Ground object spectral libraries play a significant role in remote sensing information extraction. This paper investigates the domestic and foreign spectral libraries frequently-used, including the general spectral libraries and the professional spectral libraries. Based on the biliometric analysis of the literatures about remote sensing applications based on spectral libraries, four kinds of methods are summarized, including spectral feature analysis, spectral matching, spectral mixture analysis and quantitative remote sensing modeling. Some remote sensing applications based on spectral libraries, such as ground object classification, target identification and land surface parameters inversion, are also summarized. From the background of remote sensing big data, the developing trends and application potential of the ground object spectral library are prospected at the end.
We have chances and challenges in the progress of carrying out 2030 Sustainable Development Goals in China and constructing Beautiful China. Systematically evaluating the construction of Beautiful China and finding the key problems are the important orientation. Under the background of participating in the 2030 agenda and constructing Beautiful China, also considering economic, social and environmental development, evaluation index system was built in the paper. Typical areas include Songhua River Basin, Heihe River Basin, Middle-Lower Yangtze Plain and Beijing-Tianjin-Hebei metropolitan area. Index system consists of Blue Sky, Green Land, Clear Water and Harmony People. It is a meaningful work to evaluating typical areas on Beautiful China using big earth data and remote sensing data. Research results will give references for constructing Beautiful China and regional sustainable development.
Lake water quality is directly related to the survival and development of human beings and society. Most of the existing assessment systems are based on statistical data and in-situ measurement data. Due to the long cycle and poor timeliness, these assessment systems are hard to achieve large-scale and continuous assessment of lake water environment. The development of remote sensing technology has made it possible to evaluate the quality of lake water environment with high spatial and temporal resolution. Thus, after summarizing the existing lake water environment quality assessment system, a new assessment system called “Beautiful Lakes” comprehensive assessment system was developed. A novel index system based on Big Earth Data (such as statistical data, field measured data and satellite remote sensing data) was first developed and integrates human activities, water quality, biology and hydrology indexes. Then, the threshold of each index was determined and the Percentage Compliance of Water Quality Index (cwq) was calculated. Following UN water, the threshold 80% of cwq was used to classify the water quality, which means if a certain water body is with cwq greater than 80%, the water quality is “good”; otherwise, the water quality is poor. Finally, the Percentage of Water Bodies of Good Quality (WBGQ) was calculated to attain the comprehensive assessment of water quality on a large scale (basin scale or national scale). The new assessment system will promote the comprehensive assessment of lake water environment quality in China under the framework of the UN Sustainable Development Goals and provide a technical reference for the evaluation of beautiful China.
At present, the layout of rail transportation is an important driving force for optimizing the spatial structure of cities and regions and will also accelerate urban space renewal. This paper uses inter-field interpretation of multi-source data information, combining the spatial judgment scheme, Based on the comprehensive perspective of “three-dimensional integration” scale characteristics, functional attributes and agglomeration patterns of urban functional space, finely depicted urban spatial succession trajectory and update model of the rail transportation station. The study found that: (1) urban space scale showed significant reduction in urban central area, high-density residential, commercial service site;(2) urban functional succession tends to dominate the update mode, especially the high-efficiency catering and entertainment, business service format is the main function;(3) urban function of the rail transit station space is agglomerated. Taking this as a reference, it is proposed that the urban spatial renewal of China's rail transit stations should be based on responding to the development requirements of the times and stimulating the spatial vibrancy of the station; corresponding to the station environment, rational allocation of functional elements; careful considerate functional agglomeration characteristics, optimize station space structure and update spatial succession framework strategy, in order to provide important inspiration and policy recommendations for China's successive access to the station space update and planning regulation of different rail transportation development stages.
