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Papers by Nyenshu Seb Rengma

Environmental monitoring and assessment, May 22, 2024

Research Square (Research Square), Mar 5, 2024

Environmental Monitoring and Assessment, Jul 25, 2023

Research Square (Research Square), May 10, 2023

Soil physico-chemical properties in uence ecosystem services and subsequently human's lives, ther... more Soil physico-chemical properties in uence ecosystem services and subsequently human's lives, therefore soil information is crucial for promoting sustainable land use and ensuring the long-term health and productivity of soils. In environmentally vulnerable regions like the Himalayas, where rapid socioeconomic development is seen and expected to grow, it is imperative to precisely map the soil information in the landscape to protect and manage it sustainably. The demand for applying arti cial intelligence to automate a variety of tasks for its ability to learn and analyze large datasets has enabled the applications of different machine learning methods for digital soil mapping (DSM) approach. Despite the growing number of ML algorithms used in DSM, no studies have used preprocessing technique like resampling for soil datasets for supervised ML regression model. The main objective of this study is the mapping and analyses of soil texture and organic carbon mapping using a random forest regression (RFR) model of an area in the mid-Himalayas by employing more than 100 environmental covariates. The study uses gaussian noise up-sampling technique to resample the small imbalanced soil datasets from the highly undulating terrain, resulting in signi cantly accurate maps. Model performances, evaluated against an unknown dataset were signi cant with an R-square of 0.80, 0.79, 0.72, and 0.84 for clay, sand, silt, and SOC, respectively, and their respective mean absolute error and root mean square error are reported. Further, sensitivity analysis of the environmental covariates contributing to the model resulted in effective contribution of all the soil forming factors.

Renewable Energy Focus, Sep 1, 2023

Research Square (Research Square), Aug 15, 2023

Land use and land cover (LULC) analysis is highly signi cant for various environmental and social... more Land use and land cover (LULC) analysis is highly signi cant for various environmental and social applications. As remote sensing (RS) data becomes more accessible, LULC benchmark datasets have emerged as powerful tools for complex image classi cation tasks. These datasets are used to test stateof-the-art arti cial intelligence models, particularly convolutional neural networks (CNNs), which have demonstrated remarkable effectiveness in such tasks. Nonetheless, there are existing limitations, one of which is the scarcity of benchmark datasets from diverse settings, including those speci cally pertaining to the Indian scenario. This study addresses these challenges by generating medium-sized benchmark LULC datasets from two Indian states and evaluating state-of-the-art CNN models alongside traditional ML models. The evaluation focuses on achieving high accuracy in LULC classi cation, speci cally on the generated patches of LULC classes. The dataset comprises 4000 labelled images derived from Sentinel-2 satellite imagery, encompassing three visible spectral bands and four distinct LULC classes. Through quantitative experimental comparison, the study demonstrates that ML models outperform CNN models, exhibiting superior performance across various LULC classes with unique characteristics. Notably, using a traditional ML model, the proposed novel dataset achieves an impressive overall classi cation accuracy of 96.57%. This study contributes by introducing a standardized benchmark dataset and highlighting the comparative performance of deep CNNs and traditional ML models in the eld of LULC classi cation.

2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), Dec 6, 2021

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jan 12, 2023

In the realm of data analytics, machine learning (ML) is one of the most successful techniques fo... more In the realm of data analytics, machine learning (ML) is one of the most successful techniques for making predictions. The ability of ML algorithms has also been studied in various aspects of land surface temperature (LST) besides its retrieval. The few investigations on LST retrieval using ML algorithms suggested that it may potentially obtain the LST values incorporating relevant variables of land surface parameters; however, the variables and ML models used differ, and so do their accuracies. The accuracy of the model is affected by the variable's contribution, its quality and quantity, and the fulfilment of each technique's assumptions. Hence this study provides a wide range of LST indicators to be employed for LST retrieval using a widely used ML algorithm, random forest. The ML algorithm framework for LST prediction is illustrated with significant spectral indices and terrain parameters across the highly industrialised district of Jharkhand, India. With the exception of one (aspect) variable, the analysis shows that all 20 variables that were included as independent factors were significant and equally contributed to the model. The model built with all the variables including the aspect of the terrain obtained an RMSE of 1.13 degree Celsius and R 2 of 0.48. However, after the removal of aspect, the model obtained an R 2 of 0.89 and RMSE of 0.74º C. The performance of the model on consecutive removal of lesser significant variables are evaluated and the study made clear how crucial it is to consider several environmental or land-use factors that could be pertinent to LST.

Land use and land cover (LULC) analysis is highly significant for various environmental and socia... more Land use and land cover (LULC) analysis is highly significant for various environmental and social applications. As remote sensing (RS) data becomes more accessible, LULC benchmark datasets have emerged as powerful tools for complex image classification tasks. These datasets are used to test state-of-the-art artificial intelligence models, particularly convolutional neural networks (CNNs), which have demonstrated remarkable effectiveness in such tasks. Nonetheless, there are existing limitations, one of which is the scarcity of benchmark datasets from diverse settings, including those specifically pertaining to the Indian scenario. This study addresses these challenges by generating medium-sized benchmark LULC datasets from two Indian states and evaluating state-of-the-art CNN models alongside traditional ML models. The evaluation focuses on achieving high accuracy in LULC classification, specifically on the generated patches of LULC classes. The dataset comprises 4000 labelled imag...

Environmental Monitoring and Assessment

Soil physico-chemical properties influence ecosystem services and subsequently human’s lives, the... more Soil physico-chemical properties influence ecosystem services and subsequently human’s lives, therefore soil information is crucial for promoting sustainable land use and ensuring the long-term health and productivity of soils. In environmentally vulnerable regions like the Himalayas, where rapid socio-economic development is seen and expected to grow, it is imperative to precisely map the soil information in the landscape to protect and manage it sustainably. The demand for applying artificial intelligence to automate a variety of tasks for its ability to learn and analyze large datasets has enabled the applications of different machine learning methods for digital soil mapping (DSM) approach. Despite the growing number of ML algorithms used in DSM, no studies have used preprocessing technique like resampling for soil datasets for supervised ML regression model. The main objective of this study is the mapping and analyses of soil texture and organic carbon mapping using a random fo...

2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS)