Figures (2)  Tables (7)

    • Figure 1. 

      Distribution of major forest vegetation in China's Taiwan region[4].

    • Figure 2. 

      Integrated three-layer smart-forestry framework with operational and calibration feedback.

    • Region Dominant trajectory Strength Constraint/transferable lesson
      Europe and North America Sequential: Plot inventories → remote sensing/LiDAR → AI and operational services Long validation heritage Interoperability and user uptake remain uneven; retain plot-based calibration[6,12,13]
      Japan and South Korea Policy- and sector-led integration of machinery, remote sensing, and disaster analytics Public-program alignment Terrain and workforce constraints favor targeted automation and shared infrastructure[14,15]
      Southeast Asia Project-based national inventories and remote-sensing platforms Rapid coverage of heterogeneous forests Connectivity and institutional capacity require open tools and regional training[16]
      China's Taiwan region Parallel pilots of satellite/NTN IoT, UAVs, and AI Fast functional complementarity Cloud, relief, fragmented data governance, and limited field benchmarks require staged validation
      Patterns are nonexclusive interpretive syntheses, not rankings.

      Table 1. 

      Interpretive comparison of regional smart forestry deployment patterns.

    • Case Pathway and inclusion basis Evidence used Enterprise internal test indicators Validation boundary
      A AI/natural asset carbon software; documented partnerships Public corporate/partner/
      policy + EID-A      		 Accuracy/F1; processing; coverage; carbon error; cost No independent audit; market share and MRV compliance were unverified
      B Third-generation partnership project (3GPP) NTN/IoT connectivity; institutional pilot Public corporate/partner/
      government + EID-B      		 Accuracy/F1; latency; sensor coverage; cost; staffing No common-site replication; interoperability unverified
      C Fixed-wing composite UAVs; mature commercial hardware Public corporate/
      certification + EID-C      		 Accuracy/F1; area per flight; efficiency; carbon error; cost No common-site replication or independent audit
      EID-A, EID-B, and EID-C denote confidential enterprise-provided internal datasets for Cases A–C. Aggregated results are reported; proprietary underlying records are withheld. A coded source register can be supplied to the editor under confidentiality.

      Table 2. 

      Case selection and evidence register.

    • MetricCase A: AI classificationCase B: IoT monitoringCase C: UAV survey
      Primary taskVegetation species classificationEnvironmental anomaly detectionAerial forest mapping
      Measured overall accuracy88.0%–93.2%89.5%–92.5%86.2%–91.7%
      Measured F1 score0.910.860.90
      Processing / transmission speed2.4 frames/s (satellite imagery)15-s sensor-to-cloud latency120 ha/flight hour
      Coverage per unit500 ha/satellite scene200 ha/sensor-node cluster120 ha/fixed-wing flight
      Enterprise-reported improvement+38% accuracy vs. traditional remote sensing72% lower data latency vs. manual survey8.5× survey efficiency vs. ground survey
      Observed operational limitation25% cloud-affected data lossSensor-node battery degradationMountain flight restrictions
      Source: Confidential internal data from the enterprises, EID-A, EID-B, and EID-C, were supplied to the authors. Values are enterprise-reported field test results and were not independently audited; the protocols were not standardized across cases. These indicators are descriptive and noncomparative; they were produced under different test conditions and should not be read as a direct performance ranking among the three pathways.

      Table 3. 

      Enterprise-provided field test performance results for the three cases.

    • Performance dimensionCase A: AI + satelliteCase B: IoT networkCase C: UAV surveyConventional ground survey
      Initial survey/deployment3–5 days1–2 days8–12 days30–45 days
      Area coverage (ha/day)200–330NA, continuous80–12522–33
      Tree species accuracy93.2%N/A91.7%95.0% (limited sample)
      Carbon stock error± 8.5%N/A± 6.2%± 5.0% (ground truth)
      Real-time monitoringNo: batchYes: continuousNo: campaignNo
      Wildfire-detection latencyHours to days< 15 minHours: post-flightHours to days
      Annual cost per 1,000 ha (USD)18,000–25,00035,000–50,000 first year; 12,000–18,000 thereafter22,000–30,00045,000–60,000
      Personnel2–3 technicians1–2 technicians3–4 operators6–8 surveyors
      Cloud/weather impactHigh: 25% data lossNone for ground sensorsModerate: flight restrictionsNone
      Source: EID-A–EID-C. Enterprise comparisons used a nominal 1,000-ha deployment basis; the conventional baselines were supplied with the internal data. Results were not produced by one common-site trial and should not be interpreted as inferential rankings. The conventional ground survey baseline represents typical industry-reported ranges for subtropical montane terrain; it was not measured in a trial that was co-located with the three cases. Cross-column comparisons are descriptive only; the four columns draw on different data sources and test conditions.

