New global forest/non-forest maps from ALOS PALSAR data (2007–2010)

Shimada, Masanobu; Itoh, Takuya; Motooka, Takeshi; Watanabe, Masahiro; Shiraishi, Takeshi; Thapa, Rajesh; Lucas, Richard

doi:10.1016/j.rse.2014.04.014

Cited by 535 publications

(387 citation statements)

References 37 publications

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“…The comparison with alternative space-borne remotely sensed data indicated that AGB models using TanDEM-X WorldDEM data had a larger predictive accuracy than other models using either RapidEye, global Landsat maps [34], or global PALSAR maps [35] for which Naesset et al [29] reported RMSE values of 32.2 t·ha −1 (62.7%), 43.6 t·ha −1 (85.0%) and 45.3 t·ha −1 (88.3%), respectively. Consistent with studies conducted in similar forest types, the present one reported effects of saturation for field plots with AGB >186 t·ha −1 .…”

Section: Discussionmentioning

confidence: 99%

Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data

et al. 2017

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Abstract:The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in tropical forests have been inconsistent and further knowledge on the accuracy of models linking AGB and InSAR height data is crucial for the development of large scale forest monitoring programs. This study provides an example of the use of TanDEM-X WorldDEM data to model AGB in Tanzanian woodlands. The primary objective was to assess the accuracy of a model linking AGB with InSAR height from WorldDEM after the subtraction of ground heights. The secondary objective was to assess the possibility of obtaining InSAR height for field plots when the terrain heights were derived from global navigation satellite systems (GNSS); i.e., as an alternative to using airborne laser scanning (ALS). The results revealed that the AGB model using InSAR height had a predictive accuracy of RMSE = 24.1 t·ha −1 ; or 38.8% of the mean AGB when terrain heights were derived from ALS. The results were similar when using terrain heights from GNSS. The accuracy of the predicted AGB was improved when compared to a previous study using TanDEM-X for a sub-area of the area of interest and was of similar magnitude to what was achieved in the same sub-area using ALS data. Overall; this study sheds new light on the opportunities that arise from the use of InSAR data for large scale AGB modelling in tropical woodlands.

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Section: Discussionmentioning

confidence: 99%

Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data

et al. 2017

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“…Therefore, SAR data are an ideal complement to optical-based forest monitoring systems [30][31][32]. In particular, multi-temporal L-band SAR data from ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) have been proven to be capable of detecting tropical deforestation [33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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“…Other multi-temporal products, such as the ALOS PALSAR Forest/Non-Forest data available from 2007 to 2010 [34], were also found to be less effective for detecting small harvests. As an example, we show a map of the Forest/Non-Forest data for the years 2009 and 2010 ( Figure 6).…”

Section: Resultsmentioning

confidence: 99%

“…We combine stand level ground-truth data on harvests with the satellite images, to calculate the rate and intensity of harvests on a yearly basis. We validate these results with harvests outside of the FCC, and compare our results to the Landsat-based Global Forest Watch [30] and the ALOS PALSAR Forest/Non-Forest databases [34]. …”

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confidence: 75%

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

2015

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Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of OPEN ACCESS Remote Sens. 2015, 7 7810 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition.

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New global forest/non-forest maps from ALOS PALSAR data (2007–2010)

Cited by 535 publications

References 37 publications

Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data

Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

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