Paper Citation Record · LEDGER

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring

As of 20 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.16107.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.16107 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:34:53.254035Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b098bf9-12f4-4b28-95e0-6ebd9486c748 · outbound

This paper cites The latest data confirms: Forest fires are getting worse,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring The latest data confirms: Forest fires are getting worse,

Reference 1

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Observation 8822866b-83bb-47ea-84af-8785fda3e559 · outbound

This paper cites Hyperspec- tral remote sensing of fire: State-of-the-art and future perspectives,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Hyperspec- tral remote sensing of fire: State-of-the-art and future perspectives,

Reference 2

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Observation 33cbc1ce-d2e5-47ca-aae6-d848f107f4f2 · outbound

This paper cites Spectral imaging for remote sensing,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Spectral imaging for remote sensing,

Reference 3

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Observation 52379e6f-3e3c-4d54-aa5d-7cf89afce0cb · outbound

This paper cites Xprize wildfire,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Xprize wildfire,

Reference 4

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Observation 28502759-eded-4fda-8ad0-e542a9a22d6e · outbound

This paper cites Fire terminology,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Fire terminology,

Reference 5

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Source-reported events for the cited work

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Observation c1bf8568-f097-4b52-9b0b-0db105b6b922 · outbound

This paper cites Forest service manual (fsm) directive issuances, series 5000—protection and development,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Forest service manual (fsm) directive issuances, series 5000—protection and development,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.481774Z

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Observation 9fcd3118-b97b-4953-954a-2ad1cda43d26 · outbound

This paper cites Wildfire response performance measurement: current and future directions,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Wildfire response performance measurement: current and future directions,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.472102Z

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Observation 68e7ed98-ee21-4e11-bebd-4d821b632ed3 · outbound

This paper cites Patterns of wildfire risk in the united states from systematic operational risk assessments: How risk is characterised by land managers,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Patterns of wildfire risk in the united states from systematic operational risk assessments: How risk is characterised by land managers,

Reference 8

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Observation 6aaed07e-2299-49e0-9578-5639da4e10a8 · outbound

This paper cites Development of a framework for fire risk assessment using remote sensing and geographic information system technologies,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Development of a framework for fire risk assessment using remote sensing and geographic information system technologies,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.455274Z

Source-reported events for the cited work

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Observation 1382e99d-a3b1-470c-85bd-64c338eb278a · outbound

This paper cites Com- parison of different methods for the in situ measurement of forest litter moisture content,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Com- parison of different methods for the in situ measurement of forest litter moisture content,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.446782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40dd5097-c2a5-4993-a830-1c06e6b09ee6 · outbound

This paper cites Estimating live fuel moisture content from remotely sensed reflectance,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Estimating live fuel moisture content from remotely sensed reflectance,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.438235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0f0793ad-185c-4c03-9826-fb4306c05300 · outbound

This paper cites Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.430229Z

Source-reported events for the cited work

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Observation ad730392-a9d7-49cc-8c97-efe1b8172f36 · outbound

This paper cites Validating the effect of fuel moisture content by a multivalued operator in a simplified physical fire spread model,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Validating the effect of fuel moisture content by a multivalued operator in a simplified physical fire spread model,

Reference 13

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0f359248-3450-4062-8c9b-6b2bd636eaff · outbound

This paper cites Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review,

Reference 14

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Source-reported events for the cited work

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Observation 0106e79c-e491-4b4d-b968-b8fa7b2039a6 · outbound

This paper cites Monitoring vegetation systems in the great plains with erts,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Monitoring vegetation systems in the great plains with erts,

Reference 15

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Observation 4069e354-0cef-4b83-8dab-fa0b6be61366 · outbound

This paper cites Ndwi—a normalized difference water index for remote sensing of vegetation liquid water from space,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Ndwi—a normalized difference water index for remote sensing of vegetation liquid water from space,

Reference 16

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Observation 73e80140-ba6f-4723-97f2-affd1e78e4ff · outbound

This paper cites Close range hyperspectral imaging of plants: A review,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Close range hyperspectral imaging of plants: A review,

Reference 17

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Observation 59cc4895-36cd-451a-a16b-373191b247e5 · outbound

This paper cites Autonomous satellite wildfire detection using hyperspectral imagery and neural networks: A case study on australian wildfire,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Autonomous satellite wildfire detection using hyperspectral imagery and neural networks: A case study on australian wildfire,

Reference 18

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Observation 8b377ba8-70ae-4821-aa94-14bffd4188f9 · outbound

This paper cites Hardware acceleration for real-time wildfire detection onboard drone networks,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Hardware acceleration for real-time wildfire detection onboard drone networks,

Reference 19

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Source-reported events for the cited work

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Observation 4c9038ca-aea9-416c-a3bb-6a02e8600e37 · outbound

This paper cites Early wildfire detection using uavs integrated with air quality and lidar sensors,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Early wildfire detection using uavs integrated with air quality and lidar sensors,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.365070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b05d61a-7b95-47e7-a8d7-fddb10c9341c · outbound

This paper cites A cooperative uav/ugv platform for wildfire detection and fighting,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring A cooperative uav/ugv platform for wildfire detection and fighting,

Reference 21

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Observation 8df64e57-1879-4753-8695-da5b38891ecb · outbound

This paper cites Vast: Visual and spectral terrain classification in unstructured multi-class environments,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Vast: Visual and spectral terrain classification in unstructured multi-class environments,

Reference 22

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Observation c1a99186-bca7-4747-a0b5-bf64e405a517 · outbound

This paper cites Hyper- drive: Visible-short wave infrared hyperspectral imaging datasets for robots in unstructured environments,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Hyper- drive: Visible-short wave infrared hyperspectral imaging datasets for robots in unstructured environments,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.338631Z

Source-reported events for the cited work

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Observation b97336dc-712d-4383-a24d-ccb71aee90e7 · outbound

This paper cites Field calibration of hyperspectral cameras for autonomous terrain inference,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Field calibration of hyperspectral cameras for autonomous terrain inference,

Reference 24

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Observation b84ff33a-78f7-407c-8542-db1eedd07f21 · outbound

This paper cites Szeliski, Computer Vision: Algorithms and Applications , 1st ed.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Szeliski, Computer Vision: Algorithms and Applications , 1st ed

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.321519Z

Source-reported events for the cited work

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Observation ff694e62-9c09-4c4f-809f-088e9540a102 · outbound

This paper cites GitHub - heethesh/lidar camera calibration: Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization) — github.com,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring GitHub - heethesh/lidar camera calibration: Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization) — github.com,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.312876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cf5181e7-a176-4c5c-b8e5-f701be446d68 · outbound

This paper cites Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.304547Z

Source-reported events for the cited work

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Observation 6832b023-9c4c-4dd1-bce7-39c6aca02748 · outbound

This paper cites Quantitative hyperspectral reflectance imaging,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Quantitative hyperspectral reflectance imaging,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.296190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5ab32ff-f66b-4bca-ad84-c4f60bee0b0b · outbound

This paper cites Hyperspectral reflectance imaging for water content and firmness prediction of potatoes by optimum wavelengths,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Hyperspectral reflectance imaging for water content and firmness prediction of potatoes by optimum wavelengths,

Reference 29

Resolution: verified exact; doi, observed 2026-08-12T13:34:53.277965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ce52c2a6-bd34-48cc-817b-b3071a6ecd1b · outbound

This paper cites Classification of hyperspectral reflectance images with physical and statistical criteria,.

Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring Classification of hyperspectral reflectance images with physical and statistical criteria,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:34:53.287922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.