Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

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A Simple Approach for Mapping Forest Cover from Time Series of Satellite Data

Remote Sensing ◽

10.3390/rs12182918 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2918

Author(s):

Yang Liu ◽

Ronggao Liu

Keyword(s):

Time Series ◽

Vegetation Index ◽

Forest Cover ◽

Google Earth ◽

Satellite Observations ◽

Training Data ◽

Herbaceous Vegetation ◽

Forest Mapping ◽

Maturity Period ◽

Forest Cover Mapping

Forest cover mapping based on multi-temporal satellite observations usually uses dozens of features as inputs, which requires huge training data and leads to many ill effects. In this paper, a simple but efficient approach was proposed to map forest cover from time series of satellite observations without using classifiers and training data. This method focuses on the key step of forest mapping, i.e., separation of forests from herbaceous vegetation, considering that the non-vegetated area can be easily identified by the annual maximum vegetation index. We found that the greenness of forests is generally stable during the maturity period, but a similar greenness plateau does not exist for herbaceous vegetation. It means that the mean greenness during the vegetation maturity period of forests should be larger than that of herbaceous vegetation, while its standard deviation should be smaller. A combination of these two features could identify forests with several thresholds. The proposed approach was demonstrated for mapping the extents of different forest types with MODIS observations. The results show that the overall accuracy ranges 91.92–95.34% and the Kappa coefficient is 0.84–0.91 when compared with the reference datasets generated from fine-resolution imagery of Google Earth. The proposed approach can greatly simplify the procedures of forest cover mapping.

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Improving the Accuracy of Fractional Evergreen Forest Cover Estimation at Subpixel Scale in Cloudy and Rainy Areas by Harmonizing Landsat-8 and Sentinel-2 Time-Series Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3064580 ◽

2021 ◽

Vol 14 ◽

pp. 3373-3385

Author(s):

Taixia Wu ◽

Yuting Zhao ◽

Shudong Wang ◽

Hongjun Su ◽

Yingying Yang ◽

...

Keyword(s):

Time Series ◽

Forest Cover ◽

Time Series Data ◽

Evergreen Forest ◽

Series Data ◽

Landsat 8 ◽

Sentinel 2

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Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series

Remote Sensing ◽

10.3390/rs13142675 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2675

Author(s):

Stefan Mayr ◽

Igor Klein ◽

Martin Rutzinger ◽

Claudia Kuenzer

Keyword(s):

Time Series ◽

Surface Water ◽

Spatial Resolution ◽

Pure Water ◽

Feature Space ◽

Observation Time ◽

Temporal Information ◽

Landsat 8 ◽

Water Fraction ◽

Classification Probability

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.

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Mapping Regional Landscape by Using OpenstreetMap (OSM)

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch033 ◽

2019 ◽

pp. 771-790 ◽

Cited By ~ 1

Author(s):

Di Yang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Cover ◽

Regional Scale ◽

Google Earth ◽

Computing Power ◽

New Approach ◽

Computation Cost ◽

Regional Landscape ◽

Google Earth Engine

A forest patterns map over a large extent at high spatial resolution is a heavily computation task but is critical to most regions. There are two major difficulties in generating the classification maps at regional scale: large training points sets and expensive computation cost in classifier modelling. As one of the most well-known Volunteered Geographic Information (VGI) initiatives, OpenstreetMap contributes not only on road network distributions, but the potential of justify land cover and land use. Google Earth Engine is a platform designed for cloud-based mapping with a strong computing power. In this study, we proposed a new approach to generating forest cover map and quantifying road-caused forest fragmentations by using OpenstreetMap in conjunction with remote sensing dataset stored in Google Earth Engine. Additionally, the landscape metrics produced after incorporating OpenStreetMap (OSM) with the forest spatial pattern layers from our output indicated significant levels of forest fragmentation in Yucatan peninsula.

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Mapping Regional Landscape by Using OpenstreetMap (OSM)

Advances in Geospatial Technologies - Volunteered Geographic Information and the Future of Geospatial Data ◽

10.4018/978-1-5225-2446-5.ch008 ◽

2017 ◽

pp. 138-157

Author(s):

Di Yang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Cover ◽

Regional Scale ◽

Google Earth ◽

Computing Power ◽

New Approach ◽

Computation Cost ◽

Regional Landscape ◽

Google Earth Engine

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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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Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Remote Sensing ◽

10.3390/rs11111266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1266 ◽

Cited By ~ 3

Author(s):

Mingzheng Zhang ◽

Dehai Zhu ◽

Wei Su ◽

Jianxi Huang ◽

Xiaodong Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Kalman Filter ◽

Spatial Resolution ◽

Temporal Resolution ◽

Meteorological Data ◽

High Temporal Resolution ◽

Growth Monitoring ◽

Landsat 8 ◽

Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

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Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

Remote Sensing ◽

10.3390/rs12040688 ◽

2020 ◽

Vol 12 (4) ◽

pp. 688 ◽

Cited By ~ 3

Author(s):

Jacky Lee ◽

Jeffrey A. Cardille ◽

Michael T. Coe

Keyword(s):

Remote Sensing ◽

Time Series ◽

Land Cover ◽

Spatial Resolution ◽

Processing Speed ◽

Remote Sensing Data ◽

Google Earth ◽

Wide Audience ◽

Data Set ◽

Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

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Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

Remote Sensing ◽

10.3390/rs12244114 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4114

Author(s):

Shaobo Sun ◽

Yonggen Zhang ◽

Zhaoliang Song ◽

Baozhang Chen ◽

Yangjian Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Open Access ◽

Spatial Resolution ◽

Coastal Wetlands ◽

Coastal Wetland ◽

Regional Scale ◽

Google Earth ◽

Human Welfare ◽

Bohai Rim ◽

Terrain Indices

Coastal wetlands provide essential ecosystem services and are closely related to human welfare. However, they can experience substantial degradation, especially in regions in which there is intense human activity. To control these increasingly severe problems and to develop corresponding management policies in coastal wetlands, it is critical to accurately map coastal wetlands. Although remote sensing is the most efficient way to monitor coastal wetlands at a regional scale, it traditionally involves a large amount of work, high cost, and low spatial resolution when mapping coastal wetlands at a large scale. In this study, we developed a workflow for rapidly mapping coastal wetlands at a 10 m spatial resolution, based on the recently emergent Google Earth Engine platform, using a machine learning algorithm, open-access Synthetic Aperture Radar (SAR) and optical images from the Sentinel satellites, and two terrain indices. We then generated a coastal wetland map of the Bohai Rim (BRCW10) based on the workflow. It has a producer accuracy of 82.7%, according to validation using 150 wetland samples. The BRCW10 data reflected finer information when compared to wetland maps derived from two sets of global high-spatial-resolution land cover data, due to the fusion of multiple data sources. The study highlights the benefits of simultaneously merging SAR and optical remote sensing images when mapping coastal wetlands.

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