Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate

There is an urgent need for techniques to rapidly and periodically measure biodiversity and ecosystem services over large landscapes. Conventional vegetation classification and mapping approaches are based on discrete arbitrary classes which do not capture gradual changes in forest type (and corresponding biodiversity and ecosystem services values) from site to site. We developed a simple multi-date NDVI based Mahalanobis distance measure (called eco-climatic distance) that quantifies forest type variability across a moisture gradient for complex tropical forested landscapes on a single ecologically interpretable, continuous scale. This Mahalanobis distance, unlike other distance measures takes into account the variability in the reference class and shared information amongst bands as it is based on the covariance matrix, and therefore is most useful to summarize ecological distance of a pixel to a reference class in multi-band remotely sensed space In this study we successfully apply this measure as a surrogate for tree biodiversity and ecosystem services at two nested scales for the Western Ghats Bio-diversity hotspot. Data from over 500 tree-plots and forest type maps was used to test the ability of this remotely sensed distance to be a surrogate for abundance based tree-species compositional turn-over and as a continuous measure of forest type and ecosystem services. Our results suggest a strong but scale dependant relationship between the remotely-sensed distance measure and floristic distance between plots. The multi-date NDVI distance measure emerges as very good quantitative surrogate for forest type and is a useful complement to existing forest classification systems. This surrogate quantifies forest type variability on a single, continuous quantitative scale and has important applications in conservation planning and mapping and monitoring of hydrologic and carbon storage and sequestration services.     

Memetic search in artificial bee colony algorithm

Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over benchmark as well as real world optimization problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is a enough chance to skip the true solution due to large step size. In order to balance between diversity and convergence capability of the ABC, a new local search phase is integrated with the basic ABC to exploit the search space identified by the best individual in the swarm. In the proposed phase, ABC works as a local search algorithm in which, the step size that is required to update the best solution, is controlled by Golden Section Search approach. The proposed strategy is named as Memetic ABC (MeABC). In MeABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. MeABC is established as a modified ABC algorithm through experiments over 20 test problems of different complexities and 4 well known engineering optimization problems.     

Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution

The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters’ selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.      

An input-output analysis of recreational fishing expenditures (2006 & 2011) across the southern United States

This paper examines the economic contribution of recreational fishing expenditures across the southern United States (US). Input-output models were constructed for each southern state using US Fish and Wildlife Service (USFWS) fishing expenditures data, both saltwater and freshwater, to determine the direct, indirect, and induced effects of these expenditures. Comparisons were made between pre- and post-recession (2006 and 2011), freshwater and saltwater fishing, and between states. Expenditures were essentially unchanged from 2006 to 2011; but numbers of participating anglers increased by ten percent and the economic contribution generated by the expenditures increased by 10–30%, depending on the economic indicator. The variations across states were substantial, with Florida dominating the others, especially in saltwater fishing. This study documents the important economic contributions of recreational fishing.     

Second Order Mutual Information based Grey Wolf Optimization for effective storage and de-duplication

This paper intends to perform de-duplication for enhancing the storage optimization by utilizing the similarity in mutual information. Hence, this paper contributes by proposing a hybrid fingerprint extracting using SH and HC algorithms. Secondly, the data is clustered using the latest technique called as SOMI-GO to extract the metadata. The extracted metadata is stored in metadata server which provides better storage optimization and de-duplication. SOMI-GO is adopted as it provides maximum second-order mutual information based on the similarity index. The proposed SOMI-GO technique is compared with the existing methods such as K-means, K-mode, ED-PSO, ED-GA and ED-GWO in terms of accuracy, TPR, TNR and performance time and the significance of the SOMI-GO method is described.     

Surface characterization and parameter estimation for industrial vision system

In this paper we have presented some geometric techniques to characterize and parametrize surfaces of industrial parts in range images. The surfaces are characterized to one of plane, sphere, cylinder and cone, because they form the majority of object surfaces in man-made industrial parts. The problem has been studied for two different situations. In the first case,a priori knowledge about the surface shape is assumed. In such a situation the problem of surface characterization reduces to that of surface parameter estimation. The standard deviations of the estimated parameters give a measure of uncertainty of characterizing a surface patch to one of the four surface types. In the second case, noa priori information regarding the shape of a surface is available. This includes partially visible surfaces also. To deal with such a situation, a fuzzy classifier is designed using the uncertainty values. The fuzzy classifier classifies the unknown surface patch (including partially visible surfaces) to one of the four surface types. Experimental results with synthetic range images are presented to highlight the distinctive features of our technique.     

The role of liquid ion exchange in the processing of complex solutions

The use of liquid ion exchange in the processing of complex solutions is described, with particular reference to the development and economics of the process for co-extraction and selective stripping of copper and nickel.