Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

Potapov, Peter; Hansen, Matthew C.; Kommareddy, Indrani; Kommareddy, Anil; Turubanova, Svetlana; Pickens, Amy; Adusei, Bernard; Tyukavina, Alexandra; Ying, Qing

doi:10.3390/rs12030426

Cited by 189 publications

(147 citation statements)

References 39 publications

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“…Enhanced and novel LTS-based change detection algorithms are being explored in the FIA RS community (e.g., [256]), and opportunities for using data from other sensors, such as those from the European Union's Sentinel mission, with FIA data are in the knowledge discovery phase [162]. Analysis-ready LTS data are being created with new processing tools, making them more usable for operational monitoring [257]. It is likely that adopting LTS products will result in an increase of time series data use in mapping not only of change, but also of other FIA attributes (e.g., Wilson et al [165]), and this will only become easier as more cloud-based options for computing and storage emerge.…”

Section: Rs Imagery Time Seriesmentioning

confidence: 99%

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Lister

Andersen

Frescino

et al. 2020

Forests

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Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

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Section: Rs Imagery Time Seriesmentioning

confidence: 99%

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Lister

Andersen

Frescino

et al. 2020

Forests

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“…Satellite images for sites #1, #3, #5, #7-10 were used for training and #2, #4, #6 for validation. Corresponding batches of mask data had shape (20,256,256,1). The network training assessment was performed on sub-images generated from image #2 (Table 1).…”

Section: Training and Validation Datamentioning

confidence: 99%

Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning

Kislov¹,

Korznikov²

2020

Remote Sensing

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Wind disturbances are significant phenomena in forest spatial structure and succession dynamics. They cause changes in biodiversity, impact on forest ecosystems at different spatial scales, and have a strong influence on economics and human beings. The reliable recognition and mapping of windthrow areas are of high importance from the perspective of forest management and nature conservation. Recent research in artificial intelligence and computer vision has demonstrated the incredible potential of neural networks in addressing image classification problems. The most efficient algorithms are based on artificial neural networks of nested and complex architecture (e.g., convolutional neural networks (CNNs)), which are usually referred to by a common term—deep learning. Deep learning provides powerful algorithms for the precise segmentation of remote sensing data. We developed an algorithm based on a U-Net-like CNN, which was trained to recognize windthrow areas in Kunashir Island, Russia. We used satellite imagery of very-high spatial resolution (0.5 m/pixel) as source data. We performed a grid search among 216 parameter combinations defining different U-Net-like architectures. The best parameter combination allowed us to achieve an overall accuracy for recognition of windthrow sites of up to 94% for forested landscapes by coniferous and mixed coniferous forests. We found that the false-positive decisions of our algorithm correspond to either seashore logs, which may look similar to fallen tree trunks, or leafless forest stands. While the former can be rectified by applying a forest mask, the latter requires the usage of additional information, which is not always provided by satellite imagery.

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“…Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD, Potapov et al 2020) were used as a source of yearly cloud-free Landsat mosaics. For comparison with the Sentinel-2 data, we selected mosaics from the same two points in time (i.e.…”

Section: Landsatmentioning

confidence: 99%

“…Each composite represents the average of reflectance values between the 25 th and 75 th percentile from the gap-filled cloud-free annual observation time-series. Specific details of the processing steps involved in the production of GLAD ARD data can be found in Potapov et al (2020). As for the Sentinel-2 data, the bands' values corresponding to each NFI plot centre were extracted.…”

Section: Landsatmentioning

confidence: 99%

Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data

Puliti

Hauglin

Breidenbach

et al. 2020

Remote Sensing of Environment

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This study aimed at estimating total forest above-ground net change (ΔAGB; Mt) over five years (2014-2019) based on model-assisted estimation utilizing freely available satellite imagery. The study was conducted for a boreal forest area (approx. 1.4 Mill hectares) in Norway where bitemporal national forest inventory (NFI), Sentinel-2, and Landsat data were available. Biomass change was modelled based on a direct approach. The precision of estimates using only the NFI data in a basic expansion estimator were compared to four different alternative model-assisted estimates using 1) Sentinel-2 or Landsat data, and 2) using bi-or uni-temporal remotely sensed data. We found that the use of remotely sensed data improved the precision of the purely field-based estimates by a factor of up to three. The most precise estimates were found for the model-assisted estimation using bi-temporal Sentinel-2 (standard error; SE= 1.7 Mt). However, the decrease in precision when using Landsat data was small (SE= 1.92 Mt). In addition, we found that ΔAGB could be precisely estimated also when remotely sensed data were available only at the end of the monitoring period. We conclude that satellite optical data can considerably improve ΔAGB estimates, even in those cases where repeated and coincident NFI data are available. The free availability, global coverage, frequent update, and long-term time horizon make data from programs such as Sentinel-2 and Landsat a valuable data source for a consistent and durable monitoring of forest carbon dynamics.

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Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

Cited by 189 publications

References 39 publications

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning

Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data

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