Regionalization for large area crop estimates by remote sensing

The crop estimates by remote sensing, developing quickly in recent decades, is a up-to-date technique. Regionalization for large area crop estimates by remote sensing, a special applied regionalization, is the foundation of crop estimates in a large area by remote sensing. According to the actual demands of wheat yield estimation by remote sensing and wheat agroclimatic demarcation of China, this paper first puts forward some principles upheld in this regionalization and analyses its main bases. Secondly, it works out the classificatory schemes about the optimum temporal for estimating wheat yield by remote sensing, information sources of space remote sensing and landuse structure in China. Finally, According to the regionalization indices, this study divides the wheat plantable region of China into 14 regions of crop yield estimates and 31 subregions of crop yield estimates.     

High resolution paleoenvironmental and chronological investigations of Norse landnám at Tasiusaq, Eastern Settlement, Greenland

High-resolution paleoenvironmental data from a peat profile with a small pollen source area are used to reconstruct the impacts of landnám on vegetation and soils at a Norse farm complex (∅2 at Tasiusaq) comprising two farms in the Eastern Settlement of Greenland. Analyses include the AMS ¹⁴C dating of plant macrofossil samples and the use of Bayesian radiocarbon calibration to construct improved age-depth models for Norse cultural horizons. The onset of a regional landnám may be indicated by the clearance of Betula pubescens woodland immediately prior to local settlement. The latter is dated to AD 950-1020 (2σ) and is characterised by possible burning of Betula glandulosa scrub to provide grassland pasture for domestic stock. Clearance and grazing resulted in accelerated levels of soil erosion at a westerly farm. This was followed by an easterly migration of settlement and agriculture. Site constraints prevent an assessment of the demise of the easterly farm, but pressures of overgrazing and land degradation may have been the major factors responsible for the abandonment of the earlier farm.     

Depths of Partial Crystallization of H2O-bearing MORB: Phase Equilibria Simulations of Basalts at the MAR near Ascension Island (7 11{degrees}S)

Phase equilibria simulations were performed on naturally quenchedbasaltic glasses to determine crystallization conditions priorto eruption of magmas at the Mid-Atlantic Ridge (MAR) east ofAscension Island (7–11°S). The results indicate thatmid-ocean ridge basalt (MORB) magmas beneath different segmentsof the MAR have crystallized over a wide range of pressures(100–900 MPa). However, each segment seems to have a specificcrystallization history. Nearly isobaric crystallization conditions(100–300 MPa) were obtained for the geochemically enrichedMORB magmas of the central segments, whereas normal (N)-MORBmagmas of the bounding segments are characterized by polybariccrystallization conditions (200–900 MPa). In addition,our results demonstrate close to anhydrous crystallization conditionsof N-MORBs, whereas geochemically enriched MORBs were successfullymodeled in the presence of 0·4–1 wt% H₂O in theparental melts. These estimates are in agreement with direct(Fourier transform IR) measurements of H₂O abundances in basalticglasses and melt inclusions for selected samples. Water contentsdetermined in the parental melts are in the range 0·04–0·09and 0·30–0·55 wt% H₂O for depleted and enrichedMORBs, respectively. Our results are in general agreement (within±200 MPa) with previous approaches used to evaluate pressureestimates in MORB. However, the determination of pre-eruptiveconditions of MORBs, including temperature and water contentin addition to pressure, requires the improvement of magma crystallizationmodels to simulate liquid lines of descent in the presence ofsmall amounts of water. KEY WORDS: MORB; Mid-Atlantic Ridge; depth of crystallization; water abundances; phase equilibria calculations; cotectic crystallization; pressure estimates; polybaric fractionation     

A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures

1 .IntroductionNondestructiveinspection (NDI)isveryimportantforensuringthereliabilityofoffshorestructuresintheirservicelives (Lauraetal.,1 996 ) .Itiswellknownthatdetectionofflawsinvolvesconsider ablestatisticaluncertainties.Asaresult,theprobabilityofdetection (POD)forallflawsofagivensizehasbeenusedintheliteraturetodefinethecapabilityofaparticularNDItechniqueinagivenen vironment.SincethedataofPODusuallyscatterlargely ,itisdifficulttodeterminewhichmodelfitstheavailabledatabest.Thismodelun…     

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.     

The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem

This paper deals with the problem of detecting and correcting cycle-slips in Global Navigation Satellite System (GNSS) phase data by exploiting the Bayesian theory. The method is here applied to undifferenced observations, because repairing cycle-slips already at this stage could be a useful pre-processing tool, especially for a network of permanent GNSS stations. If a dual frequency receiver is available, the cycle-slips can be easily detected by combining two phase observations or phase and range observations from a single satellite to a single receiver. These combinations, expressed in a distance unit form, are completely free from the geometry and depend only on the ionospheric effect, on the electronic biases and on the initial integer ambiguities; since these terms are expected to be smooth in time, at least in a short period, a cycle-slip in one or both the two carriers can be modelled as a discontinuity in a polynomial regression. The proposed method consists in applying the Bayesian theory to compute the marginal posterior distribution of the discontinuity epoch and to detect it as a maximum a posteriori (MAP) in a very accurate way. Concerning the cycle-slip correction, a couple of simultaneous integer slips in the two carriers is chosen by maximazing the conditional posterior distribution of the discontinuity amplitude given the detected epoch. Numerical experiments on simulated and real data show that the discontinuities with an amplitude 2 or 3 times larger than the noise standard deviation are successfully identified. This means that the Bayesian approach is able to detect and correct cycle-slips using undifferenced GNSS observations even if the slip occurs by one cycle. A comparison with the scientific software BERNESE 5.0 confirms the good performance of the proposed method, especially when data sampled at high frequency (e.g. every 1 s or every 5 s) are available.