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Tea plantations expansion has occurred in the major tea planting area of Yunnan province, China, however, it is a big challenge to extract and map the distribution of tea plantations due to their non-obvious phenological characteristics in the tropical and subtropical regions. Firstly, we demonstrated the pruning phenological phase of tea plantations by using field hyperspectral data. Secondly, we analyzed the optical features of typical land cover types from the available time series Landsat imagery with the Google Earth Engine (GEE) platform. We proposed a pixel and phenology- based approach to extract and map tea plantations distribution at the 30-m resolution of the historical time period of 2015 to 2019. The resultant map had high user/producer accuracies of 91.07% and 86.90%, respectively. Our study provides a promising new approach to extract and map tea plantations in complex tropical landscapes at fine spatial resolution by using only free historical Landsat imagery. [ABSTRACT FROM AUTHOR]

Highlights • Integrated ALOS PALSAR and Landsat TM/ETM+/OLI time series imagery. • Presents a pixel-based support vector machine algorithm to map tea plantation. • Quantified spatial-temporal changes of tea plantations in complex landscapes of tropical and subtropical China. • Demonstrates expansion of tea plantations result in deforestation and discusses the reasons for tea plantations expansion. Abstract High demand for tea has driven the expansion of tea plantations in the tropical and subtropical regions over the past few decades. Tea plant cultivation promotes economic development and creates job opportunities, but tea plantation expansion has significant impacts on biodiversity, carbon and water cycles, and ecosystem services. Mapping the spatial distribution and extent of tea plantations in a timely fashion is crucial for land use management and policy making. In this study, we mapped tea plantation expansion in Menghai County, Yunnan Province, China. We analyzed the structure and features of major land cover types in this tropical and subtropical region using (1) the HH and HV gamma-naught imagery from the Advanced Land Observation Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and (2) time series Landsat TM/ETM+/OLI imagery. Tea plantation maps for 2010 and 2015 were generated using the pixel-based support vector machine (SVM) approach at 30 m resolution, which had high user/producer accuracies of 83.58%/91.67% and 87.50%/90.83%, respectively. The resultant maps show that tea plantation area increased by 33.56% (∼9335 ha), from ∼27,817 ha in 2010 to ∼37,152 ha in 2015. The additional tea plantation area was mainly converted from forest (32.50%) and cropland (67.50%). The results showed that the combination of PALSAR and optical data performed better in tea plantation mapping than using optical data only. This study provides a promising new approach to identify and map tea plantations in complex tropical landscapes at high spatial resolution. [ABSTRACT FROM AUTHOR]

Accurate information about forest type and distribution is critical for many scientific applications. It is possible to make a forest type map from the satellite data in a cost effective way. However, forest type mapping over a large and mountainous geographic area is still challenging, due to complex forest type compositions, spectral similarity among various forest types, poor quality images with clouds or cloud shadows and difficulties in managing and processing large amount data. Based on the Google Earth Engine (GEE) cloud platform, a method of forest types mapping using Landsat-8 OLI imagery and multiple environmental factors was developed and tested within Yunnan Province (about 390,000 km2) of China. The proposed approach employed a pixel-based seasonal image compositing method to produce two types of seasonal composite images, i.e., four 7-spectral-band composite images and four 5-VI-band composite images associated in spring, summer, autumn, and winter. Then, single-season feature bands and multi-seasonal feature bands were combined with the feature bands of topography, temperature, and precipitation, respectively, and resulting in 17 feature combinations. Finally, using a random forest (RF) classifier, 17 feature combinations were separately experimented to classify the forest type over the study area. The study area was firstly classified into the forest and the non-forest, and then the forest was sub-classified into five forest types (evergreen needleleaf forest, deciduous needleleaf forest, evergreen broadleaf forest, deciduous broadleaf forest, and mixed forest). The results showed that the pixel-based multi-seasonal median composite can produce a cloud-free image for the entire region and is suitable for forest type mapping. Compared with a single-season composite, a multi-seasonal composite can distinguish different forest types more effectively. The environmental factors also improve the accuracy of forest type mapping. With the ground survey samples as reference values, the classification performance of 17 feature combinations was compared, and the optimal feature combination was found out. For the optimal feature combination, its overall accuracy of the forest/non-forest cover map and the forest type map reached 97.57% (Kappa = 0.950) and 70.30% (Kappa = 0.628), respectively. The proposed approach has demonstrated strong potential of high classification accuracy and convenient calculation when mapping forest types over a national or global scale, and its product of 30 m resolution forest type map is capable of contributing to forest resource management. [ABSTRACT FROM AUTHOR]