Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery

Du, Zhenrong; Yu, Le; Yang, Jianyu; Coomes, David A.; Kanniah, Kasturi Devi; Fu, Haohuan; Gong, Peng

doi:10.1109/jstars.2023.3267796

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…We, therefore, used the yearly mean forest cover possibility map based on all available forest cover possibility maps in 2020. Assuming that the mean yearly mean forest cover possibility map is FP, the threshold T used to segment FP into a forest cover map by using the stable forest samples can be expressed as [38,39]:…”

Section: Fused Forest Cover Map Productionmentioning

confidence: 99%

Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility

Wang

Zhang

2023

Forests

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Given the increasingly fragmented forest landscapes, it is necessary to map forest cover with fine spatial resolution in a large area. The European Space Agency (ESA) released the 10 m global land cover map in 2020 based on Sentinel-1 and Sentinel-2 images, and Dynamic World provides near real-time possibilities of many land cover classes based on Sentinel-2 images, but they are not designed particularly for forest cover. In this research, we aimed to develop a method to automatically estimate an accurate 10 m forest cover map in 2020 by fusing the ESA forest cover map and Dynamic World near real-time forest cover possibilities. The proposed method includes three main steps: (1) generating stable forest samples, (2) determining the threshold T and (3) producing the fused forest cover map. China’s Han River Basin, dominated by complex subtropical forests, was used as the study site to validate the performance of the proposed method. The results show that the proposed method could produce a forest cover map with the best overall accuracy of 98.02% ± 1.20% and more accurate spatial details compared to using only one of the two data sources. The proposed method is thus superior in mapping forest cover in complex forest landscapes.

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Section: Fused Forest Cover Map Productionmentioning

confidence: 99%

Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility

Wang

Zhang

2023

Forests

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show abstract

“…According to the results, forest area decreased from 1990 to 2010, then increased from 2013 to 2017 (Tariq et al, 2023). Du et al (2023) was developed the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm for mapping annual forest gain globally using Landsat time series and the Google Earth Engine platform (Du et al, 2023). In the Central European region, Landsat time series to map forest disturbances in five sites across Austria, the Czech Republic, Germany, Poland, and Slovakia, in which disturbance maps achieved overall accuracies ranging from 81% to 93% (Senf et al, 2017).…”

Section: Introductionmentioning

confidence: 98%

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

Tran,

Trinh,

Vambol

et al. 2024

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Dak Lak is a province in the Central Highlands region of Vietnam, with a large area of forests and forestry land. However, the forest cover has changed dramatically in recent times due to the influence of human activities and climate change. This article presents the results of assessing forest cover changes in Dak Lak province from Landsat satellite image data for the period 2000 – 2020. Three Landsat satellite image scenes, including Landsat 5 TM images taken in March 2000 and February 2010 and Landsat 8 OLI image taken in February 2020 are used to classify forest cover. Three common machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and the traditional maximum likelihood classification algorithm are used to classify forest cover in the study area, thereby choosing the method with the highest accuracy. The results show that the RF algorithm has the highest accuracy in classifying forest cover from multi-temporal Landsat images by comparing the overall accuracy value and the Kappa coefficient. The obtained results are used to build forest cover change maps in the period 2000 - 2010, 2010 - 2020 and 2000 - 2020. The results received in the study provide information to help managers in monitoring and protecting forest resources.

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A global annual fractional tree cover dataset during 2000–2021 generated from realigned MODIS seasonal data

Liu,

Chen

et al. 2024

Sci Data

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Fractional tree cover facilitates the depiction of forest density and its changes. However, it remains challenging to estimate tree cover from satellite data, leading to substantial uncertainties in forest cover changes analysis. This paper generated a global annual fractional tree cover dataset from 2000 to 2021 with 250 m resolution (GLOBMAP FTC). MODIS annual observations were realigned at the pixel level to a common phenology and used to extract twelve features that can differentiate between trees and herbaceous vegetation, which greatly reduced feature dimensionality. A massive training data, consisting of 465.88 million sample points from four high-resolution global forest cover products, was collected to train a feedforward neural network model to predict tree cover. Compared with the validation datasets derived from the USGS circa 2010 global land cover reference dataset, the R2 value, MAE, and RMSE were 0.73, 10.55%, and 17.98%, respectively. This dataset can be applied for assessment of forest cover changes, including both abrupt forest loss and gradual forest gain.

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Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery

Cited by 5 publications

References 54 publications

Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility

Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

A global annual fractional tree cover dataset during 2000–2021 generated from realigned MODIS seasonal data

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