MODIS Enhanced Vegetation Index Data Research Articles

Magnitude and direction of green-up date in response to drought depend on background climate over Mongolian grassland

Increasing drought is one major consequence of ongoing global climate change and is expected to cause significant changes in vegetation phenology, especially for naturally vulnerable ecosystems such as grassland. However, the linkage between the response characteristic of green-up date (GUD) to drought and background climate remains largely unknown. Here, we focused on how the GUD of Mongolian grassland responds to extreme drought events (EDE). We first extracted the GUD from the MODIS Enhanced Vegetation Index data during 2001–2020 and identified the preseason EDE using the standardized precipitation evapotranspiration index data. Subsequently, we quantified the response of GUD to preseason EDE (DGUD) in each pixel as the difference in GUD between drought and normal years. The effect of 12 factors on DGUD was analyzed using the random forest algorithm. The results showed that the GUD under EDE may delay or advance by > 20 days compared to normal years. For the regions with mean annual temperature > −2 °C, the GUD was delayed under EDE due to the dominant role of water restriction on GUD, while the GUD was advanced under EDE in colder areas due to the warmer temperature during drought. However, the magnitude of delay in GUD under drought was greater in regions with less precipitation and more severe droughts. Our results could help to develop appropriate management strategies to mitigate the impacts of drought on grasslands.

Effects of Effective Precipitation and Accumulated Temperature on the Terrestrial EVI (Enhanced Vegetation Index) in the Yellow River Basin, China

To identify the vegetation dynamics and relationship with the hydrothermal conditions in the Yellow River basin (YRB), the spatial–temporal variations of EVI, effective precipitation (Epr), accumulated temperature (At), and their relationships were obtained based on the MODIS EVI data and meteorological data from the YRB during 2001–2020. The results indicate that EVI trends increased during 2001 to 2020, especially in the farmland, forestland, and grassland ecosystems. Epr and At have also increased over the last 20 years. Epr mostly increased faster in the grassland, and water bodies and wetland ecosystems. At mostly increased faster in the water bodies and wetland, desert, and forest ecosystems. Affected by Epr and At, the correlation between the EVI and hydrothermal conditions varied under different hydrothermal conditions. Compared to the At, the Epr was the restrictive factor for the EVI variations in the terrestrial ecosystem in the YRB. In addition, the dynamical thresholds of the EVI, Epr, and At were confirmed. This study can improve the understanding of vegetation variations and their response to regional climate change, which is critical for ecological conservation and the high-quality development of the YRB.

Modeling endangered mammal species distributions and forest connectivity across the humid Upper Guinea lowland rainforest of West Africa

Species distribution data provide critical baseline information for conservation planning and decision making. However, in many of the Earth’s most biodiverse regions, such data are lacking for many species. Here, we used ecological niche modeling and connectivity analyses to model distributions of endangered species and protected area connectivity across the Upper Guinea Forest (UGF) Global Conservation Hotspot of West Africa. We estimated the current distributions of African forest elephant Loxodonta cyclotis (Vulnerable), western chimpanzee Pan troglodytes verus (Critically Endangered), and pygmy hippopotamus Choeropsis liberiensis (Endangered) across the region and optimized connectivity in two main forest complexes in the region. We used occurrence data for the period 2010–2016 for the three species from two well-sampled national parks in Liberia (Sapo National Park and Gola National Park), and remotely sensed MODIS enhanced vegetation index data for the period 2010–2015. Our models predicted a total of 75,157 km2 of suitable habitat for chimpanzees in the region, 79,400 km2 for elephants, and 290,696 km2 for hippos. Of these areas, for chimpanzees, 30% of the area predicted falls within the boundaries of proposed or designated protected areas, and likewise 30% for elephants, and 19% for hippos. Liberia had the largest blocks of contiguous forest suitable for these species compared to other countries in the region but this forest was largely unprotected. This study identifies priority areas for biodiversity conservation and forest connectivity in the region, and reemphasizes the practicality of these tools to optimize conservation planning and implementation.

A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data

Agricultural land use and cropping patterns are closely related to food production, soil degradation, water resource management, greenhouse gas emission, and regional climate alterations. Methods for reliable and cost-efficient mapping of cropping pattern, as well as their changes over space and time, are therefore urgently needed. To cope with this need, we developed a phenology-based method to map cropping patterns based on time-series of vegetation index data. The proposed method builds on the well-known ‘threshold model’ to retrieve phenological metrics. Values of four phenological parameters are used to identify crop seasons. Using a set of rules, the crop season information is translated into cropping pattern. To illustrate the method, cropping patterns were determined for three consecutive years (2008–2010) in the Henan province of China, where reliable validation data was available. Cropping patterns were derived using eight-day composite MODIS Enhanced Vegetation Index (EVI) data. Results show that the proposed method can achieve a satisfactory overall accuracy (~84%) in extracting cropping patterns. Interestingly, the accuracy obtained with our method based on MODIS EVI data was comparable with that from Landsat-5 TM image classification. We conclude that the proposed method for cropland and cropping pattern identification based on MODIS data offers a simple, yet reliable way to derive important land use information over large areas.

Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data

Abstract Stretching over 4300 km north to south, Chile is a special country with complicated landscapes and rich biodiversity. Accurate and timely updated land cover map of Chile in detailed classification categories is highly demanded for many applications. A conclusive land cover map integrated from multi-seasonal mapping results and a seasonal dynamic map series were produced using Landsat 8 imagery mainly acquired in 2013 and 2014, supplemented by MODIS Enhanced Vegetation Index data, high resolution imagery on Google Earth, and Shuttle Radar Topography Mission DEM data. The overall accuracy is 80% for the integrated map at level 1 and 73% for level 2 based on independent validation data. Accuracies for seasonal map series were also assessed, which is around 70% for each season, greatly improved by integrated use of seasonal information. The importance of growing season imagery was proved in our analysis. The analysis of the spatial variation of accuracies among various ecoregions indicates that the accuracy for land cover mapping decreases gradually from central Chile to both north and south. More mapping efforts for those ecoregions are needed. In addition, the training dataset includes sample points spatially distributed in the whole country, temporally distributed throughout the year, and categorically encompassing all land cover types. This training dataset constitutes a universal sample set allowing us to map land cover from any Landsat 8 image acquired in Chile without additional ad hoc training sample collection.

MODIS Enhanced Vegetation Index data do not show greening of Amazon forests during the 2005 drought

MODIS Enhanced Vegetation Index data do not show greening of Amazon forests during the 2005 drought