Novel algorithm for detection and identification of radioactive materials in an urban environment

This study introduces a novel algorithm to detect and identify radioactive materials in urban settings using time-series detector response data. To address the challenges posed by varying backgrounds and to enhance the quality and reliability of the energy spectrum data, we devised a temporal energy window. This partitioned the time-series detector response data, resulting in energy spectra that emphasize the vital information pertaining to radioactive materials. We then extracted characteristic features of these energy spectra, relying on the formation mechanism and measurement principles of the gammaray instrument spectrum. These features encompassed aggregated counts, peak-to-flat ratios, and peak-to-peak ratios. This methodology not only simplified the interpretation of the energy spectra's physical significance but also eliminated the necessity for peak searching and individual peak analyses. Given the requirements of imbalanced multi-classification, we created a detection and identification model using a weighted k-nearest neighbors(KNN) framework. This model recognized that energy spectra of identical radioactive materials exhibit minimal inter-class similarity. Consequently, it considerably boosted the classification accuracy of minority classes, enhancing the classifier's overall efficacy. We also executed a series of comparative experiments. Established methods for radionuclide identification classification, such as standard KNN, support vector machine, Bayesian network, and random tree, were used for comparison purposes. Our proposed algorithm realized an F1 measure of 0.9868 on the time-series detector response data, reflecting a minimum enhancement of 0.3% in comparison with other techniques. The results conclusively show that our algorithm outperforms others when applied to time-series detector response data in urban contexts.

Changes in rice cropping systems in the Poyang Lake Region, China during 2004-2010

Rice cropping systems not only characterize comprehensive utilization intensity of agricultural resources but also serve as the basis to enhance the provision services of agro-ecosystems. Yet, it is always affected by external factors, like agricultural policies. Since 2004, seven consecutive No.1 Central Documents issued by the Central Government have focused on agricultural development in China. So far, few studies have investigated the effects of these policies on the rice cropping systems. In this study, based upon the long-term field survey information on paddy rice fields, we proposed a method to discriminate the rice cropping systems with Landsat data and quantified the spatial variations of rice cropping systems in the Poyang Lake Region （PLR）, China. The results revealed that： （1） from 2004 to 2010, the decrement of paddy rice field was 46.76 km2 due to the land use change. （2） The temporal dynamics of NDVI derived from Landsat historical images could well characterize the temporal development of paddy rice fields. NDVI curves of single cropping rice fields showed one peak, while NDVI curves of double cropping rice fields displayed two peaks annually. NDVI of fallow field fluctuated between 0.15 and 0.40. NDVI of the flooded field during the transplanting period was relatively low, about 0.20±0.05, while NDVI during the period of panicle initiation to heading reached the highest level （above 0.80）. Then, several temporal windows were determined based upon the NDVI variations of different rice cropping systems. （3） With the spatial pattern of paddy rice field and the NDVI threshold within optimum temporal windows, the spatial variation of rice cropping systems was very obvious, with an increased multiple cropping index of rice about 20.2% from 2004 to 2010. The result indicates that agricultural policies have greatly enhanced the food provision services in the PLR, China.

Mapping rice cropping systems using Landsat-derived Renormalized Index of Normalized Difference Vegetation Index （RNDVI） in the Poyang Lake Region, China

Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is essential. In this study, the Landsat-derived Renorma- lized Index of Normalized Difference Vegetation Index （RNDVI） was proposed based on two temporal windows in which the NDVI values of single and early （or late） rice display inverse changes, and then applied to discriminate rice cropping systems. The Poyang Lake Region （PLR）, characterized by a typical cropping system of single cropping rice （SCR, or single rice） and double cropping rice （DCR, including early rice and late rice）, was selected as a testing area. The results showed that NDVI data derived from Landsat time-series at eight to sixteen days captures the temporal development of paddy rice. There are two key phenological stages during the overlapping growth period in which the NDVI values of SCR and DCR change inversely, namely the ripening phase of early rice and the growing phase of single rice as well as the ripening stage of single rice and the growing stage of late rice. NDVI derived from scenes in two temporal windows, specifically early August and early October, was used to construct the RNDVI for discriminating rice cropping systems in the polder area of the PLR, China. Comparison with ground truth data indicates high classification accuracy. The RNDVI approach highlights the inverse variations of NDVI values due to the difference of rice growth between two temporal windows. This makes the discrimination of rice cropping systems straightforward as it only needs to distinguish whether the candidate rice typeis in the period of growth （RNDVI 〈 0） or senescence （RNDVI 〉 0）.