Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification—a case study over deciduous forests of central India

Chand, Tara; Badarinath, K. V. S.

doi:10.1080/01431160701253295

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…This has been observed also for X-band in forests [24,49]. Based on this fundamental relation, the general sensitivity of coherence for forest structural parameters was confirmed in different studies, e.g., for stem volume [21,50], Lorey's height [21,51], and tree height [47,52].…”

Section: Discussionsupporting

confidence: 61%

“…The main outcome, here, was that a separation by forest type (deciduous/coniferous) did not improve the overall relations between volume coherence and forest structural parameters and that all significant relations were negative, independent of forest type (Table 3). This is in opposition to other findings that underlined the impact of stratification on the relation between SAR data and forest parameters and even showed opposing trends in models after separation of the total sample [34,52]. A general criticism of applying models across different forest types was issued by [29] who claimed that scatterplots of SAR variables against forest structural variables show clusters within the distribution that are related to the strata and thus, the statistical relationship might not reflect the actual relation within each cluster.…”

Section: Discussionmentioning

confidence: 62%

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Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

et al. 2019

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Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests' vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey's height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type. and ranging (LiDAR), and synthetic aperture radar (SAR) data. A comprehensive summary of studies that aim at quantifying forest biomass from satellite sensors was compiled by [3]. AGB estimates from optical remote sensing data mostly rely on vegetation indices that parameterize the photosynthetic activity of vegetation. They imply a relationship between the foliage and the total AGB of a vegetation stand, which is further used to estimate AGB. Since the major part of AGB in forests is composed of non-photosynthetic (woody) components, such approaches imply high uncertainties. The major challenge in AGB mapping from optical data is the saturation of the signal after canopy closure which especially hampers the estimation of high-level AGB [4]. However, recent studies confirmed the general ability of optical systems like Landsat to map AGB at biomass levels around 70 Mg/ha [5].The saturation problem can be overcome by sensors, which are able to penetrate through the canopy and interact with the forest constituents. For instance, LiDAR and interferometric SAR (InSAR) systems generally provide the necessary technique because they are able to penetrate the canopy of a forest and deliver information about the vertical structure of a forest stand. LiDAR data produce high-accuracy canopy height models (CHM) but the main drawback ...

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Section: Discussionsupporting

confidence: 61%

Section: Discussionmentioning

confidence: 62%

Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

et al. 2019

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“…However, extensive work on the application of airborne LiDAR systems in forestry has been done [113–115]. Methodologies based on LiDAR datasets have been developed to assess three-dimensional forest structures [21–26].…”

Section: Measurement Methods and Sensorsmentioning

confidence: 99%

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

Zheng

Moskal

2009

Sensors

461

273

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The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

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“…The successful launch of RADARSAT-2 in 2007, which is equipped with a fully polarized SAR operating at C-Band (5.3 GHz) with a wavelength of approximately 5.6 cm, has provided a new opportunity for the use of radar to estimate AGB. Some studies [ 21 , 22 ] showed significant correlations between C-band backscatter and forest structural parameters such as diameter-at-breast height (DBH), volume, basal area, height, and AGB. However, estimating AGB using the relationship between biomass and SAR backscattering remains problematic due to not only high sensitivity to soil conditions, including surface roughness and soil moisture in low vegetation coverage, but also to saturation at high biomass levels [ 14 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China

Shao

Zhang

2016

Sensors

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Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass.

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Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification—a case study over deciduous forests of central India

Cited by 15 publications

References 22 publications

Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China

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