Accurate information regarding the structure of crops is critical for the improvement and optimization of land surface models. Multitemporal remote sensing imagery is more effective to determine the crop structure than the single-temporal images because they contain phenological information. Crop structure was extracted based on time series of moderate-resolution imaging spectroradiometer (MODIS) data in the middle Heihe River Basin. A time series of Normalized Difference Vegetation Index (NDVI) data with a 3-day temporal resolution was composed based on daily MODIS reflectance products (MOD 09) from January to December 2011. A total of 120 scenes of composited imagery were integrated into an image data cube of NDVI time series, which was used to extract crop structure for the study area. The spectral curves of corn, wheat, rape, vegetables, and other crops are based on both in situ measurements and visual interpretation. The major crop types were classified by using the adaptive boosting (Adaboost) and support vector machine (SVM) algorithms. The results show that the classification accuracy of Adaboost and SVM was 86.01% and 70.28%, respectively, with Kappa coefficients of 0.8351 and 0.6438, respectively. Summarizing the classification methods used in this study effectively characterize the spatial distribution of the main crops.
KEYWORDS: Vegetation, Climatology, Meteorology, Temperature metrology, Remote sensing, Environmental sensing, Climate change, Data modeling, Data conversion, Analytical research
Based on the protensive GIMMS NDVI data set and meteorological data during 1982-2009 in the Heihe River Basin, a
novel multiple time-scale analysis method, Empirical Mode Decomposition (EMD), is used to diagnose the periodicities
of NDVI, air temperature and precipitation data. At the same time, the relationship among these three elements is
performed. The results indicate that SINDVI, temperature and precipitation have the similar 3 and 10 years quasiperiodic
in the upper reaches of the Heihe River Basin. SINDVI and temperature have the similar 3 and 10 years quasiperiodic,
SINDVI and precipitation have the similar 3, 6, 8 and 15 years quasi-periodic in the middle reaches of the
Heihe River Basin. In the meantime, in the lower reaches of the Heihe River, SINDVI and temperature have the similar 3
and 10 years quasi-periodic, SINDVI and precipitation have the similar 3 and 6 years quasi-periodic. It is indicated that
the temperature and precipitation are both the driving factor affecting the vegetation in the Heihe River Basin. In
addition, the EMD method can be effectively used to analyze the relationship between time series data and the
meteorological data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.