Earth Observation Applications in the Hindu Kush Himalaya Region—Evolution and Adoptions

Bajracharya, Birendra; Irwin, Daniel; Thapa, Rajesh; Matin, Mir A.

doi:10.1007/978-3-030-73569-2_1

Cited by 5 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The HKH region extends over 3500 km, encompassing Myanmar in the East and reaching to Afghanistan in the West (dark blue region in Figure 1). With its high variability of topography, the region hosts ten large rivers including Mekong, Yangtse, Yellow River, Salween, Irrawady, Brahmaputra, Ganges, Tarim Indus, and Amu Darya, making large areas of Afghanistan, Bangladesh, India, Nepal, and Pakistan susceptible to inundation by floodwaters during every rainy season [2]. Among these countries, Bangladesh has been experiencing record-high floods occurring almost every year due to Bangladesh's unique geographical setting as the most downstream country in the HKH [16].…”

Section: Flood Mapping Needs In the Hkhmentioning

confidence: 99%

“…Floods can persist for several weeks in some regions of the country. Rapid change during the recent global warming period has altered meteorological and hydrological extreme events in the HKH, giving rise to a further increase in the frequency of flood hazards [2,8]. Today, large areas of Bangladesh, Bhutan, India, Nepal, and Pakistan are subject to frequent extreme weather events, creating a hazard environment where floods can damage infrastructure, and threaten livelihoods.…”

Section: Flood Mapping Needs In the Hkhmentioning

confidence: 99%

“…For example, the 2019 monsoon season affected 7.3 million people, caused 119 fatalities, and displaced an estimated 308,000 people in Bangladesh alone. The same event additionally caused floods and landslides across Nepal and displaced more than 1.8 million people in the northern reaches of India [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

Meyer,

Schultz,

Osmanoglu

et al. 2024

Preprint

View full text Add to dashboard Cite

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in-situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service that was co-developed with in-region partners to provide year-round, low-latency weather-hazards information across the HKH. The paper describes the end-user focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which is providing capability to scale automatically with event size. A performance assessment of our quantitative algorithms is described, demonstrating capabilities to map flood extent and water depth at an accuracy of >90% and

show abstract

Section: Flood Mapping Needs In the Hkhmentioning

confidence: 99%

Section: Flood Mapping Needs In the Hkhmentioning

confidence: 99%

See 1 more Smart Citation

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

Meyer,

Schultz,

Osmanoglu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, deep learning methods are highly suitable for forest fire identification [28][29][30][31][32][33]. However, in practical applications, most deep learning methods suffer from low accuracy in identifying forest fires.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT

et al. 2023

View full text Add to dashboard Cite

Monitoring and early warning technology for forest fires is crucial. An early warning/monitoring system for forest fires was constructed based on deep learning and the internet of things. Forest fire recognition was improved by combining the size, color, and shape characteristics of the flame, smoke, and area. Complex upper-layer fire-image features were extracted, improving the input conversion by building a forest fire risk prediction model based on an improved dynamic convolutional neural network. The proposed back propagation neural network fire (BPNNFire) algorithm calculated the image processing speed and delay rate, and data were preprocessed to remove noise. The model recognized forest fire images, and the classifier classified them to distinguish images with and without fire. Fire images were classified locally for feature extraction. Forest fire images were stored on a remote server. Existing algorithms were compared, and BPNNFire provided real-time accurate forest fire recognition at a low frame rate with 84.37% accuracy, indicating superior recognition. The maximum relative error between the measured and actual values for real-time online monitoring of forest environment indicators, such as air temperature and humidity, was 5.75%. The packet loss rate of the forest fire monitoring network was 5.99% at Longshan Forest Farm and 2.22% at Longyandong Forest Farm.

show abstract

“…Agriculture is a minor land use in terms of spatial extent but the mainstay of local economy fostering conservation or degradation of natural ecosystems depending on policy, socio-economic and ecological conditions. Extensive human induced ecological degradation became apparent after 19 th century with enormous variation in their nature, rates and drivers (Rao et al, 2016;Bajracharya et al, 2021). In Indian Himalayan Region, villages are farm-forest landscape mosaics managed by local communities.…”

mentioning

confidence: 99%

Participatory forest restoration for sustainable livelihood: A case study from Himalaya

Sreenivasa,

Semwal,

Maletha

et al. 2024

Preprint

View full text Add to dashboard Cite

Forest restoration based on the concepts of integrated, adaptive and co-management with active community participation was attempted in a village in the temperate region of Indian Himalayan region. Mixed plantation of multipurpose broad-leaved trees, bamboo and medicinal herbs selected by the inhabitants based on pooled traditional and scientific knowledge was established, with an understanding that funding will be limited to inputs unaffordable by people and will be terminated after economic benefit/cost ratio is > 1. Stakeholders would voluntarily maintain and upscale/outscale it thereafter and analysis of socio-ecological outcomes would continue for at least next 20 years. Accordingly, the funding was terminated after 7 years when economic benefit/cost ratio crossed the mark of one. Voluntary planting of Alnus nepalensis in gaps arising from gregarious flowering induced mass mortality of bamboo two years after cessation of funding maintained progression of recovery. After 20 years, the restored area had 17% of aboveground and 75% of belowground carbon stocks, and 39% of flowering species as compared to intact forests. Further, restored forest after 20 years was monetarily the most efficient land use even though there were no payments for its regulating ecosystem services. People maintained but did not upscale/outscale the forest restoration trial as also cultivation of medicinal herbs having high biodiversity conservation on farm land triggered by it. In the study village abandonment of cropping in 39% of private farm area and leasing 24% of it to a pharmaceutical company were the land use changes between 8th and 20th year of restoration. Nullification of carbon sequestration by forest restoration by emission from agricultural abandonment resulted in no net sequestration at landscape (village) scale. Land use/cover changes resulted in change in flowering plant species diversity at ecosystem but not at village landscape scale. There is a need of tailoring forest restoration as an activity enhancing traditional knowledge, culture and income of people, ensuring its synergy with other land uses/non-land activities and determining financial support for contribution of farm-forest cultural landscape mosaics to biodiversity conservation and climate change mitigation around biodiversity hotspots in developing countries

show abstract

Earth Observation Applications in the Hindu Kush Himalaya Region—Evolution and Adoptions

Cited by 5 publications

References 26 publications

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT

Participatory forest restoration for sustainable livelihood: A case study from Himalaya

Contact Info

Product

Resources

About