Forest Monitoring Using Landsat Time Series Data: A Review

Banskota, Asim; Kayastha, Nilam; Falkowski, Michael J.; Wulder, Michael A.; Froese, Robert E.; White, Joanne C.

doi:10.1080/07038992.2014.987376

Cited by 333 publications

(219 citation statements)

References 139 publications

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“…In addition to selection of suitable variables with dense dates, different algorithms such as VCT, LandTrendt, and BFAST have been developed for detection of forest disturbance and recovery [11,[23][24][25][26]32,33], but their performances vary, depending on many factors such as the composition of forest species and complexity of the landscape under investigation, the variables used, and parameters in the algorithm. For example, several control parameters are used in the LandTrendr algorithm and they need to be optimized.…”

Section: Discussionmentioning

confidence: 99%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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Detection of forest disturbance and recovery has received much attention during the last two decades due to its important influence on forest carbon budget estimation. This research used Landsat time-series data from 1984 to 2015 to examine forest disturbance and recovery in a subtropical region of eastern Zhejiang Province, China, through the LandTrendr algorithm. Field inventory data and high spatial resolution images were used to evaluate the disturbance and recovery results. This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and recovery each can be detected. Through incorporation of climate data and disturbance results, drought events also can be successfully detected. More research is needed to incorporate multisource data for detection of forest disturbance types in subtropical regions.

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Section: Discussionmentioning

confidence: 99%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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“…Furthermore, for many locations on Earth, a higher temporal revisit is needed in order to maximize the chances of getting cloud-free data: the growing cycle of vegetation in agriculture needs finer temporal resolutions than those needed for some existing forest studies [22]. For instance, summer crops, such as sunflower and maize, may be separated thanks to the longer growing period of the maize.…”

Section: Previous Workmentioning

confidence: 99%

Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

et al. 2015

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Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve Remote Sens. 2015, 7 12357 overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.

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“…LTS, which are perhaps most celebrated for characterizing forest dynamics (Wulder et al, 2012), especially disturbances (Banskota et al, 2014), have not yet been used to map microrefugia, but are a logical choice for this application. Microrefugia are defined, in part, by their temporal dynamics, and may be smaller than the pixels of traditional time series instruments such as MODIS or AVHRR but resolvable in 30 m Landsat pixels.…”

Section: Discussionmentioning

confidence: 99%

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Andrew

Warrener

2017

Remote Sensing of Environment

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(in press). Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics. Remote Sensing of Environment. Accepted 4 August 2017. DOI:https: //doi.org/10.1016/j.rse.2017.08.005 Disclaimer:The PDF document is a copy of the final version of this manuscript that was subsequently accepted by the journal for publication. The paper has been through peer review, but it has not been subject to any additional copy-editing or journal specific formatting (so will look different from the final version of record, which may be accessed following the DOI above depending on your access situation). 2 AbstractMicrorefugia are sites with stable, high quality habitat within landscapes characterized by dynamic environmental conditions driven by climate variability or ecological disturbances. There is considerable interest in the potential of microrefugia to provide climate change resilience to landscapes and to biodiversity conservation. Although attractive conceptually, there is yet little guidance on how to identify climate change microrefugia in order to study and protect them, and the data required to do so are often lacking. This study demonstrates how time series remote sensing, using all available Landsat images of a study area, can be used to directly detect microrefugia maintained by water subsidies in a semi-arid landscape in southwest Western Australia.Microrefugia were identified as pixels with abundant vegetation and consistent vegetation dynamics between wet and dry years. At every pixel, a harmonic model was fit to the intra-annual time series of vegetation index values compiled from the wettest years in the Landsat-5 Thematic Mapper (TM) archive. This model was then used to predict the phenological cycle of the driest years at that pixel. Candidate microrefugia were defined to be those pixels with (1) high vegetation activity in dry years and (2) highly predictable phenologies that are consistent regardless of the weather conditions experienced in a given year. Spatial relationships between candidate microrefugia and landscape features associated with elevated moisture availability (thought to drive climate microrefugia in these semi-arid landscapes) were assessed. The candidate microrefugia show great promise. Evaluations against high-resolution imagery reveal that candidate microrefugia most likely buffer against drought, although refugia from other disturbances, especially fire, were also detected. In contrast, spatial proxies of the physical features expected to maintain microrefugia failed to adequately represent the distribution of microrefugia across the landscape, likely due to data quality and the heterogeneity of microrefugia. Direct detection of microrefugia with Earth observation data is a promising solution in data limited regions. Landsat time series analyses are well suited to this application as they can characterize both the habitat quality and stability aspects of microrefugia.

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Forest Monitoring Using Landsat Time Series Data: A Review

Cited by 333 publications

References 139 publications

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

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