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Peng D.,CAS Institute of Remote Sensing | Peng D.,Key Laboratory of Earth Observation | Wu C.,CAS Institute of Geographical Sciences and Natural Resources Research | Li C.,Beijing Research Center for Information Technology in Agriculture | And 8 more authors.
Ecological Indicators | Year: 2017

Advances in the timing of spring green-up date are a typical response of vegetation global change. Long-term observations of plant phenology have been used to track vegetation response to climate change. Normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have been the most commonly used indicators in reconstructing spring green-up dates from remote sensing over the past several decades. The Moderate Resolution Imaging Spectroradiometer (MODIS) based phenology time series from NDVI and EVI with an enhanced TIMESAT algorithm are the two operational phenology products developed recently with sun-sensor geometry corrected reflectance. However, a comprehensive intercomparison and evaluation of these two spring green-up datasets using intensive ground observations have not been conducted, limiting their applications in regional interpretation of land surface phenology. Therefore, we used 455 ground observations from USA National Phenology Network (NPN) and 106 observations from 21 AmeriFlux sites to assess and validate the spring green-up dates for United States for 2000–2013. Our results indicate that the spring green-up dates from NDVI and EVI showed a good agreement in eastern United States, while substantial differences (70 days) were found in western and southern areas. Ground observations generally had a significant correlation (p < 0.01) with the two MODIS spring green-up dates, especially for deciduous broadleaf forests with root mean square error (RMSE) of 12 days and 16 days when compared with USA-NPN and AmeriFlux observations, respectively. Spring green-up dates from EVI overall showed a better relationship and a lower RMSE in reference to both USA-NPN and AmeriFlux observations than NDVI-based spring green-up dates. Our results highlight the importance of a rigorous validation of remote sensing products to better understand their limitations in operational applications. © 2017 Elsevier Ltd


Guo H.,Beijing Normal University | Guo H.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | Guo H.,Chongqing Research Center | Li B.,Beijing Normal University | And 4 more authors.
Forest Policy and Economics | Year: 2014

The Grain for Green program (GGP) is one of the most ambitious forestry projects in China. The GGP uses a public payment scheme to propel the participation of rural households in order to make the program acceptable and sustainable. The modification of the GGP for its long-term effectiveness has raised interest from researchers. However, few researchers have realized the role that rural households play in adjusting the GGP. By building an econometric model, we found that the decision making of rural households is optimal when the sum of the marginal benefit from residual farmland and the marginal benefit from agricultural labor time equals the sum of the subsidies for retired farmland, benefits from the increased forest/grassland and the opportunity cost rate of the rural household engaged in agricultural labor divided by agricultural labor efficiency. The results derived by a Logit regression method indicate that the economic benefit and non-monetary values stimulate households' willingness to participate, and households' attitudes have significant effects on their willingness. Our attempt to comprehensively explore the influencing factors concerning households' attitudes, the environment benefits and benefits from the GGP proves to be promising as a reference for future studies and for decision making regarding GGP. © 2014 Elsevier B.V.


Lyu H.,Nanjing Normal University | Li X.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | Wang Y.,Nanjing Normal University | Jin Q.,Nanjing Normal University | And 3 more authors.
Science of the Total Environment | Year: 2015

Fourteen field campaigns were conducted in five inland lakes during different seasons between 2006 and 2013, and a total of 398 water samples with varying optical characteristics were collected. The characteristics were analyzed based on remote sensing reflectance, and an automatic cluster two-step method was applied for water classification. The inland waters could be clustered into three types, which we labeled water types I, II and III. From water types I to III, the effect of the phytoplankton on the optical characteristics gradually decreased. Four chlorophyll-a retrieval algorithms for Case II water, a two-band, three-band, four-band and SCI (Synthetic Chlorophyll Index) algorithm were evaluated for three water types based on the MERIS bands. Different MERIS bands were used for the three water types in each of the four algorithms. The four algorithms had different levels of retrieval accuracy for each water type, and no single algorithm could be successfully applied to all water types. For water types I and III, the three-band algorithm performed the best, while the four-band algorithm had the highest retrieval accuracy for water type II. However, the three-band algorithm is preferable to the two-band algorithm for turbid eutrophic inland waters. The SCI algorithm is recommended for highly turbid water with a higher concentration of total suspended solids. Our research indicates that the chlorophyll-a concentration retrieval by remote sensing for optically contrasted inland water requires a specific algorithm that is based on the optical characteristics of inland water bodies to obtain higher estimation accuracy. © 2015 Elsevier B.V..