Accurate maps of land cover at high spatial resolution are fundamental to many researchs on carbon cycle, climate change monitoring and soil degradation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. It offer opportunities for generating land cover maps designed to meet the increasingly detailed information needs for science,monitoring, and reporting.In this study, we classified the land cover types in Shanxi using Landsat time series data based on the Google Earth Engine Platform. We selected 1 580 sample points be visual interpretation of the original fine spatial resolution images along with Google Earth historical images over six different cover types. We defined training data by randomly sampling 60% of the sample points. The remaining 40% was used for validation. We generated two diffirent types of Landsat composite: (1) one based on median values which is used as the input image for single-date classification; (2)one based on percentile values which is used as input images for time series classification. Random forest classification was performed with two different types of Landsat composites. Random forest classification was performed with two different types of Landsat composites.We visually compared the single-date based to the time series based cover maps of 1990, 2000, 2010 and 2017 in five local areas, and we future compared the results of time series to other products. We aslo performed an accuracy assessment on the land cover classification products. The results shown: (1) The results of time series classification had an overall accuracy of 84%~94%. The time series results improved overall accuracy by 5%~10% compared to single-date results; (2) The result of time series achieves the classification accuracy of products such as CNLUCC, GlobeLand30 and FROM-GLC.The following conclusions were drawn: (1) Cloud computing and archived Landsat data in the GEE has many advantages for land cover classification at a large geographic scale, such as s strong timeliness, short time cycle and low cost; (2) The statistics metrics from Landsat time series is a viable means for discrimination of land cover types, which is particularly useful for the time series classification.
The spatiotemporal variation of vegetation coverage is one of the main research fields in Global and Regional eco-environment. Based on the Google Earth Engine cloud platform, using the MODIS-EVI long-term series data of 250 m resolution from 2000 to 2017. The model of dimidiate pixel was applied in estimating the spatiotemporal variations of Fractional vegetation coverage in China since 2000. The spatiotemporal variation characteristics of China's vegetation coverage for nearly 18 years and future trends from the provincial scale also be analyzed. Trend analysis, Detrended Standard Deviation and Hurst index were employed. The results showed that: (1) The rate of variation of vegetation coverage in China since 2000 is 0.09%/a (P<0.01), the average vegetation coverage is 44.63%. The overall spatial distribution pattern shows the characteristics of “south-high and low-lying northwest”, but there is space Heterogeneity; (2) Hainan Province has the highest average vegetation coverage (79%), the lowest in Xinjiang Uygur Autonomous Region (13%), the most significant improvement in vegetation coverage in Shanxi Province (0.4%/a). Tianjin has the largest inter-annual volatility (DSD=0.039), Xinjiang, Tibet and Qinhai province which located in the westernmost of China have the least annual fluctuations in vegetation coverage; (3) The Hurst Index of Vegetation Coverage at National Scale is 0.72, China Future vegetation coverage will continue to improve. The provinces with improved sustainability are basically “T”-type distribution, and the provinces on both sides of the east and west should focus on strengthening the ecological restoration and protection of vegetation to guarantee the sustainability of regional ecological civilization construction.
Characteristics of vegetation variation play an important role in ecological monitoring and provide the basis for integrated river basin management decisions. In this study, the spatial-temporal trends in vegetation cover change and its sustainability in Heihe river basin during 2001~2017 were characterized, using MODIS-EVI time series data at a spatial resolution of 250 meters in Google Earth Engine(GEE) platform. Combined with temperature, precipitation and river runoff data, the factors affecting vegetation growth in Heihe River Basin were identified. The results show that: Over the last 17 years, the average annual increment of EVI in Heihe river basin was 0.003 9, and the annual expansion of vegetation area was 480.3 km2. Vegetation in the upper, middle and lower reaches of Heihe river has changed in varying degrees affected by temperature, precipitation, reclamation of cultivated land, water resources management and related groundwater. Whether the annual maximum EVI value or vegetation area, the increase trend of vegetation in the middle reaches was the most significant, and the oasis area was more obvious than the non-oasis area. This trend is sustainable in the short term, but there is a greater risk for a long time scale. The study provides a demonstration for high-speed monitoring of vegetation changes, reflecting the equal importance of growth and type changes for monitoring vegetation in arid regions. The regional synergy of vegetation changes in river basin puts forward higher requirements for integrated river basin management, such as reasonable water separation and strengthening surface-groundwater collaborative management.
