Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes

Abstract

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

Latest 30-m map of mature rubber plantations in Mainland Southeast Asia and Yunnan province of China: Spatial patterns and geographical characteristics

“…More importantly, compared with the ratioing method of individual band (e.g., near-infrared (NIR)), the (re)normalization of bands or VIs considerably reduces errors caused by surface anisotropy, saturation, and topographical variations (Gutman, 1991;Huete et al, 1997). Phenology-based approaches have also shown promise for reducing the demand for data to monitor rubber plantations (Li et al, 2015;Xiao et al, 2019a;Zhai et al, 2018). In the RNVI approach, only two target images of the defoliating phase and the other when new leaves emerge were needed to satisfy the data requirement.…”

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

“…It has been determined that an essential factor for accurate mapping of rubber plantations in SE Asian ecosystems is the availability of data at two crucial phenological periods: defoliation (leaf off) and new leaf emergence (leaf on), which distinguishes deciduous rubber trees from other vegetation. Many approaches consist of using optical satellite data [6,[17][18][19][20][21] to detect rubber plantations. However, due to the persistent cloud cover in tropical regions, it is difficult to acquire sufficient high resolution satellite imagery which in turn compromises spatial resolution, as coarser satellites such as MODIS are frequently used.…”

Rubber Plantation Retiring with Simulation of Market-Priced Ecosystem Services in Xishuangbanna, China

“…Land use/land cover data for was classified by 32 Landsat images which were downloaded from Earth Explorer (http://earthexplorer.usgs.gov/). The nearest-neighbor-object-based phenology approach was applied for the classification (Zhai et al, 2018). Google Earth data were used for training and validation.…”