Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

Abstract

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

The Influence of Seasonality on the Multi-Spectral Image Segmentation for Identification of Abandoned Land

“…The densest areas of the deserted lands should be inspected on site or compared with the abandoned layer from the Ministry of Agriculture of the Republic of Lithuania to assess their matching percentage. The determined average of the green spectra (950) in the calculations of the SAM algorithm corresponded to the average value determined in the previous studies [3]. The areas of excluded classes calculated by the ML and MD algorithms did not correspond to the real image.…”

“…In the middle of this decade, the intensified anticyclone over Britain reached the Baltic countries, and the precipitation left the country. Previous research has shown that grass started growing intensively in April [3]. The average air temperature ranged from 13.4 to 14.7 • C, promoting plant growth.…”

Covariate Model of Pixel Vector Intensities of Invasive H. sosnowskyi Plants

“…The mean spectral signature for the H. sosnowskyi plants obtained in this study was 956, a higher value than that previously established for abandoned land (weeds or shrub) by Sužiedelytė Visockienė et al (2020), who reported a value of 795 [ 12 ]. Areas of H. sosnowskyi growth are also classified as abandoned land, and hence to ensure full identification of abandoned land, we would recommend using mean spectral signature values of 800 to 1000 for segmentation via an unsupervised method.…”

“…In this case, satellite image classification was performed using two methods. The multi-spectral image pixels were classified using a combination of per-pixel SVM (supervised) and per-field object-oriented (unsupervised) classification methods based on the Sentinel-2A visible light bands ( Figure 2 ) [ 8 , 12 , 15 ].…”

“…Data (images) from remote sensing form the basis for an analysis of the geometric characteristics of plants. The remote sensing methods of image segmentation and mapping are used to classify the pixels [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Imaging is an important method used in this research to assess the danger of invasive H. sosnowskyi plants.…”