Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

Senf, Cornelius; Pflugmacher, Dirk; Linden, Sebastian van der; Hostert, Patrick

doi:10.3390/rs5062795

Cited by 102 publications

(101 citation statements)

References 49 publications

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“…For mapping regions where there is heterogeneous vegetation cover, it is necessary to find the variables most effective for classification. Each vegetation type in each season, was representative of different vegetation, soil, and water conditions [44], so the phenology-based indices were found effective for classifying these vegetation cover types [15,18,69,70].…”

Section: Temporal Indices Of Land Cover Classes and Relevant Variablementioning

confidence: 99%

“…At each node, a number of variables (mtry) were randomly sampled from a random subset of the features, and 100 runs were performed. To estimate the accuracy of trees, the out-of-bag prediction was used to estimate the accuracy of all trees, which represents an unbiased estimate of map accuracy, as long as the reference data were obtained via probability sampling [18]. To verify the classification result, we used the confusion matrix to estimate overall, user's and producer's accuracies, and the kappa coefficient [66,67].…”

Section: Classification Algorithm Using Rf and Validationmentioning

confidence: 99%

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Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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Abstract:Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

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Section: Temporal Indices Of Land Cover Classes and Relevant Variablementioning

confidence: 99%

Section: Classification Algorithm Using Rf and Validationmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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“…MODIS is extensively used in Land Use and Land Cover Change (LULCC) studies including crop and plantation monitoring [5,17,18]. For instance, inter-annual time-series MODIS Enhanced Vegetation Index (EVI) and Short Wave Infrared (SWIR) have been efficiently used to identify rubber plantations in tropical mountainous regions [19].…”

Section: Introductionmentioning

confidence: 99%

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Kou

Liang

Wei

et al. 2017

Forests

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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.

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“…Since the late 20th century, in search of rapid socio-economic growth, many countries and regions developed at the cost of natural resources and eco-environment, leading to shocking eco-environment problems, among which there were quite a few typical examples for vegetation destruction (Richardson et al, 2007;Hreško et al, 2009;Rastmanesh et al, 2010;Miettinen et al, 2011;Koh et al, 2011;Cui et al, 2012;Hinojosa-Huerta et al, 2013;Senf et al, 2013;Mao et al, 2014;William, 2014;Zhou et al, 2014). AO (1995) reported that during 1980-1990 there was 9.95 Â 10 4 km 2 of forest lost annually, almost equivalent to the area of South Korea.…”

Section: Human Economic Activities and Regional Vegetation Destructionmentioning

confidence: 99%

Spatial-temporal vegetation succession in Yao’an County, Yunnan Province, Southwest China during 1976–2014: A case survey based on RS technology for mountains eco-engineering

Huang

Yuan

et al. 2014

Ecological Engineering

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Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

Cited by 102 publications

References 49 publications

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Spatial-temporal vegetation succession in Yao’an County, Yunnan Province, Southwest China during 1976–2014: A case survey based on RS technology for mountains eco-engineering

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