      Table 4. 

      Enterprise-provided operational results for nominal 1,000-ha deployment comparisons.

    • DimensionCase A: AI platformCase B: NTN IoTCase C: UAV platform
      Data acquisitionSatellite/UAV/ground inputsFixed environmental sensorsUAV-borne imaging and LiDAR
      ConnectivityCloud data ingestionLong Range (LoRa)-type local links + terrestrial/NTN backhaulCommand, control, and post-flight transfer
      AnalyticsClassification, change, carbon estimationRules/cloud anomaly detectionPhotogrammetry; optional downstream AI
      Decision supportMaps and candidate reportsNear-real-time alertsHigh-resolution survey products
      Governance integrationPotential data platformPilot interfaces across institutionsProcurement and aviation permitting
      Potential MRV inputMonitoring/estimation input onlyEnvironmental evidence inputCalibration/validation evidence input
      Performance evidenceEnterprise internal field tests; no independent auditEnterprise internal field tests;
      no common-site replication      		Enterprise internal field tests;
      no independent audit
      Author-judged TRL5–66–78–9 for base hardware; 6–7 for integrated forestry use
      TRL is an author interpretation based on deployment evidence, not a measured outcome; it is analytically separate from the enterprise-reported results in Tables 3−4.

      Table 5. 

      Cross-case analytical matrix and evidence status.

    • Case Strength/opportunity Weakness/threat Mitigation path
      A Scalable analytics; demand for credible carbon information Black box risk; unverified methodology compliance Ground plots, uncertainty reporting, data lineage, independent verification, open export
      B Persistent remote connectivity; event warning Single-provider, battery, and authorization risk Multioperator 3GPP NTN; terrestrial/LoRa store-and-forward; domestic systems where authorized; replaceable power
      C Endurance and flexible payloads; precision mapping Composite supply, operator cost, flight restrictions Dual/local composite sourcing; modular payload bays and connectors; repairable spares; documented safety case
      Cross-cutting Multilayer fusion and policy demand Data silos, surveillance,
      platform lock-in      		 Open application programming interfaces/portability; role-based access; community participation; procurement exit clauses

      Table 6. 

      SWOT characterization and targeted mitigation.

    • Layer/issue Testable proposition (not a standard) Validation design Linked action
      Satellite observation H1: ≥ 1 cloud-usable optical or SAR observation every ≤ 10 days in May–October Log usable scenes by elevation and forest type Shared multisensor tasking and cross-agency catalog
      UAV calibration H2: Ground-sampling distance ≤ 10 cm and horizontal RMSE ≤ 20 cm Independent checkpoints under slope/
      canopy strata      		 Shared service subsidy; common survey and flight safety protocol
      IoT alerting H3: ≥ 95% of priority alerts delivered within 15 min; monthly uptime ≥ 95% Timestamped logs across monsoon and typhoon conditions Open protocols; multi-operator NTN; store-and-forward fallback
      AI/carbon estimation H4: relative RMSE ≤ 20%; bias and 95% uncertainty interval reported Independent plots across elevations and forest types Regional standard group; IPCC-aligned QA/QC; third-party verification
      Governance Metadata, audit logs, role-based access, and API conformance Interoperability, security, and user audits FNCA–local bureau data protocol; operator subsidies; community governance
      H1–H4 are deliberately falsifiable independent-validation hypotheses, not adopted regulatory thresholds. The 25% cloud-affected data loss in Table 3 is retained only as an internal field-test result from Case A; it is not attributed to the FNCA or generalized to the region.

      Table 7. 

      Proposed independent validation benchmarks and linked governance actions.