Article
Ecology
A. Jaya Prakash, M. D. Behera, S. M. Ghosh, A. Das, D. R. Mishra
Summary: Mangroves are important for their ability to sequester carbon, but the lack of operational methodologies to model and map their properties has hindered studies on their role in global carbon cycling and climate change. This study establishes a robust methodological protocol for estimating aboveground biomass using field measurements, allometric equations, SAR data, and machine learning models. The protocol demonstrated high prediction accuracy and low uncertainty for a mangrove forest.
ECOLOGICAL INFORMATICS
(2022)
Article
Environmental Sciences
Arunima Singh, Sunni Kanta Prasad Kushwaha, Subrata Nandy, Hitendra Padalia, Surajit Ghosh, Ankur Srivastava, Nikul Kumari
Summary: This study aimed to assess aboveground biomass (AGB) in the Barkot Forest Range, Uttarakhand, India by integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data. Various parameters were derived from the ALOS SAR data, and TLS was used to obtain diameter at breast height (dbh) and tree height. The integration of SAR and TLS data using Random Forest (RF) and Artificial Neural Network (ANN) showed that RF performed better in estimating the biomass with an R-2 value of 0.94 and an RMSE of 59.72 ton ha(-1).
Article
Ecology
Rakesh Fararoda, R. Suraj Reddy, G. Rajashekar, T. R. Kiran Chand, C. S. Jha, V. K. Dadhwal
Summary: Accurate estimation of spatial above ground biomass in tropical forests is crucial for understanding the global carbon cycle. This study combines field inventory data with optical and microwave images to spatially estimate biomass over Indian forests using a random forest approach. The inclusion of multisource data significantly increases the saturation range and reduces estimation error.
ECOLOGICAL INFORMATICS
(2021)
Article
Environmental Sciences
Yuzhen Zhang, Jingjing Liu, Wenhao Li, Shunlin Liang
Summary: Feature selection can improve the accuracy of forest aboveground biomass (AGB) prediction and identify important predictors, but its role in AGB estimation has not received sufficient attention. This study quantified the benefits of feature selection in AGB prediction and proposed a stability-heterogeneity-correlation-based ensemble (SHCE) method that outperformed existing FS methods in terms of prediction accuracy and identification of important features.
Article
Forestry
Barbara Zimbres, Pedro Rodriguez-Veiga, Julia Z. Shimbo, Polyanna da Conceicao Bispo, Heiko Balzter, Mercedes Bustamante, Iris Roitman, Ricardo Haidar, Sabrina Miranda, Leticia Gomes, Fabricio Alvim Carvalho, Eddie Lenza, Leonardo Maracahipes-Santos, Ana Clara Abadia, Jamir Afonso do Prado Junior, Evandro Luiz Mendonca Machado, Anne Priscila Dias Gonzaga, Marcela de Castro Nunes Santos Terra, Jose Marcio de Mello, Jose Roberto Soares Scolforo, Jose Roberto Rodrigues Pinto, Ane Alencar
Summary: A study was conducted to build an aboveground woody biomass model for the Brazilian Cerrado biome using optical and SAR imagery, with Random Forest algorithm showing slightly better results compared to the Classification and Regression Tree model. However, the models underestimated very high aboveground woody biomass and slightly overestimated low biomass.
