20 research outputs found
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Integration of Renewable Distributed Energy Resources into Microgrids
Get PDFAlthough the existing traditional power grid can maintain an instantaneous balance between supply and demand, it is constantly facing many problems such as energy crisis, environmental pollution and instable centralized control mode. Regarding to these problems, pioneers have been making efforts in developing microgrid. One key driver of establishing the need of microgrid in research of smart grid is the renewable distributed energy resources. However, it is still challenging to properly integrate them. Primary limitations on renewable distributed energy resources include: intermittency and uncertainty, size and site-dependent, as well as information exchange standardization. In the thesis, author proposes several solutions to address the above-mentioned problems. It includes the study on current status of integration of renewable distributed energy sources into microgrids, the mathematical modeling of each renewable resource such as solar PV panel and wind turbine with energy storage management, the development of forecasting methods by auto-regressive moving average model and persistence method, the design and solution of the optimal sizing and placement of renewable distributed energy by genetic algorithm and nonlinear programming, as well as integration of IEC 61850 into the communication network of the microgrid as a standardization of information exchange. The approaches are performed and validated on three microgrid test beds, to support the fact that renewable distributed energy generation can be gradually and properly integrated into the power grid
The process-based forest growth model 3-PG for use in forest management: A review
No full textForests are a critical resource, and need proper management in the face of dire climatic changes facing the world today. Advances in modelling system result in the formulation of numerous forest modelling approaches to provide an estimation of forests services. One such useful and straightforward forest modelling approach is process-based modelling, relying on physiological processes and biophysical parameters of forest ecosystems. It is based on parametric calculations and allometric equations, delivering crucial outputs for forest management. The dynamic 3-PG (Physiological Principles in Predicting Growth) is a process-based model (PBM) based on an ecosystem physiological process-based modelling approach. The various applications and flexible nature of the 3-PG model have resulted in its adoption and utilization over several regions of the world. The 3-PGS (Physiological Principles in Predicting Growth with Satellite) model is a modified and spatial version of the 3-PG model that took advantages of remote sensing & GIS (Geographical Information System) for estimation of biophysical variables like FAPAR (Fraction of absorbed photosynthetically active radiation), LAI (Leaf area index), and Canopy water content (CWC), which are tedious and laborious to calculate manually. The integration of remote sensing & GIS with PBMs offers insights to predict forest biomass and productivity at a regional level. Also, coupling of the 3-PG/3-PGS model with other modelling and statistical approaches in a GIS environment provides insights into the prediction of species distributions and potential disturbances due to climatic changes. The 3-PG model was originally designed for relatively homogenous forests; but with the recent development, the 3-PGmix has extended its use to mixed species forests. In this review, we have tried to emphasize the general overview, structure, applications, and efficacy of the process-based 3-PG model for forest management. In future, forests and their ecosystem services are expected to be rigorously influenced by climatic variations. Therefore, it is important to understand the role and effectiveness of the forest growth model 3-PG under the influence of climate change. The 3-PG model performs well for a diverse range of conditions for many forest types and species, and could be integrated with other models and approaches in order to widen its functions and applications. Areas such as Fertility Rating (FR), sensitivity and uncertainty of outputs to the model inputs in the 3-PG model requires attention to remove the weaker side, and to increase the effectiveness and accuracy of model outputs. In addition, the model performance can be improved by calculating its parameters from the population of interest, rather than using default values or values from extant literature. Furthermore, high-resolution remote sensing datasets and accurate input field data could increase the accuracy of the 3-PG/3-PGS model predictions at a broad regional level. In general, the simple forest growth model 3-PG delivers practical outputs, which are directly used in forest management. Additionally, the functions and applications of the 3-PG/3-PGS/3-PGmix model could be explored to deal with the impacts of climate change on forests and to ensure the sustainable management of forests
Aboveground Biomass Prediction by Fusing Gedi Footprints with Optical and SAR Data Using the Random Forest in the Mixed Tropical Forest, India
