Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland

Abstract:The unique ecosystems of the Hawaiian Islands are progressively being threatened following the introduction of exotic species. Operational implementation of remote sensing for the detection, mapping and monitoring of these biological invasions is currently hampered by a lack of knowledge on the spectral separability between native and invasive species. We used spaceborne imaging spectroscopy to analyze the seasonal dynamics of the canopy hyperspectral reflectance properties of four tree species: (i) Metrosideros polymorpha, a keystone native Hawaiian species; (ii) Acacia koa, a native Hawaiian nitrogen fixer; (iii) the highly invasive Psidium cattleianum; and (iv) Morella faya, a highly invasive nitrogen fixer. The species specific separability of the reflectance and derivative-reflectance signatures extracted from an Earth Observing-1 Hyperion time series, composed of 22 cloud-free images spanning a period of four years and was quantitatively evaluated using the Separability Index (SI). The analysis revealed that the Hawaiian native trees were universally unique from the invasive trees in their near-infrared-1 (700-1,250 nm) reflectance (0.4 > SI > 1.4). Due to its higher leaf area index, invasive trees generally had a higher near-infrared reflectance. To a lesser extent, it could also be demonstrated that nitrogen-fixing trees were spectrally unique from non-fixing trees. The higher leaf nitrogen content of nitrogen-fixing trees was expressed through slightly increased separabilities in visible and shortwave-infrared reflectance wavebands (SI = 0.4). We also found phenology to be key to spectral separability analysis. As such, it was shown that the spectral separability in the near-infrared-1 reflectance OPEN ACCESSRemote Sens. 2012, 4 2511 between the native and invasive species groups was more expressed in summer (SI > 0.7) than in winter (SI < 0.7). The lowest separability was observed for March-July (SI < 0.3). This could be explained by the invasives taking advantage of the warmer summer period to expand their canopy. There was, however, no specific time window or a single spectral region that always defined the separability of all species groups, and thus intensive monitoring of plant phenology as well as the use of the full-range (400-2,500 nm) spectrum was highly advantageous in differentiating each species. These results set a basis for an operational invasive species monitoring program in Hawai'i using spaceborne imaging spectroscopy.

Section: Discussionmentioning

confidence: 54%

Hyperspectral Time Series Analysis of Native and Invasive Species in Hawaiian Rainforests

Somers

Asner

2012

“…Some differences between the two maps can be attributed to the spatial resolution influencing the object-based results, as described by Immitzer et al [40,97] and Mirik and Ansley [98]. The differences in resolution, two meters versus ten meters, translated in some of the LSMS polygons consisting in a mix of classes.…”

Section: Impact Of Spatial Resolutionmentioning

confidence: 99%

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

Rima

Einzmann

et al. 2017

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.

“…Another important characteristic of remotely sensed imagery for mapping plant species is the spatial resolution or pixel size representing the smallest area identifiable in the imagery. Higher spatial resolution imagery often contains detailed intraspecies spectral variability than lower resolution imagery, when target species occur in small patches or cover smaller areas than the pixel size [28,48,49]. Timing, location, species composition, spectral and spatial resolutions are all important for vegetation mapping at the species level, because spectral reflectance from vegetation is determined by structural and biochemical properties, including leaf area, leaf angle distribution, architecture, biochemical and pigment (lignin, cellulose, chlorophylls, carotenes, nitrogen and water) contents [46,47] over time and space.…”

Section: Introductionmentioning

confidence: 99%

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Self Cite

Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two OPEN ACCESSRemote Sens. 2013, 5 613 distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery.