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Zhang J.,Chinese National Engineering Research Center for Information Technology in Agriculture | Zhang J.,Zhejiang University | Jin Y.,Institute of Agricultural Remote Sensing and Information Application | Jin Y.,Zhejiang Academy of Surveying and Mapping | And 5 more authors.
Intelligent Automation and Soft Computing | Year: 2010

Based on geographical informatics system (GIS) and remote sensing (RS) technologies, we developed an improved valuation model system of ecosystem services value (ESV) assessment and evaluated the value of Zhejiang Province from November 2004 to December 2005 with monthly time step. Five parts of ecosystem services (ES) were included, and they were producing organic matter, atmospheric gas regulation, recycling nutrient matter, holding water resources and conserving soil and water. The universal soil loss equation (USLE) was applied for estimating the service value of conserving soil and water which expected to improve the effect of assessment. From the results, the overall value in the entire region was 1636.81 × 108 RMB, with unbalanced constitution of 5 parts. A clear seasonal division was shown in the overall value and most parts. The distribution pattern in latitude, longitude, altitude was revealed respectively. Most parts of ecosystem services performed consistently response to both seasonal and spatial variation, except for the service of holding water resources and conserving soil and water. The major characteristics of ESV distribution can be roughly explained by the distribution and variation pattern of NPP and other crucial meteorological elements. The assessment of ESV by integrating multi-sourced data can provide an relatively objective result and have some indications for natural resources management. Copyright © 2010, TSI® Press Printed in the USA. All rights reserved. Source


Peng D.-L.,Zhejiang University | Peng D.-L.,Chinese Academy of Sciences | Huang J.-F.,Zhejiang University | Huang J.-F.,Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province | And 8 more authors.
Journal of Zhejiang University: Science B | Year: 2010

We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP. © 2010 Zhejiang University and Springer-Verlag Berlin Heidelberg. Source


Shi J.-J.,Zhejiang University | Shi J.-J.,Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province | Huang J.-F.,Zhejiang University | Huang J.-F.,Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province | And 2 more authors.
Journal of Zhejiang University: Science B | Year: 2013

The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer (MODIS) data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index (EVI) and land surface water index with a central wavelength at 2 130 nm (LSWI2130). In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3×3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination (R 2) value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale. © 2013 Zhejiang University and Springer-Verlag Berlin Heidelberg. Source


Shahtahmassebi A.R.,Zhejiang University | Shahtahmassebi A.R.,Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province | Wang K.,Zhejiang University | Wang K.,Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province | And 13 more authors.
Journal of Mountain Science | Year: 2011

In general, topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images. In this paper, information in shadow was synthesized by using two filling techniques, namely, roifill and imfill, in order to improve the accuracy of forest mapping over mountainous regions. These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County, Zhejiang Province, China. The performance of these methods was compared with two conventional techniques, including cosine correction and multisource classification. The results showed that by applying filling approaches, average overall accuracy of classification was improved by 14 percent. However, through conventional methods this value increased only by 9 percent. The results also revealed that estimated forest area on the basis of shadow- corrected images by 'roifill' technique was much closer to the survey data compared to traditional algorithms. Apart from this finding, our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions. © 2011 Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg. Source


Zhang L.-W.,Zhejiang University | Zhang L.-W.,Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province | Huang J.-F.,Zhejiang University | Huang J.-F.,Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province | And 5 more authors.
Journal of Zhejiang University: Science B | Year: 2013

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for Ta estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10°C, from reconstructed Ta based on MODIS land surface temperature (LST) data. The verification results of maximum T a, minimum Ta, GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale. © 2013 Zhejiang University and Springer-Verlag. Source

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