Ecosystem Productivity and Carbon Dynamics in Keibul Lamjao National Park, Manipur, India: A Grey Relational Analysis Perspective DOI Creative Commons

Kambam Boxen Meetei,

Meribeni Tsopoe,

Girish Chandra

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Окт. 14, 2024

Abstract An in-depth understanding of carbon dynamics and ecosystem productivity is essential for conservation management different ecosystems. Ecosystem budget are assessed by estimating Net Production (NEP) across global ecological assessment forest floating meadow ecosystems in Keibul Lamjao National Park (KLNP), Manipur, North East India was conducted using the multi-criteria decision-making process namely, Grey Relational Analysis (GRA). The analysis performed on 24 selected criterions classified either as "higher-the-better" or "lower-the-better" based their degree influence budget. Floating meadows exhibited a higher production aboveground belowground biomass total mortality decay. Furthermore, study found that soil organic (SOC) net matter (SOM) than ecosystem. showed respiration (RT), heterotrophic (RH), autotrophic (RA) meadows. primary (NPP) 616.49 ± 33.87 gCm⁻²yr⁻¹ ecosystem, which has NPP 566.64 65.26 gCm⁻²yr⁻¹. Similarly, have NEP (495.25 36.46 gCm−²yr⁻¹) (418.39 65.76 gCm−²yr⁻¹). These characteristics significant compared to ecosystems, shown larger values Coefficient (GRC) GRA. Meadows (0.82) obtained 54.72% percentage gain GRG value with (0.53). This might help improving KLNP other adjutant areas policies from vital information given importance wetlands productivity.

Язык: Английский

UAV and Deep Learning: Detection of selected riparian species along the Ganga River DOI Creative Commons
Ravindra Nath Tripathi, Aishwarya Ramachandran,

Karan Agarwal

и другие.

˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences, Год журнала: 2024, Номер XLVIII-1-2024, С. 637 - 642

Опубликована: Май 10, 2024

Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health well-being. Riparian zones, that face the highest decline in freshwater biodiversity, of prime conservation priority because they for regulating climate, preserving aquatic-terrestrial maintaining ground water recharge restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is precise answer a wide range problems including biodiversity wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) AI has resulted innovative strides conservation. This study utilised UAV imagery record high-resolution data aquatic habitat species along Ganga River employed deep learning algorithms analyse data. Through extensive field surveys Hastinapur Wildlife Sanctuary, 7,025 photos representing variety environments, 20,000 annotated samples animals such turtles gharials were generated. Vision based computing capabilities pattern recognition model developed identify these species. To enrich enhance dataset model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up 10%), adjustments brightness, saturation, shear (±15%) applied. Controlled blur 0.5%) exposure modifications (±5%) also implemented on improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, v8, Roboflow 3.0, detecting select Results show v8 excels, achieving mean average precision (mAP) 98.8% gharial 92.2% turtle detection, with rapid detection time 0.308 seconds per frame at 3200 × resolution. Additionally, our demonstrates real-time capability through sampling techniques UAVs. methodology provides promising technique collect scientific IUCN red listed critically endangered gharials, allowing monitoring, real counting minimal intrusion. conclusion, fusion UAVs promises revolutionize aiding decision-making.

Язык: Английский

Процитировано

3
Ecosystem productivity and carbon dynamics in Keibul Lamjao National Park, Manipur, India: a gray relational analysis perspective DOI

Kambam Boxen Meetei,

Meribeni Tsopoe,

Girish Chandra

и другие.

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(2)

Опубликована: Янв. 7, 2025

Язык: Английский

Процитировано

0
Conservation in action: Cost-effective UAVs and real-time detection of the globally threatened swamp deer (Rucervus duvaucelii) DOI Creative Commons
Ravindra Nath Tripathi,

Karan Agarwal,

Vikas Tripathi

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102913 - 102913

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

1
Unmanned aerial vehicle (UAV) based measurements DOI
Mozhdeh Shahbazi

Measurement, Год журнала: 2024, Номер 239, С. 115340 - 115340

Опубликована: Июль 17, 2024

Язык: Английский

Процитировано

0
Ecosystem Productivity and Carbon Dynamics in Keibul Lamjao National Park, Manipur, India: A Grey Relational Analysis Perspective DOI Creative Commons

Kambam Boxen Meetei,

Meribeni Tsopoe,

Girish Chandra

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Окт. 14, 2024

Abstract An in-depth understanding of carbon dynamics and ecosystem productivity is essential for conservation management different ecosystems. Ecosystem budget are assessed by estimating Net Production (NEP) across global ecological assessment forest floating meadow ecosystems in Keibul Lamjao National Park (KLNP), Manipur, North East India was conducted using the multi-criteria decision-making process namely, Grey Relational Analysis (GRA). The analysis performed on 24 selected criterions classified either as "higher-the-better" or "lower-the-better" based their degree influence budget. Floating meadows exhibited a higher production aboveground belowground biomass total mortality decay. Furthermore, study found that soil organic (SOC) net matter (SOM) than ecosystem. showed respiration (RT), heterotrophic (RH), autotrophic (RA) meadows. primary (NPP) 616.49 ± 33.87 gCm⁻²yr⁻¹ ecosystem, which has NPP 566.64 65.26 gCm⁻²yr⁻¹. Similarly, have NEP (495.25 36.46 gCm−²yr⁻¹) (418.39 65.76 gCm−²yr⁻¹). These characteristics significant compared to ecosystems, shown larger values Coefficient (GRC) GRA. Meadows (0.82) obtained 54.72% percentage gain GRG value with (0.53). This might help improving KLNP other adjutant areas policies from vital information given importance wetlands productivity.

Язык: Английский

Процитировано

0