The Tiangong-2 Space Laboratory(TG-2), equipped with multiple remote sensing application payloads, was launched in September 2016. The multi-angle polarization imager has 12 observation channels (565nm to 910 nm), which is the first multi-angle polarization instrument used in space exploration in China. The data quality of the instrument is crucial for the precise inversion of the optical and microphysical characteristic parameters of aerosol and cloud, and accurate image geolocation is the basis for the quantitative application of observational data. This paper is based on the multi-angle polarization design parameters of optical geometry, spacecraft attitude information, and the relation model between the image observation pixel and the ground space location is established by using parametric approaches. Firstly, the impact of both the earth's rotation and spacecraft attitude deviation on image motion is analyzed, and the preliminary correction of the corresponding drift angle and the mounting matrix is carried out, Then, the coastline inflection point method is used to detect the error of the initial correction result in the direction along-track and cross-track, and the focus correction method is used to directly compensate the geolocation error in two directions in the focal plane coordinate system. The final geolocation errors assessment shows that the mean error in both along and cross-track directions is within one pixel.
Hydrologic prediction is an important prerequisite for optimal allocation of water resources, but the traditional forecasting methods generally have the problem of low forecasting accuracy. To improve the accuracy of hydrologic prediction, a hybrid data-driven model is proposed for monthly runoff forecasting, namely, Singular Spectrum Analysis-Grey Wolf Optimizer-Support Vector Regression (SSA-GWO-SVR) model. The proposed model uses SSA to denoise the runoff data to improve the stability and predictability of runoff series, and uses GWO to optimize the parameters of SVR model to enhance the generalization ability of the model. This model is validated by monthly runoff prediction of Zhengyixia in the Heihe River Basin, and the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (R) and Nash-Sutcliffe Efficiency Coefficien (NSEC) are used as evaluation criteria. The experimental results show that the prediction accuracy of the proposed model is significantly higher than those of Autoregressive Integrated Moving Average model (ARIMA), Persistent Model (PM), Cross Validation(CV)-SVR and GWO-SVR models, and the can predict the runoff peak well, which indicates that the model is a reliable runoff forecasting model, can capture the intrinsic characteristics of hydrologic runoff more deeply, and provides a new method for hydrologic prediction based on data-driven model.
Sea surface salinity remote sensing is one of the important contents of the remote sensing research of the ocean. For the influence of the sea surface salinity remote sensing caused by the atmospheric, according to the theory of atmospheric radiation transfer, the atmospheric radiation effects were simulated and corrected, and then the sea surface salinity was inverted by the neural network model. The result showed that the atmospheric radiation effect was serious, and it needed to be corrected. When the precision of atmospheric temperature and pressure of the earth surface was 2 ℃ and 10 hPa, the atmospheric influence could be removed. The difference in the number of the training sample sets would have a certain impact on the accuracy of neural network inversion. The salinity retrieval relative error of the SMAP satellite was small, and the residual error was basically concentrated within 0.6, but the error was larger in the region where the salinity value is lower than 34.4‰.
Medium-to-high resolution aerosol information is of great significance for surface reflectance inversion and urban ambient air quality monitoring. However, the high-precision aerosol optical thickness (AOD) retrieval in bright areas, such as cities and sparse vegetation areas, has long plagued the quantitative remote sensing applications. Taking Beijing urban area and Baotou desert area as examples, using MODIS surface reflectance products to construct prior knowledge constraints, the AOD inversion of 13 scenes Sentinel-2 images in bright areas was realized based on the deep blue algorithm. To verify the accuracy of the algorithm, the result were compared with the Sentinel-2 official algorithm processing result, the Landsat-8 official aerosol products and the ground-measured AOD data from the Global Aerosol Automated Observing Network (AERONET). The results indicate that the retrieved AOD values from deep blue algorithm is significantly correlated with the measured value of AERONET(R2 > 0.90, RMSE = 0.056 0), and the AOD spatial distributions are also well consistent with those from Landsat-8, which reflects the characteristics of human activities. But, whether in desert bright area or urban bright area with less vegetation, the AOD values retrieved by Sen2Cor plug-in are fixed, no spatial distribution and do not conform to the actual situation. In general, compared with the current official products, the deep blue algorithm is suitable for aerosol retrieval in high-brightness areas of Sentinel-2 data，and has obvious advantages in terms of estimation accuracy and spatial distribution trend.
