• Energy Research
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The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationshipsamong Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climatevariables were quantified for six counties within the NTP. The notable and unevenalterations of the grassland in response to variation of climate and human impact in theNTP were revealed. Over the last two decades of the 20th century, the maximum greennessof the grassland has exhibited high increase, slight increase, no-change, slight decrease andhigh decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area ofthe NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in thecentral-eastern (eastern) NTP whereas little change was observed in the western andnorthwestern NTP. A strong negative relationship between VP-NDVI and ET0 was foundin sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgonand Amdo counties), suggesting that the ET0 is one limiting factor affecting grasslanddegradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chaliand Sokshan counties), a significant inverse correlation between VP-NDVI and populationindicates that human activities have adversely affected the grassland condition as waspreviously reported in the literature. Results from this research suggest that the alterationand degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.

The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationshipsamong Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climatevariables were quantified for six counties within the NTP. The notable and unevenalterations of the grassland in response to variation of climate and human impact in theNTP were revealed. Over the last two decades of the 20th century, the maximum greennessof the grassland has exhibited high increase, slight increase, no-change, slight decrease andhigh decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area ofthe NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in thecentral-eastern (eastern) NTP whereas little change was observed in the western andnorthwestern NTP. A strong negative relationship between VP-NDVI and ET0 was foundin sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgonand Amdo counties), suggesting that the ET0 is one limiting factor affecting grasslanddegradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chaliand Sokshan counties), a significant inverse correlation between VP-NDVI and populationindicates that human activities have adversely affected the grassland condition as waspreviously reported in the literature. Results from this research suggest that the alterationand degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.

Underwater acoustic sensor networks (UWA-SNs) are envisioned to perform monitoring tasks over the large portion of the world covered by oceans. Due to economics and the large area of the ocean, UWA-SNs are mainly sparsely deployed networks nowadays. The limited battery resources is a big challenge for the deployment of such long-term sensor networks. Unbalanced battery energy consumption will lead to early energy depletion of nodes, which partitions the whole networks and impairs the integrity of the monitoring datasets or even results in the collapse of the entire networks. On the contrary, balanced energy dissipation of nodes can prolong the lifetime of such networks. In this paper, we focus on the energy balance dissipation problem of two types of sparsely deployed UWA-SNs: underwater moored monitoring systems and sparsely deployed two-dimensional UWA-SNs. We first analyze the reasons of unbalanced energy consumption in such networks, then we propose two energy balanced strategies to maximize the lifetime of networks both in shallow and deep water. Finally, we evaluate our methods by simulations and the results show that the two strategies can achieve balanced energy consumption per node while at the same time prolong the networks lifetime.

Underwater acoustic sensor networks (UWA-SNs) are envisioned to perform monitoring tasks over the large portion of the world covered by oceans. Due to economics and the large area of the ocean, UWA-SNs are mainly sparsely deployed networks nowadays. The limited battery resources is a big challenge for the deployment of such long-term sensor networks. Unbalanced battery energy consumption will lead to early energy depletion of nodes, which partitions the whole networks and impairs the integrity of the monitoring datasets or even results in the collapse of the entire networks. On the contrary, balanced energy dissipation of nodes can prolong the lifetime of such networks. In this paper, we focus on the energy balance dissipation problem of two types of sparsely deployed UWA-SNs: underwater moored monitoring systems and sparsely deployed two-dimensional UWA-SNs. We first analyze the reasons of unbalanced energy consumption in such networks, then we propose two energy balanced strategies to maximize the lifetime of networks both in shallow and deep water. Finally, we evaluate our methods by simulations and the results show that the two strategies can achieve balanced energy consumption per node while at the same time prolong the networks lifetime.

Corals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city’s skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragility and exposure to various threats, including the impacts of climate change. Similar to successful city management, the utilization of advanced underwater videography, photogrammetric computer vision, and machine learning can facilitate precise 3D modeling and the semantic mapping of coral reefs, aiding in their careful management and conservation to ensure their survival. This study focuses on generating detailed 3D mesh models, digital surface models, and orthomosaics of coral habitats by utilizing underwater coral images and control points. Furthermore, an innovative multi-modal deep neural network is designed to perform the pixel-wise semantic segmentation of orthomosaics, enabling the projection of resulting semantic maps onto a 3D space. Notably, this study achieves a significant milestone by accomplishing semantic fine-grained 3D modeling and rugosity evaluation of coral reefs with millimeter-level accuracy, providing a potent means to understand coral reef variations under climate change with high spatial and temporal resolution.

Corals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city’s skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragility and exposure to various threats, including the impacts of climate change. Similar to successful city management, the utilization of advanced underwater videography, photogrammetric computer vision, and machine learning can facilitate precise 3D modeling and the semantic mapping of coral reefs, aiding in their careful management and conservation to ensure their survival. This study focuses on generating detailed 3D mesh models, digital surface models, and orthomosaics of coral habitats by utilizing underwater coral images and control points. Furthermore, an innovative multi-modal deep neural network is designed to perform the pixel-wise semantic segmentation of orthomosaics, enabling the projection of resulting semantic maps onto a 3D space. Notably, this study achieves a significant milestone by accomplishing semantic fine-grained 3D modeling and rugosity evaluation of coral reefs with millimeter-level accuracy, providing a potent means to understand coral reef variations under climate change with high spatial and temporal resolution.