Journal of Remote Sensing / 2021 / Article / Tab 6

Review Article

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

Table 6

Summary of “multiple” methods currently used for tree species (TS) mapping.

MethodCharacteristic and descriptionAdvantage and limitationMajor factorExample

Multisensor methodIntegrating multiple sensors’ images (different optical sensors’ (satellite, aircraft, and UAV based) combination or optical sensor(s) combined with LiDAR) is a synergy process of different sensors’ images that can provide different spatial, spectral, band setting, textural, and geometric information, which offers a new potential in improving TS classification and mappingMultiple sensors’ images enable improving TS mapping accuracy by an efficient combination of different spectral, spatial, and textural/geometric features, thus leading to higher classification accuracy. Requires complex image processing (e.g., data fusion techniques) and has high cost for using some sensor(s) dataResampling and registration as well as normalization processes needed between different sensors[10, 41, 57, 162]
Multitemporal methodUsing two or more seasons’ (dates) sensor’s images to classify TS to be expected to increase TS mapping accuracy. Can select an optimal seasonal data from multiseasonal images or two or more seasonal image combination(s) for TS classificationMultitemporal remote sensing image data are just to align the image acquisition times with the phenological cycles of different TS under investigation. Multiseason image acquisition usually comes with higher costs and greater image processing demandsResampling and registration as well as normalization processes needed between different seasonal images[34, 40, 160, 163]
Multilevel classification system methodA hierarchical spatial organization of objects (classes) in a landscape or image scene from a larger landscape/cover type unit into the smaller objects or component units (e.g., TS)Match the logical structure of surface cover classification strategies and enhance the relative spectral and textural differences among the similar cover classes at a higher level. Need more computing power and determine thresholds at different levelsDefine and determine thresholds at different levels[34, 49, 68]