Gaofen-5(GF-5) satellite was successfully launched on May 29, 2018. The Visual and Infrared Multispectral Imager(VIMI) developed independently by China is a multi-spectral imager in the range of visible band to long-wave infrared band, which has broad application prospects. The quality assessment of satellite image is not only the verification of whether the remote sensing satellite meets the design criteria, but also the reference for image processing and application. In this paper, the quality assessment for VIMI is provided, which provides reference for the processing and application of the image. Four indicators, named the Signal-to-Noise Ratio (SNR), clarity, information content and radiation heterogeneity, were used for quality assessment, and were compared with Landsat 8 images. The results show that the SNR of the shortwave infrared band (320.44~388.42) is slightly higher than that of the visible near-infrared band(208.24~238.03). The clarity (0.82~0.91) in the near-shortwave infrared band is higher than that in the other bands, especially in the long-wave infrared band (0.01~0.21). The information content of short-wave infrared band (9.01~9.97) is higher than that of medium-long-wave infrared band (5.71~8.31). The radiation heterogeneity of all 12 bands is less than 2%. The results of comparison with Landsat 8 show that ①the clarity of B1~B4 is better than Landsat 8 and this of other bands are close to Landsat 8,②for information content, B1~B10 of VIMI is close to Landsat 8, while this of B11 and B12 is less than Landsat 8 with 5.23 for B11 and 5.61 for B12,(3)for SNR, GF-5 VIMI still needs to be further improved, with 280.41、226.84 and 151.92 less than Landsat 8 for B1,B2 and B6.
Based on the 4 km resolution full-disc data of China's new generation of geostationary meteorological satellite FY-4A/AGRI, using its high temporal resolution and high spectral resolution, a multi-temporal multi-channel threshold combination cloud detection in the Qinghai-Tibet Plateau is proposed. Compared with the China National Meteorological Center cloud detection products and the traditional single-phase cloud detection method, the multi-time phase cloud detection method has an accuracy rate of 94.4%, a false detection rate of 7.2%, and a missed detection rate of 5.6%; In the cloud phase detection, the GPM precipitation and the CALIPSO satellite cloud phase observation were used to evaluate the accuracy of the detection results. The similarity between cloud phase detection result and GPM data reaches 0.883. The result of the cloud phase and the actual CALIPSO observation is also close. The rationality of cloud phase detection is verified, and it is also an auxiliary means for precipitation monitoring in the Qinghai-Tibet Plateau.
Based on DEM of Qinling Mountains, used the model of the center cell and the adjacent eight cells, we calculated the surface area of Shaanxi section Qinling Mountains. The results shows that: (1) The surface area of Shaanxi section Qinling Mountains is 75 224.67 km2, which is an increase of 22.04% from the vertical projection area；（2）The relation between the difference of surface area and vertical projection area and elevation is parabolic. The altitude of 2 000 meters is the area with the largest difference between the surface area and the vertical projection area in Shaanxi section Qinling mountains；（3）Compared with the vertical projection area, the area of low mountains, medium mountains and submountains in Shaanxi section of the Qinling mountains increased by 2 301.54 km2, 6 181.67 km2 and 691.60 km2 respectively, with the growth rate of 10.68%, 18.37% and 18.25% respectively.（4）The difference between the surface area and the vertical projection area is various in different land use types. Not using land is the largest, the difference is 34%. The second is forest land which the difference is 28%, and the lawn is approximately 20%. Difference is small in farmland, other forest land, water and residents and industrial land, which is 12%, 8%, 5% and 8% in turn.
