Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia

Grandi, Elsa Carla De; Mitchard, Edward T. A.; Hoekman, D.H.

doi:10.3390/rs8080641

Cited by 11 publications

(7 citation statements)

References 73 publications

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“…The canal gap geometry was derived from SPOT-6/7 data and is expressed as canal width and orientation. The description of forest structure is based on field observations from previous studies [17,19,59,60]. Relevant parameters include forest height and canopy roughness.…”

Section: Theoretical Background Of Canal-gap Mapping and Forest-degradation Quantificationmentioning

confidence: 99%

Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1

Hoekman

Kooij²,

Quiñónes³

et al. 2020

Remote Sensing

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The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge.

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Section: Theoretical Background Of Canal-gap Mapping and Forest-degradation Quantificationmentioning

confidence: 99%

Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1

Hoekman

Kooij²,

Quiñónes³

et al. 2020

Remote Sensing

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“…Firstly, to test if the training samples extracted from TC can separate positive and negative changes from other unchanged colored backgrounds, we introduced the Jeffries Matusita (JM) distance to measure the separability among the three classes. The JM distance is a distinguishing indicator that widely used in the remote sensing field [41][42][43], and its value ranges from 0 to 2. The larger the value is, the better separability is among the different categories.…”

Section: Validation Of the Proposed Difference Imagesmentioning

confidence: 99%

Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network

Wang

Zhang

et al. 2019

Remote Sensing

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With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.

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“…Finally, it is of interest to understand the sensitivity of HS and the consequential classification to the knowledge of the ground topography. A non-compensated ground topography may indeed induce artifacts [7]. The phase center height have thus been calculated with respect to their minimum in the HS cell.…”

Section: Using Horizontal Structure For Distinguishing Successional Smentioning

confidence: 99%

Tropical forest structure observation with TanDEM-X data

Pulella

Bispo

Pardini

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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TanDEM-X forms together with TerraSAR-X the first single-pass polarimetric interferometer in space. This allows for the first time the acquisition and analysis of Single-, Dual-, and Quad-Pol-InSAR data without the disturbing effect of temporal decorrelation globally. For this reason, the exploration of TanDEM-X data for forestry is constantly increasing especially concerning forest height estimation, biomass classification and structure characterization. This paper reports the results of recent experiments aimed at investigating the potentials of TanDEM-X in characterizing quantitatively the spatial variability of the canopy top and phase center height, which is a proxy to horizontal structure. It is shown that such characterization can allow to differentiate among e.g. different successional and / disturbance stages in tropical forests.

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Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia

Cited by 11 publications

References 73 publications

Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1

Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1

Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network

Tropical forest structure observation with TanDEM-X data

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