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This paper investigates the possible relationship between solar activity and monthly rainfall in Pokhara and Kathmandu cities in Nepal from the year 1968 to 2023. The study uses Cross Correlation Analysis (CCA) and wavelet analysis, the Cross-Wavelet Transform (XWT) and Wavelet Transform Coherence (WTC), to analyze the time series data. The CCA shows a strong positive correlation between the solar cycle and rainfall trend, with correlation coefficients of 0.77 and 0.76 and time lags of 18 months and 14 months at Pokhara and Kathmandu, respectively. The p-values, calculated to be < 0.0001 through the t-test, indicate that the observed correlations are statistically significant. The XWT results show a long-term ∼ 11-year common periodicity in solar cycle and rainfall. The WTC analysis confirms the local correlation between these two time series in time–frequency domain at a common periodicity. The results indicate that the monthly rainfall pattern in Nepal is closely related to the solar cycle pattern. Thus, solar activity likely plays a role in influencing rainfall trends in Nepal. This study shows that solar-climate interaction on a regional scale is complex and emphasizes the importance of understanding these relationships for predicting and managing climate change. [ABSTRACT FROM AUTHOR]

This paper examines the role of experts in implementing Reducing Emissions from Deforestation and Forest Degradation (REDD+) initiatives at the national level and explores the performative politics and power dynamics involved. Original research conducted in Malaysia, Nepal, and Sri Lanka, where REDD+ efforts were underway, provides the empirical basis for the study. The findings reveal tensions between the official win-win narrative of REDD+ and the reflective perspectives of experts based on their empirical knowledge and unique positionalities. These tensions raise questions about the underlying norms and values associated with REDD+. However, the pursuit of professional development within the international development and conservation sector, reliant on donor funding, impedes constructive reflection. The alignment of experts with donor visions for career advancement contributes to this obstacle. Therefore, the paper argues for creating space for a more reflexive approach among scholars and practitioners to foster comprehensive reflection and a fundamental rethink of the future of REDD+. • Examination of REDD+ experts' discourses at the national level reveals another operational challenge for REDD+. • Empirical observations and past experiences of REDD+ experts raise doubts about the core norms and values of REDD+. • The lack of emphasis on this empirical knowledge in international policy processes hinders REDD+'s progress. [ABSTRACT FROM AUTHOR]

Deer surveys play an important role in the estimation of local ecological balance. In the Chitwan National Park of Nepal, the dense tree canopies and tall vegetation often obscure the presence of wild deer, which has a negative effect on the accurate population surveys of wild deer. UAVs equipped with infrared sensors have been increasingly used to monitor wild deer by capturing a lot of images. How to automatically recognize and obtain the number of deer objects from thermal images is becoming an important research topic. Due to the difference between thermal images and true-color images, as well as the variations in deer object sizes in these two types of images, current ready-to-use object detection models, designed for true-color imagery, are ill-suited for the task of detecting small deer objects within thermal imagery. In this paper, an enhanced Faster R-CNN was constructed to detect small deer objects from thermal images, in which a Feature Pyramid Network (FPN) based on a residual network is used to improve feature extraction for small deer objects and multi-scale feature map constrution for the subsequent region proposals searching, bounding box regression, and regions of interest (RoIs) classification. In addition, small-scaled anchor boxes and a multi-scale feature map selection criterion are devised to improve the detection accuracy of small objects. Finally, based on Faster R-CNN, FPN, and different residual networks including ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152, we constructed five object detection models, and evaluated their detection performance by using COCO evaluation matrix. Under the condition of IoU ≥ 0.5 , the integration of Faster R-CNN, FPN, and ResNet18 demonstrated to perform better than others. Specifically, The COCO evaluation results revealed an Average Precision (AP) score of 91.6% for all deer objects. Small deer objects (area ≤ 200 pixels) achieved an AP score of 73.6%, medium deer objects (200 < area ≤ 400 pixels) demonstrated an AP score of 93.4%, and large deer objects (area > 400 pixels) achieved the highest AP score of 94.3%. Our research is helpful for effective wild deer monitoring and conservation and can be a valuable reference for the exploration of small object detection from low-resolution thermal images. • Drones with infrared sensors capture thermal images for wild deer surveys in areas covered by dense canopies. • The objects within thermal imagery are small and less than 30 × 30 pixels. • Existing object detection models, trained on true-color images, struggle with small objects detection from thermal imagery. • Feature Pyramid Network fuses the feature information extracted by residual networks to construct multi-scale feature maps. • Enhanced Faster R-CNN with customized anchor boxes and multi-scale feature maps achieves an average precision. [ABSTRACT FROM AUTHOR]