China is an agricultural country. Yield estimating on field scales rapidly and accurately is not only instructional to farmers’ field management, but also important for the response evaluation of farmland ecosystems to climate change, making scientific and rational food policies, external food trade and so on. The current primary estimation models include empirical statistical model, light use efficiency model, and crop growth model. Each type of model is relatively complete in its individual research filed, but all of them have certain amount of limitations. Remote sensing technology was used to estimate crop yield on a field scale within small regional areas. A farm of Heilongjiang Province was selected as the study area, and the soybean was as the research object. Based on the coupled CASA-WOFOST model and time-series HJ-1A/B remotely sensed data which covering the entire growing season of soybean to generate high temporal resolution Normalized Difference Vegetation Index (NDVI), we achieved daily continuous monitoring of crop and simulating crop yield by CASA model and CASA-WOFOST model respectively. The results indicated that the coupled model had a faster running speed of the light use efficiency model, it could also give full play to mechanism advantages of crop growth model and overcome the limitations of the CASA model applied to field scales. The R2 of soybean yields increased from 0.668 53 to 0.844 72 and RMSE decreased from 51.41 to 29.52 kg/ha. It is indicated that the coupled mode of light use efficiency model and crop growth model could simultaneously consider the light utilization and the whole physiological and ecological process of crop growth. So that the coupled model could improve the precision, reliability, and stability of crop yield estimation, and provide theoretical support for the estimation of crop yields in regional field scales and better serve the development of precision agriculture.
Land Subsidence is one of the most important geological hazards in many areas. In order to prevent disasters caused by land subsidence efficiently, 24 Sentinel-1A images covering area of Tianjin are choosed from 2015 to 2018. Based on Persistent Scatterers InSAR technique, the results of land subsidence for three years are extracted using the precise orbit data and TanDEM-X DEM and compared with the monitoring results of SBAS (Small Baseline Subset) method. Combined with land use types, hydrogeological and traffic data, the characteristics and formation reasons of several subsidence areas are analyzed. The experimental results show that: (1) In recent three years, the land subsidence in Tianjin urban area is relatively slow, with an average speed of less than 8 mm/a. However, suburban land subsidence is still serious with an average speed between 50 mm/a~70 mm/a. The most serious land subsidence area was Wangqingtuo Town in Wuqing district, the total land subsidence was over 200 mm. And there is a trend of connectivity in these subsidence areas. (2) Land subsidence and the falling of groundwater levels have a very high spatial correlation and the difference between the cumulative shape variables obtained by the two methods of SBAS and PSInSAR is less than 5 mm. The results of this study can provide data support for the government of Tianjin.
The classification and recognition of urban black-odor water by remote sensing plays an important role in the supervision and treatment of the black-odor water. Aiming at the problem that the current remote sensing recognition algorithm of black-odor water cannot classify the pollution degree of black-odor water, we conducted field experiments in Shenyang built-up area. The reflectance spectra and water quality parameters of general water, mild and heavy black-odor water were measured. According to the spectral characteristics of different water, based on the ratio of baseline difference of green band reflectance to red band reflectance, a remote sensing classification index BOCI (Black and Odorous water Classification Index) model is proposed. Firstly, BOCI is checked by the measured data on the ground, compared with improved normalized ratio model. The results show that BOCI has higher recognition accuracy. Moreover, BOCI can distinguish between mild and heavy black-odor water, which solves the problem that the existing model cannot classify the pollution degree of the black-odor water. Then, BOCI is applied to the synchronous GF-2 image of Shenyang for further tested, and the recognition accuracy is also high. Finally, BOCI is applied to the four GF-2 images of Shenyang from 2015 to 2018 to monitor the dynamic changes of black-odor water. The results show that the black-odor phenomena of Xinkai River, Nanyun River and Mantang River are gradually improved, but the black-odor phenomena of Huishan Canal are still very serious.
