Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality

Waser‬, Lars T.; Küchler, Meinrad; Jütte, Kai; Stampfer, Theresia

doi:10.3390/rs6054515

Cited by 145 publications

(127 citation statements)

References 64 publications

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“…Interestingly, the two most important features were generated from the GR index. The importance and advantages of indices is well described by the literature [59,61,63,78], while the significance of texture matches the observations of Toscani et al [76] and Koger et al [112].…”

Section: Random Forestsupporting

confidence: 70%

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Rima

Einzmann

et al. 2017

Remote Sensing

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Abstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis-competing for nutrients and water-has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the "Woody Weeds" project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87-0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80-0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.

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Section: Random Forestsupporting

confidence: 70%

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Rima

Einzmann

et al. 2017

Remote Sensing

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“…Both tree species classifications are therefore not fully satisfactory. In general, the achieved classification accuracies are lower than those of other studies, in which satellite data with higher spatial resolution were used (e.g., WorldView-2) [17,59].…”

Section: Tree Species Classificationcontrasting

confidence: 38%

“…In our case, however, object sizes were relatively small compared to the spatial resolution of S2, and this in particular for the non-dominant species (Table 2). Therefore, the theoretical advantages of OBIA could not play out as well as for EO data with a very high spatial resolution [17,59,[65][66][67][68]. Instead, in our case, the two approaches obtained comparable overall accuracies.…”

Section: Pixel-basedmentioning

confidence: 61%

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

2016

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Abstract:The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In the first case study, an S2 image was used to map six summer crop species in Lower Austria as well as winter crops/bare soil. Crop type maps are needed to account for crop-specific water use and for agricultural statistics. Crop type information is also useful to parametrize crop growth models for yield estimation, as well as for the retrieval of vegetation biophysical variables using radiative transfer models. The second case study aimed to map seven different deciduous and coniferous tree species in Germany. Detailed information about tree species distribution is important for forest management and to assess potential impacts of climate change. In our S2 data assessment, crop and tree species maps were produced at 10 m spatial resolution by combining the ten S2 spectral channels with 10 and 20 m pixel size. A supervised Random Forest classifier (RF) was deployed and trained with appropriate ground truth. In both case studies, S2 data confirmed its expected capabilities to produce reliable land cover maps. Cross-validated overall accuracies ranged between 65% (tree species) and 76% (crop types). The study confirmed the high value of the red-edge and shortwave infrared (SWIR) bands for vegetation mapping. Also, the blue band was important in both study sites. The S2-bands in the near infrared were amongst the least important channels. The object based image analysis (OBIA) and the classical pixel-based classification achieved comparable results, mainly for the cropland. As only single date acquisitions were available for this study, the full potential of S2 data could not be assessed. In the future, the two twin S2 satellites will offer global coverage every five days and therefore permit to concurrently exploit unprecedented spectral and temporal information with high spatial resolution.

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“…Additionally, based on the tree species classification described in [12,33], the bands RGB and NIR from the original ADS80 were color-transformed into intensity (I), hue (H), and saturation (S), to separate the effect of illumination from the quantity of intensity. These three new variables are: I[RGNIR], H[RGNIR], and S[RGNIR], as designated in [7,38]. Remote Sensing Indices (RSI) have been extensively used to explore vegetation's spectral signature characteristics, i.e., tree species classification, both in the visible and near-infrared part of the spectrum [39].…”

Section: Variable Computationmentioning

confidence: 99%

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

2017

Self Cite

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Abstract:Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km 2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector.

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Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality

Cited by 145 publications

References 64 publications

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

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