The Road to Operationalization of Effective Tropical Forest Monitoring Systems

Portillo-Quintero, Carlos; Hernández‐Stefanoni, José Luis; Reyes-Palomeque, Gabriela; Subedi, Mukti Ram

doi:10.3390/rs13071370

Cited by 13 publications

(8 citation statements)

References 68 publications

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“…The two primary sensor types are Synthetic Aperture Radar (SAR) and multispectral imaging sensors. The main SAR applications for detecting forest cover loss include the JJFAST algorithm ('JICA-JAXA Forest Early Warning System in the Tropics') [10] from the Advanced Land Observation Satellite (ALOS-2) Phased Array L-band Synthetic Aperture Radar (PALSAR-2) and the RAdar for Detecting Deforestation (RADD) alerts [11,12] based on Sentinel-1, a C-band radar satellite constellation by the European Space Agency under the Copernicus Programme. Multispectral forest cover loss applications include the Global Land Analysis and Discovery (GLAD) alert system by Global Forest Watch [7] based on Hansen et al's [13] global tree cover change monitoring method at 30 m resolution, and the Sentinel-2-based Python for Earth Observation (PyEO) forest alert system [14], which was developed by the UK National Centre for Earth Observation at the University of Leicester together with the Kenya Forest Service and the REDD+ Stakeholder Round Table in Kenya.…”

Section: Introductionmentioning

confidence: 99%

Due Diligence for Deforestation-Free Supply Chains with Copernicus Sentinel-2 Imagery and Machine Learning

Reading,

Bika,

Drakesmith

et al. 2024

Forests

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At COP26, the Glasgow Leaders Declaration committed to ending deforestation by 2030. Implementing deforestation-free supply chains is of growing importance to importers and exporters but challenging due to the complexity of supply chains for agricultural commodities which are driving tropical deforestation. Monitoring tools are needed that alert companies of forest losses around their source farms. ForestMind has developed compliance monitoring tools for deforestation-free supply chains. The system delivers reports to companies based on automated satellite image analysis of forest loss around farms. We describe an algorithm based on the Python for Earth Observation (PyEO) package to deliver near-real-time forest alerts from Sentinel-2 imagery and machine learning. A Forest Analyst interprets the multi-layer raster analyst report and creates company reports for monitoring supply chains. We conclude that the ForestMind extension of PyEO with its hybrid change detection from a random forest model and NDVI differencing produces actionable farm-scale reports in support of the EU Deforestation Regulation. The user accuracy of the random forest model was 96.5% in Guatemala and 93.5% in Brazil. The system provides operational insights into forest loss around source farms in countries from which commodities are imported.

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Section: Introductionmentioning

confidence: 99%

Due Diligence for Deforestation-Free Supply Chains with Copernicus Sentinel-2 Imagery and Machine Learning

Reading,

Bika,

Drakesmith

et al. 2024

Forests

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“…Outputs from indirect monitoring methods, such as maps of forest attributes, provide key advantages compared to traditional surveys by delivering information on hard-to-reach forest areas and territory-wide spatial patterns [13][14][15]. Understanding the spatial patterns of forest attributes is particularly crucial for decision-making in forest management and conservation [16,17] since it enables the identification of biomass and biodiversity hot spots and rare and endangered habitats and species. Identifying and delimiting these key natural values is central to forest conservation, particularly for zoning, defining use constraints, and planning management actions [18].…”