PubMed | Satellite Environment Application Center, Chongqing Institute of Surveying and Planning for Land Resources and Housing, National University of Singapore and Nanjing Normal University
Type: | Journal: The Science of the total environment | Year: 2015

Fourteen field campaigns were conducted in five inland lakes during different seasons between 2006 and 2013, and a total of 398 water samples with varying optical characteristics were collected. The characteristics were analyzed based on remote sensing reflectance, and an automatic cluster two-step method was applied for water classification. The inland waters could be clustered into three types, which we labeled water types I, II and III. From water types I to III, the effect of the phytoplankton on the optical characteristics gradually decreased. Four chlorophyll-a retrieval algorithms for Case II water, a two-band, three-band, four-band and SCI (Synthetic Chlorophyll Index) algorithm were evaluated for three water types based on the MERIS bands. Different MERIS bands were used for the three water types in each of the four algorithms. The four algorithms had different levels of retrieval accuracy for each water type, and no single algorithm could be successfully applied to all water types. For water types I and III, the three-band algorithm performed the best, while the four-band algorithm had the highest retrieval accuracy for water type II. However, the three-band algorithm is preferable to the two-band algorithm for turbid eutrophic inland waters. The SCI algorithm is recommended for highly turbid water with a higher concentration of total suspended solids. Our research indicates that the chlorophyll-a concentration retrieval by remote sensing for optically contrasted inland water requires a specific algorithm that is based on the optical characteristics of inland water bodies to obtain higher estimation accuracy.


Li Y.,University of South China | Li Y.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | Li Y.,Chongqing Research Center | Gao M.,University of South China | And 6 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2014

The spatial distribution and variation of rural settlement can be well presented by the fractal characteristics of rural settlement. This paper investigated the spatial variation of rural settlement and its influencing factors using fractal dimension in four typical ecological zones: shallow hilly region, low altitude hilly region, Three Gorges reservoir area and high altitude mountain region in Chongqing, China. The fundamental data including the rural settlement, hydrology, transportation, elevation and slope were extracted and processed using Arcgis 10.0 GIS software, and the sensor data including economy, population, and living space per capita were collected from the China Statistical Yearbook. The fractal dimension of rural settlement, drainage and transportation were calculated using Matlab. Differences of fractal dimension of the rural settlement were observed among four regions, which were mainly contributed by elevation, slope, per capita annual net income of farmers, drainage and transportation. The highest fractal dimension (1.63) of the rural settlement was presented in the shallow hilly region, while the lowest (1.47) in the high altitude mountain region. The rural settlement had higher fractal dimension than the transportation and drainage. It was distributed mainly in the area with the elevation <800 m, and clustered mainly in the area where the elevation varied between 300 and 500 m. The highest fractal dimension (1.5) of the shallow hilly region was presented in the area where the elevation varied between 300 and 500 m, while those of the low altitude hilly and high altitude mountain region were observed in the areas with elevation varied between 500 and 800 m with the corresponding fractal dimension of 1.48, and 1.37, respectively. The fractal dimension of the rural settlement showed similar changing patterns in the same slope belt. The highest fractal dimension were presented in two slope belts where the rural settlements were distributed mainly with the slope of the two belts varying between 6° and 15°, and between 15° and 25°, respectively. Fractal dimension of the rural settlement was positively related with per capita annual net income of farmers. High fractal dimension was observed in the ecological area with high per capita annual net income of farmers. Negatively correlation of fractal dimension was observed between rural settlements and population. The fractal dimension of the rural settlement showed decreasing or steady trends in a short period, while it would increase in a long period as the development of economy and society, and the implementation of urbanization policies. The boundary tortuosity of the rural settlements tended to decline because of the boundary shape of settlements becoming more regular, but the change rate of fractal dimension of the four ecological regions won't be the same because of the restraint of location and economy. The findings in this work would facilitate the practical applications in rural residential consolidation and layout planning in similar regions.


