact of microphysics parameterization on heavy precipitation simulations. Sensitivity experiments were conducted using the cumulus parameterization scheme of Betts-Miller-Janjic (BMJ), but with three different microphysics schemes (Lin et al, WRF Single-Moment 5-class scheme (WSM5) and WRF Single-Moment 6-class scheme (WSM6)) under three-way nested domains with horizontal resolutions of 36km, 12km and 4km. The results showed that all three microphysics schemes are able to capture the general pattern of this heavy rainfall event, but differ in simulating the location, center and intensity of precipitation. Specifically, the Lin scheme overestimated the rainfall intensity and simulated the rainfall location drifting northeastwards. However, the WSM5 scheme better simulated the rainfall location but stronger intensity than the observation, while the WSM6 scheme better produced the rainfall intensity, but with an unrealistic rainfall area.

As Ozone Monitoring Instrument (OMI) onboard the Aura satellite has provided global scale ozone measurements on a daily basis since 2004, the long-term stability and consistency of ozone retrievals is thus of critical importance, especially for the ozone recovery assessment. This study aims to evaluate the long-term stability of total ozone derived from the OMI Total Ozone Mapping Spectrometer (OMI-TOMS) algorithm, by comparing with collocated ground-based total ozone measurements recorded from 42Dobson spectrophotometers during the period 2004-2015. It is indicative that the OMI-TOMS total ozone is in good agreement with collocated ground-based measurements, with a R² of 0.96 and root mean square error (RMSE) of 3.3%. Further investigations show that the OMI-TOMS total ozone is of quality, as no significant latitude dependence is observed. In the past 12 years, the OMI-TOMS total ozone is highly consistent with the ground-based Dobson total ozone, with a variation of mean relative difference less than 1%. In general, the OMI-TOMS total ozone performs well and can be used with confidence.

Phragmites australis is a native dominant specie in the Yangtze Estuary, which plays a key role in the structure and function of wetland ecosystem. One key question is how to estimate the Chlorophyll content quickly and effectively at large scales, which could be used to reflect the growth condition and calculate the vegetation productivity. The aim of this work was to estimate Chlorophyll content of P. australis based on the PROSPECT and DART (Discrete Anisotropic Radiative Transfer) model. A total of 6 widely used Vegetation indices (VIs) were chosen (i.e., Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI), Colouration Index (COI), Simple Ratio Index (SR), Cater Index (CAI), and Red-edge Position Linear Interpolation (REP_Li)) and calculated, and then the relationship between VIs and Cab were analyzed. Results showed that COI and SIPI were sensitive to the leaf chlorophyll content as the chlorophyll content changes at the leaf scale. Meanwhile, no obvious saturation phenomenon was observed for these two indices compared to other indices.