Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images

Meng, Jinghui; Li, Shiming; Wang, Wei; Liu, Qingwang; Xie, Shiqin; Wörns, MA

doi:10.3390/rs8090719

Cited by 43 publications

(30 citation statements)

References 101 publications

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“…The authors reported that 74% of the defoliation was detected with a misclassification of undisturbed areas of 39% in those pixels where at least 50% birch forest cover was present. SPOT-5 images were investigated for forest health in [49], the authors acquired several structural indicators from these images and defined forest health indicator from them. The final forest health indicator was evaluated with RMSE of 1.674 and R-square of 0.47.…”

Section: Assessing Forest Health and Physiology Statusmentioning

confidence: 99%

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Surový

Kuželka

2019

Forests

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In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.

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Section: Assessing Forest Health and Physiology Statusmentioning

confidence: 99%

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Surový

Kuželka

2019

Forests

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“…Although LiDAR and hyperspectral data possess high potential for species classification, their operational use is restricted owing to limited availability and high acquisition costs [3,24], and the applicability of these data in a regional or global scale is still limited [25]. Therefore, optical multispectral data are often considered a good alternative to LiDAR data [26]. Currently, radar (synthetic aperture radar) data are used in tree species mapping, particularly for the determination of broad-leaved forest types [2].…”

Section: Introductionmentioning

confidence: 99%

Forest Stand Species Mapping Using the Sentinel-2 Time Series

et al. 2019

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Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.

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“…We applied the best subset variable selection procedure using the leaps package [58] in R statistical software [59] and the selection of potential independent variables was restricted to a combination of up to five independent variables with minimum Bayesian Information Criterion (BIC) as selection criteria. Since multicollinearity normally occurs between remotely sensed variables [60], we further removed collinear variables using variance inflation factors (VIF). This procedure was repeated for logarithmic and square root transformed dependent variable.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland

et al. 2016

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Application of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on the availability of reliable digital terrain models (DTMs). The main objective of this study was to evaluate application of 3D data derived from UAV imagery in biomass estimation and to compare impacts of DTMs generated based on different methods and parameter settings. Biomass was modeled using data acquired from 107 sample plots in a forest reserve in miombo woodlands of Malawi. The results indicated that there are no significant differences (p = 0.985) between tested DTMs except for that based on shuttle radar topography mission (SRTM). A model developed using unsupervised ground filtering based on a grid search approach, had the smallest root mean square error (RMSE) of 46.7% of a mean biomass value of 38.99 Mg·ha −1 . Amongst the independent variables, maximum canopy height (Hmax) was the most frequently selected. In addition, all models included spectral variables incorporating the three color bands red, green and blue. The study has demonstrated that UAV acquired image data can be used in biomass estimation in miombo woodlands using automatically generated DTMs.

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Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images

Cited by 43 publications

References 101 publications

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Forest Stand Species Mapping Using the Sentinel-2 Time Series

Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland

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