Zheng C.,Southwest University | Zheng C.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | Zheng C.,Chongqing Research Center | Yuan D.,Southwest University | And 4 more authors.
Applied Mechanics and Materials | Year: 2013

Unmanned aerial vehicle remote sensing (UAVRS) technique was applied to emergency monitor Cocktail Mountain landslip geological hazard in Wulong county of Chongqing. The paper analyzes methods of accessing and processing digital image data in mountainous area and its application to emergency response management of geological hazard. The conclusion shows that UAVRS can access quickly high-precision remote sensing digital map and supply newly and explicit data for emergency decision. Combing with GIS spatial analyst modeling, UAVRS can assess high-speed hazard and provide base of allocating relief supplies. UAVRS integrating GIS can bring up potential and secondary disaster points for guaranteeing safety of personnel and property. UAVRS can also provide hazard information timely and exactly for pacifying the public to avoid panic. So UAVRS technique can provide technical support for emergency response management of geological hazard. © (2013) Trans Tech Publications.


Wang Y.,CAS Institute of Remote Sensing | Wang Y.,University of Chinese Academy of Sciences | Liu L.,CAS Institute of Remote Sensing | Hu Y.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | And 4 more authors.
International Journal of Remote Sensing | Year: 2016

Landsat satellites have the longest history of making global-scale Earth observations at medium spatial resolution of any series of satellites and have been widely used in various remote-sensing fields. However, many remote-sensing applications, including large-area or long-term land-cover monitoring, need Landsat reflectance data that have had accurate atmospheric correction carried out. In this research, a MODIS-based per-pixel atmospheric correction procedure was developed and employed to produce the surface reflectance (SR) product. A total of 510 Landsat-8 Operational Land Imager (OLI) scenes covering the whole of China in 2013 were collected and processed. The mean relative differences between the surface and top-of-atmosphere (TOA) reflectance for China, composited and expressed as percentages, were found to be 67, 47, 18, 13, 4, 4, and 7% for Landsat-8 OLI bands 1, 2, 3, 4, 5, 6, and 7, respectively. Then, the accuracy of MODIS atmospheric products was validated using ground-based sun-radiometer observation network data, including Sun/sky-radiometer Observation Network (SONET) and Aerosol Robotic Network (AERONET) data collected from 14 SONET/AERONET stations. The validation results showed that the MODIS atmospheric products are reliable for China, with an R2 value of 0.78 and a root mean square error (RMSE) value of 0.12 for aerosols, and an R2 value of 0.98 and an RMSE of 0.25 for water vapour. Third, the SR product using our per-pixel atmospheric correction method was evaluated by comparison with the MODIS daily surface reflectance product (MOD09GA) and the United States Geological Survey (USGS) provisional Landsat-8 SR product, with a mean R2 of 0.93 and an RMSE of 0.02 for MOD09GA; and with a mean R2 of 0.97 and an RMSE of 0.01 for the USGS SR product. Finally, the advantage of our per-pixel atmospheric correction method over the per-scene method was investigated by analysis of the spatial variation of the atmospheric parameters within one Landsat scene (about 1.5 (Formula presented.) 1.5 (Formula presented.)), with a mean standard deviation value of 0.03–0.09 for aerosol. When such aerosol variation was omitted as the per-scene atmospheric correction method, the SR absolute error due to aerosol optical thickness (AOT) spatial variation was about 0.027, 0.018, 0.005, 0.003, 0.002, 0.0007, and 0.003 for the seven reflectance bands of Landsat-8. Therefore, use of Landsat-8 SR products over China with our per-pixel atmospheric correction was proved reliable, and more promising than the per-scene method, especially for the short-wavelength bands. © 2016 Taylor & Francis.