Section: Introductionmentioning

confidence: 99%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

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Understanding the spatial variations of forest features and their underlying drivers is crucial for managing and preserving forest ecosystems. Remote sensing techniques, due to their vast spatial coverage, are proving an essential tool for mapping and analyzing the complex spatial patterns of forests characteristics. In the current study, we use high-resolution Gaofen 1 satellite imagery to map features, such as biomass, biodiversity, and habitat type, of the subtropical forests in the Nanling area, a major biodiversity hot spot in the south of China; for subsequently analyzing the association between forest features and environmental drivers. We based our modeling approach on a semi-supervised fully convolutional neural network consisting of a ResNet architecture and a masking and aggregation module for computing predictions for forest survey plots of varied sizes. The fitted convolutional models yielded very high performance statistics, with test R2 values above 0.90 for basal area, aboveground biomass and Shannon diversity index regressions, slightly lower values for the regression of species richness (R2=0.83), and average test classification accuracy of forest types of 0.89, with lower performance for infrequent forest types, such as dwarf forests. The association analysis between predicted maps of forest features and environmental drivers revealed significant correlations being the strongest drivers the altitude, nitrogen content and climate variables, such as mean annual precipitation and temperature. Altitude was strongly associated with dwarf forests and high basal area and high aboveground biomass locations. Basal area and aboveground biomass were also positively influenced by annual rainfall and soil nitrogen and carbon, and negatively by temperature and radiation variables. Species richness was associated with high nitrogen and carbon contents, while the Shannon diversity index showed overall mild correlations with most drivers, with no remarkable patterns. We concluded that our semi-supervised convolutional approach using Gaofen 1 imagery proved useful for modeling the spatial distribution of the subtropical forests in Nanling; however, more research and data collection is necessary to improve the performance of species richness modeling and segmentation of infrequent forest types. Moreover, the strong associations found between climate, and predicted maps of biomass and forest types, provided key insights into the potential repercussions of climate change on Nanling forests.

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“…Field surveys are a traditional method for the high-precision monitoring of forest disturbances, but they are time consuming, labor intensive, and limited in terms of time and space [9]. In contrast, remote sensing is a practical solution for forest disturbance monitoring because it allows rapid image acquisition, provides large spatial coverage, and is widely used to monitor forest disturbances caused by human-induced (logging [10,11], disforest), natural (fire [12,13], drought [14,15], pests and diseases [10,16]), or unknown factors [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Tropical Forest Disturbance Monitoring Based on Multi-Source Time Series Satellite Images and the LandTrendr Algorithm

Yin

Kou

Yun

et al. 2022

Forests

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Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google Earth Engine (GEE), remote sensing is increasingly used to detect forest disturbances. In this study, three types of forest disturbances (abrupt, gradual, and multiple) were identified since the late 1980s on Hainan Island, the largest tropical island in China, using an improved LandTrendr algorithm and a dense time series of Landsat and Sentinel-2 satellite images on the GEE cloud platform. Results show that: (1) the algorithm identified forest disturbances with high accuracy, with the R2 for abrupt and gradual disturbance detection reaching 0.92 and 0.83, respectively; (2) the total area in which forest disturbances occurred on Hainan Island over the past 30 years accounted for 10.84% (2.33 × 105 hm2 in total area, at 0.35% per year) of the total forest area in 2020 and peaked around 2005; (3) the areas of abrupt, gradual, and multiple disturbances were 1.21 × 105 hm2, 9.96 × 104 hm2, and 1.25 × 104 hm2, accounting for 51.93%, 42.75%, and 5.32% of the total disturbed area, respectively; and (4) most forest disturbance occurred in low-lying (<600 m elevation accounts for 97.42%) and gentle (<25° slope accounts for 94.42%) regions, and were mainly caused by the rapid expansion of rubber, eucalyptus, and tropical fruit plantations and natural disasters such as typhoons and droughts. The resulting algorithm and data products provide effective support for assessments of such things as tropical forest productivity and carbon storage on Hainan Island.

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The Road to Operationalization of Effective Tropical Forest Monitoring Systems

Cited by 13 publications

References 68 publications

Due Diligence for Deforestation-Free Supply Chains with Copernicus Sentinel-2 Imagery and Machine Learning

Due Diligence for Deforestation-Free Supply Chains with Copernicus Sentinel-2 Imagery and Machine Learning

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Tropical Forest Disturbance Monitoring Based on Multi-Source Time Series Satellite Images and the LandTrendr Algorithm

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