Tracking vegetation degradation and recovery in multiple mining areas in Beijing, China, based on time-series Landsat imagery

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

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Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

Remote Sensing ◽

10.3390/rs12183038 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3038

Author(s):

Dhahi Al-Shammari ◽

Ignacio Fuentes ◽

Brett M. Whelan ◽

Patrick Filippi ◽

Thomas F. A. Bishop

Keyword(s):

Time Series ◽

Vegetation Index ◽

Landsat Imagery ◽

Google Earth ◽

Growth Stages ◽

Landsat 8 ◽

Crop Type ◽

Eastern Australia ◽

Normalised Difference Vegetation Index ◽

Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

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Technical Note: Development of a Landsat Time Series for Application in Forest Status Assessment in the Inland Northwest United States

Western Journal of Applied Forestry ◽

10.1093/wjaf/23.1.53 ◽

2008 ◽

Vol 23 (1) ◽

pp. 53-62 ◽

Cited By ~ 6

Author(s):

Russell N. Beck ◽

Paul E. Gessler

Keyword(s):

United States ◽

Time Series ◽

Vegetation Dynamics ◽

Vegetation Index ◽

Landsat Imagery ◽

Satellite Image ◽

Temporal Trends ◽

Monitoring Cost ◽

Small Pilot Study ◽

Inland Northwest

Abstract The Inland Northwest United States contains extensive areas of complex, inaccessible terrain requiring significant resource expenditure for forest inventory, assessment, and monitoring. Cost-effective methods are necessary for annual broad-scale assessment of forest condition over complex terrain. Proficiency in the use of timely satellite image products along with spatial analysis tools such as geographic information systems can assist natural resource managers to understand regional dynamics and change within these landscapes. Satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI) can effectively assess and monitor vegetation dynamics of large remote areas. This article presents a newly developed archive and example methods for monitoring forest dynamics through the creation of NDVI departure maps. The NDVI products were generated from a time series of Landsat imagery (1989–2004) to derive both density distributions and a long-term departure from average map for any year or series of years within the time series archive. A preliminary application of the data is demonstrated showing temporal trends of vegetation dynamics relating to harvesting and management within two small pilot study areas in north Idaho.

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Spatiotemporal Characterization of Mangrove Phenology and Disturbance Response: The Bangladesh Sundarban

Remote Sensing ◽

10.3390/rs11172063 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2063 ◽

Cited By ~ 2

Author(s):

Christopher Small ◽

Daniel Sousa

Keyword(s):

Time Series ◽

Mangrove Forest ◽

Landsat Imagery ◽

Methodological Approach ◽

Principal Component ◽

Spatiotemporal Analysis ◽

Low Rank ◽

Eof Analysis ◽

Vegetation Fraction ◽

Disturbance Response

This work presents a spatiotemporal analysis of the phenology and disturbance response in the Sundarban mangrove forest on the Ganges-Brahmaputra Delta in Bangladesh. The methodological approach is based on an Empirical Orthogonal Function (EOF) analysis of the new Harmonized Landsat Sentinel-2 (HLS) BRDF and atmospherically corrected reflectance time series, preceded by a Robust Principal Component Analysis (RPCA) separation of Low Rank and Sparse components of the image time series. Low Rank components are spatially and temporally pervasive while Sparse components are transient and localized. The RPCA clearly separates subtle spatial variations in the annual cycle of monsoon-modulated greening and senescence of the mangrove forest from the spatiotemporally complex agricultural phenology surrounding the Sundarban. A 3 endmember temporal mixture model maps spatially coherent differences in the 2018 greening-senescence cycle of the mangrove which are both concordant and discordant with existing species composition maps. The discordant patterns suggest a phenological response to environmental factors like surface hydrology. On decadal time scales, a standard EOF analysis of vegetation fraction maps from annual post-monsoon Landsat imagery is sufficient to isolate locations of shoreline advance and retreat related to changes in sedimentation and erosion, as well as cyclone-induced defoliation and recovery.

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Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2016.03.031 ◽

2016 ◽

Vol 179 ◽

pp. 196-209 ◽

Cited By ~ 74

Author(s):

Wenyu Li ◽

Peng Gong

Keyword(s):

Time Series ◽

Continuous Monitoring ◽

Landsat Imagery

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Time-series studies on air pollution and daily outpatient visits for allergic rhinitis in Beijing, China

The Science of The Total Environment ◽

10.1016/j.scitotenv.2011.04.007 ◽

2011 ◽

Vol 409 (13) ◽

pp. 2486-2492 ◽

Cited By ~ 49

Author(s):

Fengying Zhang ◽

Wuyi Wang ◽

Jinmei Lv ◽

Thomas Krafft ◽

Jin Xu

Keyword(s):

Air Pollution ◽

Time Series ◽

Allergic Rhinitis ◽

Outpatient Visits ◽

Time Series Studies ◽

Beijing China

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Harnessing the Temporal Dimension to Improve Object-Based Image Analysis Classification of Wetlands

Remote Sensing ◽

10.3390/rs10091467 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1467 ◽

Cited By ~ 7

Author(s):

Meghan Halabisky ◽

Chad Babcock ◽

L. Moskal

Keyword(s):

Time Series ◽

Image Analysis ◽

Classification Accuracy ◽

Sampling Design ◽

Landsat Imagery ◽

Temporal Dimension ◽

Substantial Impact ◽

Object Based Image Analysis ◽

Temporal Domain ◽

Object Based

Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their temporal pattern may be their sole defining characteristic. When multiple images are used in object-based image analysis, it is often constrained to a specific number of images which are selected because they cover the perceived range of temporal variability of the features of interest. Here, we provide a method to identify wetlands using a time series of Landsat imagery by building a Random Forest model using each image observation as an explanatory variable. We tested our approach in Douglas County, Washington, USA. Our approach exploiting the temporal domain classified wetlands with a high level of accuracy and reduced the number of spectrally similar false positives. We explored how sampling design (i.e., random, stratified, purposive) and temporal resolution (i.e., number of image observations) affected classification accuracy. We found that sampling design introduced bias in different ways, but did not have a substantial impact on overall accuracy. We also found that a higher number of image observations up to a point improved classification accuracy dependent on the selection of images used in the model. While time series analysis has been part of pixel-based remote sensing for many decades, with improved computer processing and increased availability of time series datasets (e.g., Landsat archive), it is now much easier to incorporate time series into object-based image analysis classification.

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