Search Results (6)

Taihu Lake is a large lake with high levels of eutrophication. Cyanobacterial outbreaks significantly affect the ecological environment and socioeconomic development. The chlorophyll-a (Chl-a) concentration, which is crucial for monitoring eutrophication, can be obtained through remote sensing inversion, and the random, sudden, and complex changes impose stringent requirements on the monitoring scale. However, single remote sensing images often fail to meet both the high temporal and spatial resolution requirements for Chl-a monitoring. This study took Taihu Lake as the research object, combined COMS-1 GOCI (1 h/500 m resolution) and Sentinel-2 MSI (5 d/10 m resolution) inverted Chl-a data, and developed a precorrection-based spatiotemporal downscaling method (PC-STDM). After eliminating systematic bias, the model used temporal weighting downscaling (TWD) and regression trend assessment downscaling (TRAD) methods to downscale the inverted Chl-a data, improving the temporal resolution of the Sentinel-2 MSI Chl-a inversion data from 5 d to 1 h. The verification resulted in an average R² of 0.87 between the COMS-1 GOCI and Sentinel-2 MSI Chl-a data after adaptive correction. A comparison with the measured Chl-a data yielded a maximum fitting coefficient of 0.98, verifying the credibility of the model. The downscaled Chl-a concentration data detailed hourly changes and development trends, providing support for water quality monitoring in the Taihu Lake area. Full article

The significant differences in data domains between SAR images and the expensive and time-consuming process of data labeling pose significant challenges to terrain classification. Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies. Full article

Access to land for many people in Africa is insecure and continues to pose risks to poverty, hunger, forced evictions, and social conflicts. The delivery of land tenure in many cases has not been adequately addressed. Fit-for-purpose spatial frameworks need to be adapted to the context of a country based on simple, affordable, and incremental solutions toward addressing these challenges. This paper looked at three case studies on the use of the Social Tenure Domain Model (STDM) tool in promoting the development of a fit-for-purpose land administration spatial framework. Data gathering from primary and secondary sources was used to investigate the case studies. The empirical findings indicated that the use and application of the STDM in support of the fit-for-purpose land administration framework is quite effective and can facilitate the improvement in land tenure security. The findings also revealed that the tool, together with participatory and inclusive processes, has the potential to contribute to other frameworks of Fit-For-Purpose Land Administration (FFP LA) toward influencing changes in policy and institutional practices. Evidently, there was a remarkable improvement in the institutional arrangements and collaboration among different institutions, as well as a notable reduction in land conflicts or disputes in all three case studies. Full article

Currently, high-temperature risk assessments of crops at the regional scale are usually conducted by comparing the observed air temperature at ground stations or via the remote sensing inversion of canopy temperature (such as MODIS (moderate-resolution imaging spectroradiometer) land surface temperature (LST)) with the threshold temperature of the crop. Since this threshold is based on the absolute temperature value, it is difficult to account for changes in environmental conditions and crop canopy information between different regions and different years in the evaluation model. In this study, MODIS LST products were used to establish an evaluation model (spatiotemporal deviation mean (STDM)) and a classification method to determine maize-growing areas at risk of high temperatures at the regional scale. The study area was the Huang-Huai-Hai River plain of China where maize is grown and high temperatures occur frequently. The spatiotemporal distribution of the high-temperature risk of summer maize was determined in the study area from 2003 to 2018. The results demonstrate the applicability of the model at the regional scale. The distribution of high-temperature risk in the Huang-Huai-Hai region was consistent with the actual temperature measurements. The temperatures in the northwestern, southwestern, and southern parts were relatively high and the area was classified as a stable zone. Shijiazhuang, Jiaozuo, Weinan, Xi’an, and Xianyang city were located in a zone of increasing high temperatures. The regions with a stable high-temperature risk were Xiangfan, Yuncheng, and Luoyang city. Areas of decreasing high temperatures were Handan, Xingtai, Bozhou, Fuyang, Nanyang, Linfen, and Pingdingshan city. Areas that need to focus on preventing high-temperature risks include Luoyang, Yuncheng, Xianyang, Weinan, and Xi’an city. This study provides a new method for the detailed evaluation of regional high-temperature risk and data support. Full article

The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods. Full article

The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson’s rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves. Full article