Phenology-based method for mapping tropical evergreen forests by integrating of MODIS and landsat imagery

32Citations
Citations of this article
83Readers
Mendeley users who have this article in their library.

Abstract

Updated extent, area, and spatial distribution of tropical evergreen forests from inventory data provides valuable knowledge for research of the carbon cycle, biodiversity, and ecosystem services in tropical regions. However, acquiring these data in mountainous regions requires labor-intensive, often cost-prohibitive field protocols. Here, we report about validated methods to rapidly identify the spatial distribution of tropical forests, and obtain accurate extent estimates using phenology-based procedures that integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. Firstly, an analysis of temporal profiles of annual time-series MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) was developed to identify the key phenology phase for extraction of tropical evergreen forests in five typical lands cover types. Secondly, identification signatures of tropical evergreen forests were selected and their related thresholds were calculated based on Landsat NDVI, EVI, and LSWI extracted from ground true samples of different land cover types during the key phenology phase. Finally, a map of tropical evergreen forests was created by a pixel-based thresholding. The developed methods were tested in Xishuangbanna, China, and the results show: (1) Integration of Landsat and MODIS images performs well in extracting evergreen forests in tropical complex mountainous regions. The overall accuracy of the resulting map of the case study was 92%; (2) Annual time series of high-temporal-resolution remote sensing images (MODIS) can effectively be used for identification of the key phenology phase (between Julian Date 20 and 120) to extract tropical evergreen forested areas through analysis of NDVI, EVI, and LSWI of different land cover types; (3) NDVI and LSWI are two effective metrics (NDVI ≥ 0.670 and 0.447 ≥ LSWI ≥ 0.222) to depict evergreen forests from other land cover types during the key phenology phase in tropical complex mountainous regions. This method can make full use of the Landsat and MODIS archives as well as their advantages for tropical evergreen forests geospatial inventories, and is simple and easy to use. This method is suggested for use with other similar regions.

References Powered by Scopus

High-resolution global maps of 21st-century forest cover change

8240Citations
N/AReaders
Get full text

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

2724Citations
N/AReaders
Get full text

Status and distribution of mangrove forests of the world using earth observation satellite data

2260Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Determination of vegetation thresholds for assessing land use and land use changes in Cambodia using the Google Earth Engine cloud-computing platform

57Citations
N/AReaders
Get full text

Expansion dynamics of deciduous rubber plantations in Xishuangbanna, China during 2000–2010

38Citations
N/AReaders
Get full text

Classification of nemoral forests with fusion of multi-temporal sentinel-1 and 2 data

36Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Kou, W., Liang, C., Wei, L., Hernandez, A. J., & Yang, X. (2017). Phenology-based method for mapping tropical evergreen forests by integrating of MODIS and landsat imagery. Forests, 8(2). https://doi.org/10.3390/f8020034

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 35

65%

Researcher 13

24%

Lecturer / Post doc 4

7%

Professor / Associate Prof. 2

4%

Readers' Discipline

Tooltip

Environmental Science 24

47%

Earth and Planetary Sciences 12

24%

Agricultural and Biological Sciences 11

22%

Engineering 4

8%

Article Metrics

Tooltip
Social Media
Shares, Likes & Comments: 1

Save time finding and organizing research with Mendeley

Sign up for free