Detecting and Quantifying Forest Change: The Potential of Existing C- and X-Band Radar Datasets

Tanase, Mihai A.; Ismail, Ismail; Lowell, Kim; Karyanto, Oka; Santoro, Maurizio

doi:10.1371/journal.pone.0131079

Cited by 22 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As well, the wetlands in the CGE are located in areas with an altitude near or under the sea level [47,65]. Additionally, the elevation data of SRTM90 could be affected by densely vegetated areas [66–69] and wetlands [70]. SRTM has a spatial resolution of 90 m 2 and was, therefore, resampled to 231.35 m 2 to match the MOD13Q1 pixels using a bilinear interpolation.…”

Section: Methodsmentioning

confidence: 99%

Geospatial modeling of land cover change in the Chocó-Darien global ecoregion of South America; One of most biodiverse and rainy areas in the world

Fagua

Ramsey

2019

PLoS ONE

View full text Add to dashboard Cite

The tropical rain forests of northwest South America fall within the Chocó-Darien Global Ecoregion (CGE). The CGE is one of 25 global biodiversity hotspots prioritized for conservation due to its high biodiversity and endemism as well as threats due to deforestation. The analysis of land-use and land-cover (LULC) change within the CGE using remotely sensed imagery is challenging because this area is considered to be one of the rainiest places on the planet (hence high frequency of cloud cover). Furthermore, the availability of high-resolution remotely sensed data is low for developing countries before 2015. Using the Random Forest ensemble learning classification tree system, we developed annual LULC maps in the CGE from 2002 to 2015 using a time series of cloud-free MODIS vegetation index products. The MODIS imagery was processed through a Gaussian weighted filter to further correct for cloud pollution and matched to visual interpretations of land cover and land use from available high spatial resolution imagery (WorldView-2, Quick Bird, Ikonos and GeoEye-1). Validation of LULC maps resulted in a Kappa of 0.87 (Sd = 0.008). We detected a gradual replacement of forested areas with agriculture (mainly grassland planted to support livestock grazing), and secondary vegetation (agriculture reverting to forest) across the CGE. Forest loss was higher between 2010–2015 when compared to 2002–2010. LULC change trends, deforestation drivers, and reforestation transitions varied according to administrative organization (countries: Panamanian CGE, Colombian CGE, and Ecuadorian CGE).

show abstract

Section: Methodsmentioning

confidence: 99%

Geospatial modeling of land cover change in the Chocó-Darien global ecoregion of South America; One of most biodiverse and rainy areas in the world

Fagua

Ramsey

2019

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Other methods potentially suitable for mapping windthrown forests with SAR data include approaches demonstrated in other studies of natural and/or anthropogenic forest disturbance. These include mapping snow-damaged forest areas [23], monitoring selective logging and thinning operations in boreal and tropical forest biomes [11,[24][25][26][27][28], forest clear cutting and other forest changes [29][30][31][32][33][34].…”

Section: Windstorm Damage Studies With Sarmentioning

confidence: 99%

Detection of Forest Windstorm Damages with Multitemporal SAR Data—A Case Study: Finland

et al. 2021

View full text Add to dashboard Cite

The purpose of this study was to develop methods to localize forest windstorm damages, assess their severity and estimate the total damaged area using space-borne SAR data. The development of the methods is the first step towards an operational system for near-real-time windstorm damage monitoring, with a latency of only a few days after the storm event in the best case. Windstorm detection using SAR data is not trivial, particularly at C-band. It can be expected that a large-area and severe windstorm damage may affect backscatter similar to clear cutting operation, that is, decrease the backscatter intensity, while a small area damage may increase the backscatter of the neighboring area, due to various scattering mechanisms. The remaining debris and temporal variation in the weather conditions and possible freeze–thaw transitions also affect observed backscatter changes. Three candidate windstorm detection methods were suggested, based on the improved k-nn method, multinomial logistic regression and support vector machine classification. The approaches use multitemporal ESA Sentinel-1 C-band SAR data and were evaluated in Southern Finland using wind damage data from the summer 2017, together with 27 Sentinel-1 scenes acquired in 2017 and other geo-referenced data. The stands correctly predicted severity category corresponded to 79% of the number of the stands in the validation data, and already 75% when only one Sentinel-1 scene after the damage was used. Thus, the damaged forests can potentially be localized with proposed tools within less than one week after the storm damage. In this study, the achieved latency was only two days. Our preliminary results also indicate that the damages can be localized even without separate training data.

show abstract

“…Such trees maintain some scattering elements (e.g., leaves) immediately after the fire, thus sustaining higher levels of radar scattering. However, as a consequence of fire damage, such trees may not recover and die shortly after the fire, which results in reduced backscatter coefficient as dry vegetation has a lower dielectric constant when compared to healthy vegetation [92].…”

Section: Discussionmentioning

confidence: 99%

Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas

2019

Self Cite

View full text Add to dashboard Cite

Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a better understanding of the temporal decorrelation effects as well as its sources. This paper analyses the temporal decorrelation of the Sentinel-1 C-band backscatter coefficient over burned areas in Mediterranean ecosystems. Several environmental variables influenced the radar scattering such as fire severity, post-fire vegetation recovery, water content, soil moisture, and local slope and aspect were analyzed. The ensemble learning method random forests was employed to estimate the importance of these variables to the decorrelation process by land cover classes. Temporal decorrelation was observed for over 32% of the burned pixels located within the study area. Fire severity, vegetation water content, and soil moisture were the main drivers behind temporal decorrelation processes and are of the utmost importance for areas detected as burned immediately after fire events. When burned areas were detected long after fire (decorrelated areas), due to reduced backscatter coefficient variations between pre- to post-fire acquisitions, water content (soil and vegetation) was the main driver behind the backscatter coefficient changes. Therefore, for efficient synthetic aperture radar (SAR)-based monitoring of burned areas, detection, and mapping algorithms need to account for the interaction between fire impact and soil and vegetation water content.

show abstract

Detecting and Quantifying Forest Change: The Potential of Existing C- and X-Band Radar Datasets

Cited by 22 publications

References 37 publications

Geospatial modeling of land cover change in the Chocó-Darien global ecoregion of South America; One of most biodiverse and rainy areas in the world

Geospatial modeling of land cover change in the Chocó-Darien global ecoregion of South America; One of most biodiverse and rainy areas in the world

Detection of Forest Windstorm Damages with Multitemporal SAR Data—A Case Study: Finland

Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas

Contact Info

Product

Resources

About