Ecosystem respiration (Re) is an important component of terrestrial ecosystem carbon budget, and it was important to simulate Re accurately. In this study, Re was simulated at daily and 8-day time scales at 24 flux sites (52 site years) including 5 vegetation types by using three typical ecological models established based on remote sensing data, C-flux (the carbon flux model), ReRSM (Ecosystem respiration Remote Sensing Model) and TPGPP (Temperature Precipitation Gross Primary Production) model. Results showed that the three models had different performances. At 52 site years, the ranges of R2 and RMSE were 0.72~0.96 and 0.30~3.47 gCm-2d-1 for the C-flux model, 0.70~0.98 and 0.45~6.07 gCm-2d-1 for the ReRSM model, and 0.76~0.97 and 0.41~2.45 gCm-2d-1 for the TPGPP model. The TPGPP performed best compared with the other two models. R2 simulated with the TPGPP model was higher than the other two models at most site years with proportions of 73% and 67% at daily and 8-day scale, respectively. At daily and 8-day scale, R2 simulated with the ReRSM model was higher than that with the C-flux model at most site years with proportions of 75% and 77%, while RMSE with ReRSM model was higher than that with the C-flux model at most site years with proportions of 79% and 76%, respectively. Results indicated that the ReRSM model could simulate the trends of seasonal variations of Re while model parameters had some uncertainties. One important parameter in the ReRSM model, LSWIsm (Mean annual growing season of land surface water index), which was much lower would result in overestimation of Re, and higher LSWIsm would result in Re underestimation.
Lijiang River is the core of Guilin's landscape. Protecting the ecological environment of Lijiang River Basin has become a national strategy. In this paper, Lijiang River Basin was used as the research area. The GF-1 multispectral image and SAR image were used as the data source. The wavelet fusion algorithm was used to fuse the GF-1 multispectral image and the SAR VV polarized backscatter image. Using random forest algorithm to construct a high-precision recognition model for GF-1 multispectral imagery, GF-1 and sentinel fusion images. The model can extract rivers, coniferous forests, broad-leaved forests, paddy fields, drylands, residential land and other land types that are closely related to the ecological environment of the Lijiang River. The results show that ①the overall accuracy based on GF-1 image classification reaches 96.15% in the 95% confidence interval, and the overall accuracy based on GF-1 and sentinel-1A backscatter coefficient reaches 94.40%. ②The classification accuracy of rivers, broad-leaved forests and drylands based on GF-1 multispectral images reached 97.74%, 93.20%, and 90.90%. They are 7.57%, 8.96%, and 1.22% higher than those based on the fused GF-1 multispectral and SAR data, respectively. The classification accuracy of the other features is similar. ③In the fusion of GF-1 multispectral and SAR data, wavelet transform was used for image fusion. It was found that the karst topography of the fusion image was prominent, which increased the difference of the features of the ground features.
Crop yield is important for national and regional food production, food trade and food security. Traditional yield estimation by satellite remote sensing is limited by many factors such as spatiotemporal resolution and number of bands. UAV imaging hyperspectral technology has been widely applied to modern intelligent agriculture and precision agriculture with its advantages of high spatial and temporal resolution, rich band number and the combination of image and spectrum It is possible to estimate crop yield accurately. The multi-temporal vegetation indices for yield estimation are obtained with different illumination conditions, atmospheric conditions and background values, the differences in these external conditions may result in errors in vegetation indices. Therefore, using these multi-temporal vegetation indices which containing these external conditions for yield estimation is likely to cause errors. To address this problem, this study proposes the concept of “relative spectral variables” and “relative yield” to estimate rice yield using multi- temporal relative variables. Firstly, the bands obtained from hyperspectral imager are combined to establish the Relative Normalized Difference Spectral Index(RNDSI) and the optimal RNDSI are selected for different growth stages. Then, the optimal models of rice yield estimation with different growth stage combinations are determined and validated. The results shows that multiple linear regression model consisting of tillering stage RNDSI[784, 635], jointing stage RNDSI[807, 744], booting stage RNDSI[784, 712] and heading stage RNDSI[816, 736] is the optimal models for rice yield estimation with R2 of 0.74 and RMSE of 248.97 kg/ha. This model is validated and the result is acceptable with average relative error of 4.31%. In conclusions, the relative vegetation index and relative yield can be applied to the pixel-level yield estimation by remote sensing. Besides, the rice yield distribution map is drawn based on the model, which represents the differences of rice yield at different filed positions. The map may be used to carry out precise field management.
