An intelligent energy controller is proposed to manage operation of wireless sensor nodes equipped with energy harvesting devices. The energy controller uses Takagi-Sugeno fuzzy logic and has inputs for the state of the energy buffer and forecasts of solar energy available for harvest. Two different forecasting horizons were investigated, current and next-day, using ideal and pressure-based forecasts. Differential evolution is used to optimize the controller. To validate the evolved controller, a wireless sensor network is simulated using real field-collected environmental data. The optimization goal is to best utilize the solar energy available for harvest while preserving a backup energy reserve. Performing the highest number of operations possible while leaving the energy reserve intact increases deployment time and reliability. The controller using current and next-day energy forecasts made better use of the available energy, indicated by a lower fitness function. However, while it took more measurements when compared to the controller only using the current-day forecast, it also used more reserve energy while still remaining at only a small fraction of the total available reserve. Reserve energy usage using the pressure-based forecast was higher for both forecasting horizons compared to the ideal energy forecast, pointing to further performance improvements possible for a more accurate forecast.

Harvesting-aware energy management for environmental monitoring WSN

This paper reviewed 42 studies of how local knowledge contributes to adaptation to climate and climate change in the Asia-Pacific Region. Most studies focused on traditional ecological or indigenous knowledge. Three simple questions were addressed: (1) How are changes in climate recognized? (2) What is known about how to adapt to changes in climate? (3) How do people learn about how to adapt? Awareness of change is an important element of local knowledge. Changes in climate are recognized at multiple time scales from observations that warn of imminent extreme weather through expectations for the next season to identification of multi-year historical trends. Observations are made of climate, its impact on physical resources, and bio-indicators. Local knowledge about how to adapt can be divided into four major classes: land and water management, physical infrastructure, livelihood strategies, and social institutions. Adaptation actions vary with time scale of interest from dealing with risks of disaster from extreme weather events, through slow onset changes such as seasonal droughts, to dealing with long-term multi-year shifts in climate. Local knowledge systems differ in the capacities and ways in which they support learning. Many are dynamic and draw on information from other places, whereas others are more conservative and tightly institutionalized. Past experience of events and ways of learning may be insufficient for dealing with a novel climate. Once the strengths and limitations of local knowledge (like those of science) are grasped the opportunities for meaningful hybridization of scientific and local knowledge for adaptation expand.

Local knowledge and adaptation to climate change in natural resource-based societies of the Asia-Pacific

Brown and green LAI mapping through spectral indices

Pecan is a major crop in the lower Rio Grande Valley (LRGV), New Mexico. Currently, about 11,000 ha of pecan orchards at various stages of growth are consuming about 40% of irrigation water in the area. Pecan evapotranspiration (ET) varies with age, canopy cover, soil type and method of water management. There is a need for better quantification of pecan ET for the purpose of water rights adjudication, watershed management and agronomical practices. This paper describes a process where remote sensing information from Landsat-5 and Landsat-7 were combined with ground level measurements to estimate pecan ET and field scale actual crop coefficient (Kc) for the LRGV. The results showed that annual pecan water use for 279 fields ranged from 498 to 1,259 mm with an average water use of 1,054 mm. For fields with NDVI > 0.6 (normalized difference vegetation index), which represented mature orchards (total of 232 fields), the annual water use ranged from 771 to 1,259 mm with an average water use of 1,077 mm. The results from remote sensing model compared reasonably well with ground level ET values determined by an eddy covariance system in a mature pecan orchard with an average error of 4% and the standard error of estimate (SEE) ranging from 0.91 to 1.06 mm/day. A small fraction (5%) of the pecan fields were within the range of maximum ET and Kc.

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Using remote sensing to evaluate the spatial variability of evapotranspiration and crop coefficient in the lower Rio Grande Valley, New Mexico

Prediction of crop coefficients from fraction of ground cover and height. Background and validation using ground and remote sensing data

A simple irrigation scheduling approach for pecans

This article evaluates the vitality of the ancient karez systems in various aspects in modern society by providing examples from Turpan region of China. These aspects include the historical and cultural importance, socio-economic impacts, interactions with the surrounding environment, contribution to agricultural biodiversity in arid lands, and unique regional characteristics of karezes. The results show that the karez systems are not only economically feasible but also a sustainable water supply for irrigation and domestic uses. Furthermore, karezes have invaluable historical, cultural, and social significance. However, karez systems are facing abandonment due to the introduction of new technologies as a result of an increase in water demand. Karezes are particularly susceptible to impairment by groundwater withdrawals with modern wells and pumps. In such regions, the proper conservation and maintenance of karez systems will help sustainable water management and contribute to economic development.

Vitality of ancient karez systems in arid lands: a case study in Turpan region of China

Remote-sensing techniques can detect and up-scale leaf-level physiological responses to large areas, and provide significant and reliable information on water use and irrigation management. The objectives of this study were to screen leaf-level physiological changes that occur during the cyclic irrigation of pecan orchards to determine which responses best represent changes in moisture status of plants and link plant physiological changes to remotely sensed surface reflectance data derived from the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus ETM+. The study was conducted simultaneously on two southern New Mexico mature pecan orchards. For both orchards, plant physiological responses and remotely sensed surface reflectance data were collected from trees that were either well watered or in water deficit. Remotely sensed variables included reflectance in band 1, the ratio between shortwave infrared SWIR bands B5:B7, the normalized difference vegetation index, and SWIR moisture indices. Midday stem water potential Ψsmd was the best performing leaf-level physiological response variable for detecting moisture status in pecans. The B5:B7 ratio positively and significantly correlated with Ψsmd in five of six irrigation cycles while multiple linear regression weighted with six remotely sensed surface reflectance variables revealed a significant relationship with moisture status in all cycles in both orchards R2 > 0.73. Because changes in the B5:B7 band ratio and multiple regression of spectral variables correlate with the moisture status of pecan orchards, we conclude that remotely sensed data hold promise for detecting the moisture status of pecans.

Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

A procedure is introduced to estimate daily solar radiation from maximum and minimum temperatures for areas where solar radiation is either unavailable or unreliable. The procedure uses historical temperature data for spatial and temporal self-calibration. The results from the self-calibrated procedure are compared with measured solar radiation data in one coastal and two interior regions. The self-calibrated procedure is also compared with an equation presented by Thornton and Running that also uses historical maximum and minimum temperature to estimate solar radiation. The comparison between measured and predicted solar radiation values for several years shows that the self-calibrated method can predict solar radiation with error ranging from 0–3%. The error from the Thornton and Running equation ranges from 7–11%. In general, the Thornton and Running equation tends to overestimate solar radiation especially for overcast sky conditions such as monsoon season.

Salim Bawazir

Papers

A simple irrigation scheduling approach for pecans

Vitality of ancient karez systems in arid lands: a case study in Turpan region of China

Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

Estimating Solar Radiation from Temperature with Spatial and Temporal Calibration

Estimating Riparian ET through Remote Sensing