Time Series MODIS and in Situ Data Analysis for Mongolia Drought

Dorjsuren, Munkhzul; Liou, Yuei An; Cheng, Chi-Han

doi:10.3390/rs8060509

Cited by 39 publications

(27 citation statements)

References 36 publications

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“…This approach takes advantages of the high-revisit characteristics of the MODIS sensor, which increase the possibility of capturing good quality images from optical sensors. Annual MODIS time-series have been used in previous research [3,12,[31][32][33], and most of them are not in tropical mountainous regions. It should be mentioned, however, that the coarse spatial resolution (500 m) may produce some inaccurate results from mixing pixels of small-area land cover types, such as narrow rivers.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Several researchers are currently monitoring drought conditions in Mongolian grassland using satellite-derived indices [1,19,20], and meaningful results have been obtained. As detailed above, other drought indices have performed well in other countries and climate zones.…”

Section: Introductionmentioning

confidence: 99%

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Chang

Niu

et al. 2017

Remote Sensing

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Abstract:In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices for accurate and real-time expression of grassland drought in Mongolia. Firstly, an adaptability analysis was performed by comparing nine remote sensing-derived drought indices with reference indicators obtained from field observations using several methods (correlation, consistency percentage (CP), and time-space analysis). The reference information included environmental data, vegetation growth status, and region drought-affected (RDA) information at diverse scales (pixel, county, and region) for three types of land cover (forest steppe, steppe, and desert steppe). Second, a meteorological index (PED), a normalized biomass (NorBio) reference indicator, and the RDA-based drought CP method were adopted for describing Mongolian drought. Our results show that in forest steppe regions the normalized difference water index (NDWI) is most sensitive to NorBio (maximum correlation coefficient (MAX_R): up to 0.92) and RDA (maximum CP is 87%), and is most consistent with RDA spatial distribution. The vegetation health index (VHI) and temperature condition index (TCI) are most correlated with the PED index (MAX_R: 0.75) and soil moisture (MAX_R: 0.58), respectively. In steppe regions, the NDWI is most closely related to soil moisture (MAX_R: 0.69) and the VHI is most related to the PED (MAX_R: 0.76), NorBio (MCC: 0.95), and RDA data (maximum CP is 89%), exhibiting the most consistency with RDA spatial distribution. In desert steppe areas, the vegetation condition index (VCI) correlates best with NorBio (MAX_R: 0.92), soil moisture (MAX_R: 0.61), and RDA spatial distribution, while TCI correlates best with the PED (MAX_R: 0.75) and the RDA data (maximum CP is 79%). The VHI is a combination of constructed VCI and TCI, and can be used instead of them. Finally, the mode method was adopted to identify appropriate drought indices. The best two indices (VHI and NDWI) can be utilized to develop a combination drought model for accurately monitoring and quantifying drought in the future. Additionally, the new framework can be adopted to investigate and analyze the suitability of satellite-derived drought indices and determine the most appropriate index/indices for other countries or areas.

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“…Besides, the MOD13A1 provides a piece of information about vegetation indices (i.e., normalized difference vegetation index and enhanced vegetation index) and its potential associated with climate changes (e.g., increasing frequency increase and drought severity). The vegetation responses can be observed through vegetation greenness changes with land-atmosphere water, carbon and energy fluxes, and linked climate feedbacks [24,30,85]. Additionally, the LST derived from MOD11A2 has been used as a key factor to examine drought severity [33,40,86] as a response of soil moisture and its texture [29].…”

Section: Datamentioning

confidence: 99%

“…However, NDVI has lower sensitivity than EVI, which limits it when being applied to the detection of the vegetation greenness in high biomass regions [44,45]. Several subsequent studies modified the DSI by replacing factors around the drought algorithm for EVI2 [30] or climate data derived from the advanced very high resolution radiometer (AVHRR) [25] and applied them for their typical regions. Hence, the analysis of the input factors that have a role in drought modeling is vital for the assessment of the drought status at the area of analysis.…”

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confidence: 99%

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Tran

Myint

et al. 2019

Remote Sensing

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Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001-2018 period, along with the Mann-Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

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Time Series MODIS and in Situ Data Analysis for Mongolia Drought

Cited by 39 publications

References 36 publications

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

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

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

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