Aiming at the problems of low accuracy, large error and high omission rate in the extraction of industrial thermally polluted areas, an extraction method for industrial thermally polluted areas based on the octant method was proposed. That is, firstly, the surface temperature is obtained based on the improved single-channel algorithm, and then the high temperature anomalous area is extracted by the octet method, and the suspected industrial thermal pollution area is obtained by superimposing the building land. Then, using the color steel room as the criterion, Google Earth images were used to further extract the industrial heat-polluted areas. By comparing and analyzing the difference in accuracy of the three methods of Heat Island intensity (HI), improved boxplot, and octave method, it is found that the potential thermal anomaly is the main factor leading to the reduction of results. Among them, the intensity of the heat island and the improved box plot method are affected by human activities. The proportion of residential and commercial areas in the extracted industrial thermal pollution area is not less than that of the industrial area, and the false positive rate is high, The effect is better, and the impact of potential thermal anomalies is avoided to a certain extent. In order to verify the accuracy of the results, randomly selected 10% of industrial thermally polluted pixels were used as verification points. After verification by Google Earth, it was found that the accuracy of the industrial thermally polluted areas extracted by the octave method was more than 70%, up to 92%. Therefore, the octave method is simple and effective for extracting industrial thermally polluted areas, and the results are accurate and reliable. It can avoid the potential thermal anomalies in the target area, and can provide technical support services for environmental protection and industrial capacity reduction monitoring.
According to the application requirements in the process of data automatic acquisition and collection of the ecological monitoring Internet of Things. This paper refers to the related systems and developed a multi-source heterogeneous data automatic collection and aggregation middleware. The middleware had flexible scalability, it could cooperate with data sensing and collecting devices of ecological monitoring Internet of things from different manufacturers, obtain multi-source heterogeneous real-time data, then automatically stored the data into database after normalization. Based on modular design concept, the middleware was composed of three modules: data automatic collecting module, data automatic parsing processing module and data automatic storage module. These modules had high cohesion and low coupling, and closely cooperated to complete the full automatic processing of the monitoring data flow. The middleware was implemented with Python, which were fully used the object-oriented programming. The design of the class was followed by a single responsibility principle and interfaces-oriented programming, which ensured the top-down inheritance and extensibility of the program. The middleware has been fully tested for several months, and it could accomplish the business requirements of monitoring data collecting, parsing processing and automatic warehousing of the ecological monitoring Internet of Things. For the ecological monitoring IoT system, the data automatic collection and aggregation middleware has considerable reference significance and application value.
A method for measuring parcel area based on the instantaneous field of view of the UAV is proposed. Firstly, the internal and external parameters and distortion parameters of the camera on UAV are obtained by calibration, and the single image of the land parcel is corrected for distortion. Then extract the target parcel area in the image and count the number of pixels in the area. The actual area of the plot is obtained by estimating the ratio of pixels to area.The experimental results show that the calculation accuracy of this method increases with the increase of the flying height of the UAV. The calculation accuracy gradually increases after reaching a peak, and the relative error within the effective flight altitude is below 10% ,which can effectively calculate the parcel area. This method has practical significance for mountain operations requiring simple and fast operation and relatively low precision.
Remote sensing monitoring of land degradation can serve policy formulation, restoration optimization, effect evaluation and security early warning, and it is urgent to establish and improve the basic theoretical framework and technical system. Based on the theory of land degradation (desertification) in arid areas in China and abroad, this paper establishes the theoretical framework of remote sensing monitoring of land degradation in arid areas coupled with bio-physical-socio-economic systems, taking ecosystem services as the link and vegetation-habitat interaction as the core. Secondly, based on the proposed theoretical framework and the consistency, stability, inclusiveness and applicability of remote sensing monitoring, a technical method system for remote sensing monitoring and evaluation in arid areas is established with wide-band remote sensing as an example, which mainly includes five parts: local environmental knowledge mining, multi-season spectral mixture decomposition, dryland ecosystem structure analysis and mapping, quantification and evaluation of dryland ecosystem function and dynamic monitoring of dryland ecosystem degradation process. The theoretical framework and technical system can provide reference for remote sensing monitoring, evaluation and comparative analysis of land degradation in different regions and scales in arid areas.
