From scaling to governance of the land system: bridging ecological and economic perspectives

One of the main unresolved problems in policy making is the step from scale issues to effective governance. What is appropriate for a lower level, such as a region or location, might be considered undesirable at a global scale. Linking scaling to governance is an important issue for the improvement of current environmental management and policies. Whereas social–ecological science tends to focus on adaptive behavior and aspects of spatial ecological data, new institutional economics focuses more on levels in institutional scales and temporal dimensions. Consequently, both disciplines perceive different scaling challenges while aiming at a similar improvement of effective governance. We propose that future research needs to focus on four themes: (1) How to combine spatial properties such as extent and grain with the economic units of market and agent; (2) How to combine the different governance instruments proposed by both perspectives; (3) How to communicate the different scaling perspectives (hierarchy vs. no hierarchy) and meanings to policy makers and other stakeholders; and (4) How to deal with the non-equilibrium conditions in the real world and the disciplinary perspectives. Here, we hypothesize that a combined system perspective of both disciplines will improve our understanding of the missing link between scaling and governanc

Routing Protocols for Large-Scale Wireless Sensor Networks: A Review

With the advances in micro-electronics, wireless sensor gadgets have been made substantially littler and more coordinated, and large-scale wireless sensor networks (WSNs) based the participation among the noteworthy measure of nodes have turned into a hotly debated issue. "Large-scale" implies for the most part large region or high thickness of a system. As needs be the routing protocols must scale well to the system scope augmentation and node thickness increments. A sensor node is regularly energy-constrained and can't be energized, and in this manner its energy utilization has a very critical impact on the adaptability of the protocol. To the best of our insight, at present the standard strategies to tackle the energy issue in large-scale WSNs are the various leveled routing protocols. In a progressive routing protocol, every one of the nodes are separated into a few gatherings with various task levels. The nodes inside the abnormal state are in charge of data aggregation and administration work, and the low level nodes for detecting their environment and gathering data. The progressive routing protocols are ended up being more energy-proficient than level ones in which every one of the nodes assume a similar part, particularly as far as the data aggregation and the flooding of the control bundles. With concentrate on the various leveled structure, in this paper we give an understanding into routing protocols planned particularly for large-scale WSNs. As per the distinctive goals, the protocols are by and large ordered in light of various criteria, for example, control overhead decrease, energy utilization mitigation and energy adjust. Keeping in mind the end goal to pick up a thorough comprehension of every protocol, we feature their imaginative thoughts, portray the basic standards in detail and break down their points of interest and hindrances. Also a correlation of each routing protocol is led to exhibit the contrasts between the protocols as far as message unpredictability, memory necessities, localization, data aggregation, bunching way and different measurements. At last some open issues in routing protocol plan in large-scale wireless sensor networks and conclusions are proposed

Routing Protocols for Large-Scale Wireless Sensor Networks: A Review

With the advances in micro-electronics, wireless sensor gadgets have been made substantially littler and more coordinated, and large-scale wireless sensor networks (WSNs) based the participation among the noteworthy measure of nodes have turned into a hotly debated issue. "Large-scale" implies for the most part large region or high thickness of a system. As needs be the routing protocols must scale well to the system scope augmentation and node thickness increments. A sensor node is regularly energy-constrained and can't be energized, and in this manner its energy utilization has a very critical impact on the adaptability of the protocol. To the best of our insight, at present the standard strategies to tackle the energy issue in large-scale WSNs are the various leveled routing protocols. In a progressive routing protocol, every one of the nodes are separated into a few gatherings with various task levels. The nodes inside the abnormal state are in charge of data aggregation and administration work, and the low level nodes for detecting their environment and gathering data. The progressive routing protocols are ended up being more energy-proficient than level ones in which every one of the nodes assume a similar part, particularly as far as the data aggregation and the flooding of the control bundles. With concentrate on the various leveled structure, in this paper we give an understanding into routing protocols planned particularly for large-scale WSNs. As per the distinctive goals, the protocols are by and large ordered in light of various criteria, for example, control overhead decrease, energy utilization mitigation and energy adjust. Keeping in mind the end goal to pick up a thorough comprehension of every protocol, we feature their imaginative thoughts, portray the basic standards in detail and break down their points of interest and hindrances. Also a correlation of each routing protocol is led to exhibit the contrasts between the protocols as far as message unpredictability, memory necessities, localization, data aggregation, bunching way and different measurements. At last some open issues in routing protocol plan in large-scale wireless sensor networks and conclusions are proposed

Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis

Land-use-and-land-cover (LULC) mapping is crucial in precision agriculture, environmental monitoring, disaster response, and military applications. The demand for improved and more accurate LULC maps has led to the emergence of a key methodology known as Geographic Object-Based Image Analysis (GEOBIA). The core idea of the GEOBIA for an object-based classification system (OBC) is to change the unit of analysis from single-pixels to groups-of-pixels called `objects\u27 through segmentation. While this new paradigm solved problems and improved global accuracy, it also raised new challenges such as the loss of accuracy in categories that are less abundant, but potentially important. Although this trade-off may be acceptable in some domains, the consequences of such an accuracy loss could be potentially fatal in others (for instance, landmine detection). This thesis proposes a method to improve OBC performance by eliminating such accuracy losses. Specifically, we examine the two key players of an OBC system : Hierarchical Segmentation and Supervised Classification. Further, we propose a model to understand the source of accuracy errors in minority categories and provide a method called Scale Fusion to eliminate those errors. This proposed fusion method involves two stages. First, the characteristic scale for each category is estimated through a combination of segmentation and supervised classification. Next, these estimated scales (segmentation maps) are fused into one combined-object-map. Classification performance is evaluated by comparing results of the multi-cut-and-fuse approach (proposed) to the traditional single-cut (SC) scale selection strategy. Testing on four different data sets revealed that our proposed algorithm improves accuracy on minority classes while performing just as well on abundant categories. Another active obstacle, presented by today\u27s remotely sensed images, is the volume of information produced by our modern sensors with high spatial and temporal resolution. For instance, over this decade, it is projected that 353 earth observation satellites from 41 countries are to be launched. Timely production of geo-spatial information, from these large volumes, is a challenge. This is because in the traditional methods, the underlying representation and information processing is still primarily pixel-based, which implies that as the number of pixels increases, so does the computational complexity. To overcome this bottleneck, created by pixel-based representation, this thesis proposes a dart-based discrete topological representation (DBTR), where the DBTR differs from pixel-based methods in its use of a reduced boundary based representation. Intuitively, the efficiency gains arise from the observation that, it is lighter to represent a region by its boundary (darts) than by its area (pixels). We found that our implementation of DBTR, not only improved our computational efficiency, but also enhanced our ability to encode and extract spatial information. Overall, this thesis presents solutions to two problems of an object-based classification system: accuracy and efficiency. Our proposed Scale Fusion method demonstrated improvements in accuracy, while our dart-based topology representation (DBTR) showed improved efficiency in the extraction and encoding of spatial information

A Survey on Routing Protocols for Large-Scale Wireless Sensor Networks

With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. “Large-scale” means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and other metrics. Finally some open issues in routing protocol design in large-scale wireless sensor networks and conclusions are proposed

Approaches for advancing scientific understanding of macrosystems

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them