Hu Y.,CAS Institute of Remote Sensing | Hu Y.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | Liu L.,CAS Institute of Remote Sensing | Caccetta P.,CSIRO | Jiao Q.,CAS Institute of Remote Sensing
Journal of Remote Sensing | Year: 2015

Time-series remote sensing images were previously employed to detect land use and land-cover changes and to analyze related trends. However, land-cover change mapping using time-series remote sensing data, especially medium-resolution imagery, was often constrained by a lack of high-quality training and validation data, especially for historical satellite images. In this study, we tested and evaluated a generalized classifier for time series Landsat Thematic Mapper (TM) imagery based on spectral signature extension. First, a new atmospheric correction procedure and a robust relative normalization method were performed on time-series images to eliminate the radiometric differences between them and to retrieve the surface reflectance. Second, we selected one surface reflectance image from the time series as a source image based on the availability of reliable ground truth data. The spectral signature was then extracted from the training data and the source image. Third, the spectral signature was extended to all the corrected time-series images to build a generalized classifier. This method was tested on a time series consisting of five Landsat TM images of the Tibetan Plateau, and the results showed that the corrected time-series images could be classified effectively from the reference image using the generalized classifier. The overall accuracy achieved was between 88.35% and 94.25%, which is comparable with the results obtained using traditional scene-by-scene supervised classification. Results also showed that the performance of the extension method was affected by the difference in acquisition times of the source image and target image. ©, 2015, Journal of Remote Sensing. All right reserved.


Peng D.,CAS Institute of Remote Sensing | Hu Y.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | Li Z.,Chinese Academy of Agricultural Sciences
Journal of Applied Spectroscopy | Year: 2016

It is important to detect and quantify deforestation to guide strategic decisions regarding environment, socioeconomic development, and climate change. In the present study, we conducted a field experiment to examine spectral reflectance and vegetation index changes in poplar and locust tree foliage with different leaf area indices over the course of three sunny days, following tree removal from the canopy. The spectral reflectance of foliage from harvested trees was measured using an ASD FieldSpec Pro spectroradiometer; synchronous meteorological data were also obtained. We found that reflectance in short-wave infrared and red-edge reflectance was more time sensitive after tree removal than reflectance in other spectral regions, and that the normalized difference water index (NDWI) and the red-edge chlorophyll index (CIRE) were the preferred indicators of these changes from several indices evaluated. Synthesized meteorological environments were found to influence water and chlorophyll contents after tree removal, and this subsequently changed the spectral canopy reflectance. Our results indicate the potential for such tree removal to be detected with NDWI or CIRE from the second day of a deforestation event. © 2016 Springer Science+Business Media New York.


Zheng C.,Southwest University | Zheng C.,Chongqing Institute of Surveying and Planning for Land Resources and Housing
WIT Transactions on Information and Communication Technologies | Year: 2014

This paper makes a research on the urgency problems and overall idea of establishing cultivated land protection fund which aims to provide policy reference to the establishing Chongqing cultivated land protection fund. Methods of documentary data approach and comparative analysis approach are applied. The result of the study indicates that (i) it is urgent to establish cultivated land protection fund in Chongqing; (ii) funding gap of cultivated land protection is much bigger; (iii) the subsidy standard of cultivated land protection is hard to be unified; (iv) the square measure of cultivated land protection subsidy ascertains difficult; (v) the subsidy way of cultivated land protection is difficult to be unified; (vi) it is hard to coordinate establishing of cultivated land protection; (vii) the supervision and control of cultivated land protection funds is troublesome. The conclusion of this paper shows that (i) building up other classifications and levels of subsidy standards; (ii) expanding source channel of cultivated land protection fund; (iii) perfecting statistics and registering of cultivated land; (iv) granting subsidy of cultivated land protection by various forms; (v) establishing corporation responsibility of cultivated land protection funds; (vi) building up strict fund supervision and administration management. © 2014 WIT Press.

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