No full textThe objective is to predict forest aboveground biomass density (AGBD) by integrating spaceborne Light detection and Ranging (LiDAR) Global Ecosystem Dynamics Investigation (GEDI) L4A AGBD footprints with optical and synthetic aperture radar (SAR) data using random forest (RF) in the mixed tropical forests of the Shoolpaneshwar wildlife sanctuary (SWLS), Gujarat, India. RF was trained using GEDI L4A AGBD, while 3-fold cross-validation (CV) was used to minimize overfitting or underfitting. RF achieved optimal training accuracy with root mean square error (RMSE) = 35.05 Mg/ha and R-squared (R2) = 0.44, while testing showed that RF had predicted AGBD with RMSE = 30.44 Mg/ha and R2 = 0.46. GEDI derived predictors correlate most with AGBD and are most important in AGBD prediction. The predicted mean AGBD in SWLS is 41.05 Mg/ha, and AGBD patches of greater than 100 Mg/ha lie in the inner parts of SWLS. Overall, the used approach would help assess and monitor carbon dynamics in forest ecosystems
Efficacy of Spatial Land Change Modeler as a forecasting indicator for anthropogenic change dynamics over five decades: A case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India
No full textEfficacy of Advanced Remote Sensing (Hyperspectral and LIDAR) in Enhancing Forest Resources Management
No full textSeamless Landsat-7 and Landsat-8 data composites covering all Amazonia
Get PDFThe use of satellite remote sensing has considerably improved scientific understanding of the heterogeneity of Amazonian rainforests. However, the persistent cloud cover and strong Bidirectional Reflectance Distribution Function (BRDF) effects make it difficult to produce up-to-date satellite image composites over the huge extent of Amazonia. Advanced pre-processing and pixel-based compositing over an extended time period are needed to fill the data gaps caused by clouds and to achieve consistency in pixel values across space. Recent studies have found that the multidimensional median, also known as medoid, algorithm is robust to outliers and noise, and thereby provides a useful approach for pixel-based compositing. Here we describe Landsat-7 and Landsat-8 composites covering all Amazonia that were produced using Landsat data from the years 2013–2021 and processed with Google Earth Engine (GEE). These products aggregate reflectance values over a relatively long time, and are, therefore, especially useful for identifying permanent characteristics of the landscape, such as vegetation heterogeneity that is driven by differences in geologically defined edaphic conditions. To make similar compositing possible over other areas and time periods (including shorter time periods for change detection), we make the workflow available in GEE. Visual inspection and comparison with other Landsat products confirmed that the pre-processing workflow was efficient and the composites are seamless and without data gaps, although some artifacts present in the source data remain. Basin-wide Landsat-7 and Landsat-8 composites are expected to facilitate both local and broad-scale ecological and biogeographical studies, species distribution modeling, and conservation planning in Amazonia
Modelling the growth response to climate change and management of Tectona grandis L. f. using the 3-PGmix model
No full textKey message: Teak (Tectona grandis L. f.) is a native tree species of India. It is one of the most desirable timber species because of its strength, fine texture, and durability. Its growth is strongly dependent on the climatic conditions, but empirical data are often unavailable to support management decisions. The physiological principles for predicting growth incorporated in the 3-PGmix model make it a useful tool in modelling the growth responses and management in the changing climate. We assessed that under elevated atmospheric carbon dioxide (CO2) concentration and no thinning, teak would store more carbon than currently. Context: Uncertainty and lack of scientific understanding about the growth response to climate change and thinning regimes have created challenges in teak sustainability, both regionally and globally. Aims: This research examines climate change and management implications on teak growth in India using the 3-PGmix model. Methods: The 3-PGmix model was coupled with climate scenarios (Representative Concentration Pathway (RCP) 4.5 and 8.5) to forecast growth response up to the year 2100 with 1981–2010 as the baseline under thinning (G-quality, P-quality) regimes. Thinning under G-quality is performed at earlier stand age than P-quality, and then simulations under ‘no thinning’ based on stocking/ha at different thinning intensity. Results: Under ‘no thinning’, predicted net primary productivity (NPP) for RCP4.5 and RCP8.5 became 5.77 t/ha/year and 5.28 t/ha/year in 2100. However, under increasing CO2, it became 7.39 t/ha/year and 8.22 t/ha/year respectively in 2100. In the future, increasing CO2 would be the dominating factor for an increase in teak growth; however, abnormal precipitation and warmer temperature could produce an unforeseen growth condition. The carbon stock and CO2 sequestration are predicted to be higher under no thinning, which signifies the CO2 fertilisation effect in teak. Conclusion: The set of parameters used in 3-PGmix offers an opportunity to predict teak responses to future climatic conditions and management treatments.</p
