Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments

Sesnie, Steven E.; Gessler, Paul E.; Finegan, Bryan; Thessler, Sirpa

doi:10.1016/j.rse.2007.08.025

Cited by 191 publications

(111 citation statements)

References 71 publications

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“…Regarding the feature importance for the highest accuracy of forest type classification (Figure 7), the input features were evaluated by the substitution method [33], and the feature distribution from the highest classification accuracy of forest types is consistent with some previous studies [51,57,58]. The slope and phenological features with higher quantization scores were the two most pivotal features for forest type identification.…”

Section: Discussionsupporting

confidence: 73%

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

et al. 2018

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Abstract:Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.

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Section: Discussionsupporting

confidence: 73%

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

et al. 2018

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“…Other studies have confirmed these remotely sensed variables, particularly near infrared and NDVI, are important for land cover classification and land cover change mapping. Such variables are particularly important when discriminating between forest structural condition (i.e., open or closed canopy), monitoring stand age and regrowth, and estimating species composition and richness [85][86][87]. Studies have also established that the multiple scattering and subsequent depolarization of the radar signal explains the importance of HV polarization for classifying land cover and estimating biomass, particularly in forested regions [72,78,87].…”

Section: Upland Water and Wetland Land Cover Classification (Level 1)mentioning

confidence: 99%

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

2013

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Abstract:Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource OPEN ACCESS Remote Sens. 2013, 5 3213 managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

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“…Successful classifications have been carried out using multi-spectral aerial/ satellite imagery (Immitzer et al 2012;Ke et al 2010;Key et al 2001;Korpela et al 2011;Lucas et al 2008;Sesnie et al 2008) and hyperspectral data (Dalponte et al 2014;2012). The scale of analysis varies from tree crown pixel or object (object-based image analysis) to the crown itself.…”

Section: Introductionmentioning

confidence: 99%

“…The scale of analysis varies from tree crown pixel or object (object-based image analysis) to the crown itself. When available, 3D data (ALS LiDAR, SRTM DEM) can improve the classification accuracy (Dalponte et al 2014;Ke et al 2010;Sesnie et al 2008). Forest species classification has also been studied through the use of multi-temporal remote sensing dataset which allows to highlight phenological differences between forest species (Key et al 2001;Hill et al 2010;Zhu and Liu 2014).…”

Section: Introductionmentioning

confidence: 99%

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

198

179

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Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.The comparison of various scales of image analysis identified the smallest object-based image analysis (OBIA) objects (ca. 1 m 2 ) as the most relevant scale. Variables derived from spectral information (bands ratios) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for site 1 and site 2). The classification scenario regarding the health condition of the black alders of the site 1 performed the best (90.6 %).The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multi-spectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metric datasets derived from those dense time series.

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Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments

Cited by 191 publications

References 71 publications

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

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