Comparison of two algorithms for estimating stand-level changes and change indicators in a boreal forest in Norway

Jutras-Perreault, Marie-Claude; Gobakken, Terje; Ørka, Hans Ole

doi:10.1016/j.jag.2021.102316

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…We used the temporal segmentation algorithm LandTrendr [ 50 ], implemented with Google Earth Engine [ 51 ]. LandTrendr has been widely used and shows good performance in similar environments [ 52 ]. A disturbance map was created based on the ALS tessellation and recording the year of disturbance.…”

Section: Methodsmentioning

confidence: 99%

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

Strîmbu

Næsset

Ørka

et al. 2023

Carbon Balance Manage

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Background Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is “invisible” to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data. Results ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km2) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha−1 yr−1. The biomass carbon change was 0.405 (0.13) Mg ha−1 yr−1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha−1 yr−1, and the change in SO carbon was − 0.01 (0.003) Mg ha−1 yr−1. Conclusions Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach.

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Section: Methodsmentioning

confidence: 99%

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

Strîmbu

Næsset

Ørka

et al. 2023

Carbon Balance Manage

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“…Satellite image quality is an important factor in forest disturbance monitoring. Landsat imagery has been used extensively for forest disturbance monitoring with the LandTrendr algorithm [11,[52][53][54] because it has a long-term observation history [2]. However, the long-term monitoring of forest disturbances in the tropics with Landsat alone remains challenging due to frequent cloud cover [55].…”

Section: Data and Algorithms For Forest Disturbance Monitoringmentioning

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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“…At present, the Global Forest Change (GFC) product including both forest loss and gain in 2001-2020 was available for providing forest loss and gain data covering the entirety of China; however, this product has not been specifically validated based on local forest state data [43]. Based on multiple data comparisons, several previous studies have also indicated that the GFC product significantly underestimated the forest loss and gain area at stand [44], national [19,45], and global scales [46]. In addition, this product only covered recent years and missed the 1980-2000 period when China has experienced stronger disturbance and initializations of several afforestation projects [16].…”

Section: Introductionmentioning

confidence: 99%

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

et al. 2022

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China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations.

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Comparison of two algorithms for estimating stand-level changes and change indicators in a boreal forest in Norway

Cited by 4 publications

References 17 publications

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

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

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

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