FOREST ECOLOGY AND MANAGEMENT
(2021)
Article
Environmental Sciences
Mark L. Williams, Anthea L. Mitchell, Anthony K. Milne, Tim Danaher, Geoff Horn
Summary: This study utilizes L-band synthetic aperture radar (SAR) data to estimate vegetation indicators. It proposes a method to reduce the influence of external factors by correcting terrain slope and cross-track tendencies, and normalizing backscatter differences using linear least squares difference minimization. The method is applied in New South Wales, Australia, and demonstrates improved estimation of vegetation and provides spatially explicit forest structural information.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Stephanie P. George-Chacon, David T. Milodowski, Juan Manuel Dupuy, Jean-Francois Mas, Mathew Williams, Miguel Angel Castillo-Santiago, Jose Luis Hernandez-Stefanoni
Summary: Machine learning was successfully used to upscale forest aboveground biomass from field data to remote sensing data, while uncertainty was effectively propagated and the relative contributions of each sensor were explored. Sentinel-2 outperformed ALOS-PALSAR in the model performance, but the combination of both sensors provided the best fit.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Maurizio Santoro, Oliver Cartus, Johan E. S. Fransson
Summary: This article presents a study on the estimation and tracking of carbon density in Swedish forests using satellite L-band observations. The study found that while there were substantial uncertainties at the pixel level, the average values at landscape and county scale were consistent with existing data, and Swedish forests acted as a carbon sink between 2010 and 2015.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Maurizio Santoro, Oliver Cartus, Johan E. S. Fransson
Summary: This study revisited the Water Cloud Model (WCM) for estimating forest biomass-related variables, aiming to reduce systematic retrieval errors associated with empirical assumptions in the model by exploring physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at a larger scale. The integration of allometries in the WCM effectively reduced estimation errors, demonstrating the potential for providing large-scale estimates of biomass-related variables using L-band backscatter observations.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Yifan Hu, Yonghui Nie, Zhihui Liu, Guoming Wu, Wenyi Fan
Summary: This research aims to improve forest aboveground biomass (AGB) estimation by feature selection based on scattering mechanism and sensitivity analysis, and utilizing dual-frequency SAR data. The results show that the RF-AGA model with feature selection performs well in AGB estimation. This study highlights the potential of investigating scattering mechanisms, sensitivity factors, and parameter selection in SAR data for improved forest AGB estimation.
Article
Engineering, Electrical & Electronic
Soni Darmawan, Ita Carolita, Rika Hernawati, Dede Dirgahayu, Agustan, Didin Agustian Permadi, Dewi Kania Sari, Widya Suryadini, Dhimas Wiratmoko, Yohanes Kunto
Summary: This study explores the scattering model of oil palm phenology using spaceborne X-, C-, and L-band polarimetric Synthetic Aperture Radar (SAR) imaging. Different scattering characteristics were observed for X-, C-, and L-band polarimetric SAR, with the potential of C-band on TV polarization. The results suggest that the scattering model has potential for identifying oil palm phenology in Indonesia, with future improvements needed for accuracy.
JOURNAL OF SENSORS
(2021)
Article
Forestry
Igor da Silva Narvaes, Joao Roberto dos Santos, Polyanna da Conceicao Bispo, Paulo Mauricio de Alencastro Graca, Ulisses Silva Guimaraes, Fabio Furlan Gama
Summary: We developed a method for estimating above-ground biomass (AGB) from polarimetric SAR images. The model used power and phase-radar attributes, as well as attributes from Touzi decomposition. The proposed model showed good predictive capacity and a positive correlation with the validation results.
Article
Ecology
Unmesh Khati, Gulab Singh
Summary: This study explores the potential of combining backscatter with polarimetric SAR interferometry (PolInSAR) estimated forest stand height for improved above-ground biomass (AGB) estimation. The results demonstrate the potential of this synergistic combination for AGB mapping over a tropical forest range in India.
FRONTIERS IN FORESTS AND GLOBAL CHANGE
(2022)
Article
Environmental Sciences
Fabio Furlan Gama, Natalia Cristina Wiederkehr, Polyanna da Conceicao Bispo
Summary: This article presents a methodology using Fourier fast transform to filter stripes on radar images caused by ionospheric disturbances. The filtered images were then classified using random forest, showing improved classification performance compared to the original scenes.
Article
Environmental Sciences
Rashmi Malik, Gulab Singh, Onkar Dikshit, Yoshio Yamaguchi
Summary: The polarimetric synthetic aperture radar (PolSAR) provides a complete 3x3 matrix with nine real-valued and independent polarimetric parameters. In the proposed decomposition method, the observed coherency matrix [T] is preprocessed using two consecutive unitary transformations to fit a five-component scattering model. This allows for the retrieval of five powers corresponding to different types of scattering, which can be used for various applications such as polarimetric calibration and classification.