Efficacy of Spatial Land Change Modeler as a forecasting indicator for anthropogenic change dynamics over five decades: A case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India
No full textAnthropogenic impacts cause Land use and land cover (LULC) changes that adversely disturb the protected area’s (PA). A quantitative evaluation of historical and future LULC changes over 50 years (1999–2049) in a highly exploited Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India is the primary objective of this study. Maximum likelihood classification (MLC) - a supervised classification technique was applied to classify LULC using Landsat 1999, 2009, and 2019 imagery. Land Change Modeler (LCM) embedded in IDRISI Terrset version 18.21 software incorporated with Multilayer perceptron (MLP) neural network and Markov chain with eight driver variables has been the centre for LULC monitoring, change assessment and future predictions. Classified LULC map for the year 1999, 2009 and 2019 show an overall accuracy with Kappa coefficient of 0.9879, 0.9625 and 0.9381 was 99.08%, 97.58% and 95.45% respectively. During 1999–2019, vegetation cover decreased from 29389.50 ha to 21207.35 ha while agricultural land increased from 28479.18 ha to 31920.66 ha respectively. The kappa indices (Kno, Klocation, and Kstandard) values are 0.9992, 0.9703, and 0.9493, respectively. The projected LULC map for 2029, 2039, and 2049 depicts that the vegetation cover will further degrade, while agricultural land would be increased. Overall, the study reveals that the anthropogenic interventions and intents would increase in the upcoming future, which will severely disturb the integrity of a diversity rich PA. This integrated approach of LULC modeling and remote sensing offers a reliable method for SWS management and planning; it recommends taking a few regulatory steps to moderate human-induced disturbances in SWS
Spatio-Temporal Dynamics of Tropical Deciduous Forests under Climate Change Scenarios in India
No full textClimate change directly or indirectly affects forest growth and productivity, disturbing the plant’s physiological processes, composition and distribution patterns. Due to climate change, significant forest changes have been observed in the last half-century. Climatic conditions such as temperature and precipitation are closely related to forest growth and distribution. Also, climatic conditions are commonly interpreted to observe the response of forests to the changing climate. Therefore, there is some mutual relation between climate change and forest dynamics, which need to be critically investigated for sustainable forest management and climate change mitigation. This chapter discusses the studies that highlight the benefits of integrating machine learning algorithms to the study of forest growth and distribution. Machine learning and remotely sensed datasets unlock new opportunities to study the forest cover dynamics and distribution mapping at a large scale, with faster speed and accuracy
Mixed tropical forests canopy height mapping from spaceborne LiDAR GEDI and multisensor imagery using machine learning models
No full textSpatial mapping of forests canopy height (Hcanopy) provides an opportunity to assess above-ground biomass, net primary productivity, carbon dioxide (CO2) sequestration, biodiversity conservation and forest fire risks. This study incorporated a continuous coverage of multi-spectral optical and synthetic aperture radar (SAR) along with sparsely global ecosystem dynamics investigation (GEDI) spaceborne Light Detection and Ranging (LiDAR) data in the machine learning (ML) models for mapping Hcanopy in the mixed tropical forests of Shoolpaneshwar wildlife sanctuary (SWLS), Gujarat, India. We trained seven ML models, including quantile random forest (QRF), support vector machine (SVM), Bayesian regularization for feed-forward neural networks (BRNN), conditional inference random forest (Cforest), Extreme gradient boosting (Xgbtree), multivariate adaptive regression splines (MARS), and k-nearest neighbors (KNN) using GEDI_02A extracted Hcanopy as training data. We used predictors which were extracted from LiDAR (GEDI metrics), multispectral optical (Landsat -8, Sentinel-2), and SAR (ALOS-2/PALSAR-2, Sentinel-1). A 10-fold cross-validation (CV) resampling was used to avoid overfitting or underfitting. The comparison of the models performances shows that the BRNN model has the highest satisfactory accuracy metrics, such as root mean square error (RMSE) of 4.686 m, R-squared (R2) of 0.49 and mean absolute error (MAE) of 3.66 m. Low training samples of tall canopies (>25 m), presence of mixed vegetation, geometric and structural variability and sloppy terrain of SWLS possibly restricted models from performing well. Field validation shows an R2 of 0.55, satisfactory for mixed tropical forests using spaceborne LiDAR. The present work provides insights into using spaceborne LiDAR GEDI data with optical and SAR data for Hcanopy mapping through ML models, which help to manage SWLS and further implications of forest Hcanopy mapping over large spatial scales.</p
