Comparison of temporal and spatial changes in three major tropical forests based on MODIS data

Numerous studies have shown that intact tropical forests account for half of the total terrestrial sink for anthropogenic carbon dioxide.Here,we analyzed and compared changes in three main tropical forest regions from 2000 to 2014,based on time-series analysis and landscape metrics derived from moderate-resolution imaging spectroradiometer data.We examined spatialpattern changes in percentage of tree cover and net primary production(NPP)for three tropical forest regions—Amazon basin,Congo basin,and Southeast Asia.The results show that:the Amazon basin region had the largest tropical forest area and total NPP and a better continuity of TC distribution;the Southeast Asia region exhibited a sharp decrease in NPP and had comparatively separate spatial patterns of both TC and NPP;and the Congo basin region exhibited a dramatic increase in NPP and had better aggregation of forest NPP distribution.Results also show that aggregative patterns likely correlate with high NPP values.

Understanding the spring phenology of Arctic tundra using multiple satellite data products and ground observations

The Arctic is highly sensitive to climate change,and the rise in its near-surface air temperatures has been almost twice the global average.The increased growth of the Arctic tundra and its changing seasonality have been observed,largely in response to the impacts of climate change.In this study,we investigated the temporal and spatial variations of the start of the growing season(SOS)using various remote sensing indices,including Normalized Difference Vegetation Index,Normalized Difference Water Index,and Normalized Difference Snow Index from 2000 to 2018 in Arctic tundra regions.The SOS was derived at 29 sites from ground observations,including CO2 flux data,phenological images,and field records that were used to validate the SOS from remote sensing indices.Our results revealed that the SOS was delayed by approximately 3.86 days per degree of latitude along the northward latitudinal gradient.From 2000 to 2018,the start of the growing season and the interannual variability differed greatly among tundra types.Although the overall trends were not significant from 2000 to 2018,the start of the growing season in different plant communities was consistently delayed after 2016.High Arctic vegetation,including(1)low wetland complexes(5–10 cm)dominated by sedges,grasses,and mosses,and(2)slightly higher prostrate and hemi-prostrate shrubs(<15 cm),experienced a delayed start of the growing season.The start of the growing season of Low Arctic vegetation,comprising(1)wetland complexes(10–40 cm)dominated by sedges,grasses,mosses,and dwarf shrubs,(2)moist tundra(20–50 cm)dominated by tussock cottongrass and dwarf shrubs,and(3)transition zones containing tundra and taiga,displayed no obvious trend.

Research on SAR data integrated processing methodology oriented on earth environment factor inversions

As an important advanced technique in the field of Earth observations,Synthetic Aperture Radar(SAR)plays a key role in the study of global environmental change,resources exploration,disaster mitigation,urban environments,and even lunar exploration.However,studies on imaging,image processing,and Earth factor inversions have often been conducted independently for a long time,which significantly limits the application effectiveness of SAR remote sensing due to the lack of an overall integrated design scheme and integrated information processing.Focusing on this SAR application issue,this paper proposes and describes a new SAR data processing methodology–SAR data integrated processing(DIP)oriented on Earth environment factor inversions.The simple definition,typical integrated modes and overall implementation ideas are introduced.Finally,focusing on building information extraction(man-made targets)and sea ice classification(natural targets)applications,three SAR DIP methods and experiments are conducted.Improved results are obtained under the guidance of the SAR DIP framework.Therefore,the SAR DIP theoretical framework and methodology represent a new SAR science application mode that has the capability to improve the SAR remote sensing quantitative application level and promote the development of new theories and methodologies.