Review
Geosciences, Multidisciplinary
Shivaprasad S. Sharma, Parth Sarathi Roy, V Chakravarthi, G. Srinivasarao, V Bhanumurthy
GEOMATICS NATURAL HAZARDS & RISK
(2017)
Article
Geosciences, Multidisciplinary
P. Bhavani, V Chakravarthi, P. S. Roy, P. K. Joshi, K. Chandrasekar
GEOMATICS NATURAL HAZARDS & RISK
(2017)
Article
Geosciences, Multidisciplinary
Shivaprasad S. Sharma, Parth Sarathi Roy, V Chakravarthi, Srinivasa Rao
GEOMATICS NATURAL HAZARDS & RISK
(2018)
Article
Biodiversity Conservation
Reshma M. Ramachandran, Parth Sarathi Roy, V. Chakravarthi, J. Sanjay, Pawan K. Joshi
ECOLOGICAL INDICATORS
(2018)
Article
Geosciences, Multidisciplinary
Pulakesh Das, Mukunda Dev Behera, Nitesh Patidar, Bhabagrahi Sahoo, Poonam Tripathi, Priti Ranjan Behera, S. K. Srivastava, Partha Sarathi Roy, Praveen Thakur, S. P. Agrawal, Y. V. N. Krishnamurthy
JOURNAL OF EARTH SYSTEM SCIENCE
(2018)
Article
Environmental Sciences
Rajendra Mohan Panda, Mukunda Dev Behera, Partha Sarathi Roy
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2018)
Article
Environmental Sciences
M. D. Behera, P. Tripathi, P. Das, S. K. Srivastava, P. S. Roy, C. Joshi, P. R. Behera, J. Deka, P. Kumar, M. L. Khan, O. P. Tripathi, T. Dash, Y. V. N. Krishnamurthy
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2018)
Article
Computer Science, Information Systems
Sunitha Koneti, Lakshmi Sunkara, Parth Sarathi Roy
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2018)
Article
Environmental Sciences
Sandy P. Harrison, I. Colin Prentice, Keith J. Bloomfield, Ning Dong, Matthias Forkel, Matthew Forrest, Ramesh K. Ningthoujam, Adam Pellegrini, Yicheng Shen, Mara Baudena, Anabelle W. Cardoso, Jessica C. Huss, Jaideep Joshi, Imma Oliveras, Juli G. Pausas, Kimberley J. Simpson
Summary: Recent extreme wildfires are often linked to hot, dry conditions worsened by climate change. Despite much research on fire weather drivers, understanding natural wildfire regimes and their interactions with human activities remains limited. An ecosystem-centered approach is proposed to enhance comprehension and modeling of wildfires, emphasizing the importance of considering fire ecology in future projections in a changing climate.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Environmental Sciences
Preet Lal, Amit Kumar, Alisha Prasad, Shubham Kumar, Purabi Saikia, Arun Dayanandan, Parth Sarathi Roy, Mohammed Latif Khan
Summary: The study found that while the COVID-19 high and very high-risk primarily concentrated in limited areas, the proportion of high to very high-risk was evident in larger regions. Most parts of India exhibited moderate to very high socio-economic vulnerability, with districts containing megacities being severely affected due to complex urban and social systems.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Beependra Singh, Chockalingam Jeganathan, Virendra Singh Rathore, Mukunda Dev Behera, Chandra Prakash Singh, Parth Sarathi Roy, Peter M. Atkinson
Summary: This study conducted an analysis of the spatio-temporal pattern of natural vegetation and the impact of rainfall on forest growth in central India using satellite remote sensing data. The research found that most forested areas in Central India have shown a greening trend in recent years, with significant impacts observed in certain years of drought and surplus rainfall.
Article
Environmental Sciences
Swapna Mahanand, Mukunda Dev Behera, Partha Sarathi Roy
Summary: The spectral information derived from satellite data is important for assessing plant diversity. This study examined the suitability of satellite-derived biophysical proxies for assessing and monitoring plant diversity in different biogeographic regions of India. The results showed that FAPAR is a potentially essential biodiversity variable for rapid assessment of plant diversity in these regions.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Environmental Sciences
Sujit M. Ghosh, Mukunda D. Behera, Subham Kumar, Pulakesh Das, Ambadipudi J. Prakash, Prasad K. Bhaskaran, Parth S. Roy, Saroj K. Barik, Chockalingam Jeganathan, Prashant K. Srivastava, Soumit K. Behera
Summary: This study presents a novel approach using random forest to estimate forest canopy height in India. The results show that the majority of India's forests have canopy heights between 10-20 meters, indicating mature forest vegetation. This study highlights the importance of using GEDI data to assess canopy height, particularly in data-deficit mountainous regions.
