Seasonal variation of soil respiration under different land use/land cover in arid region

Through combining the soil respiration with the main environmental factors under the planting shelterbelt (Populus woodland) and the natural desert vegetation (Tamarix ramosissima Phragmites communis community and Haloxylon ammodendron community) in the western Junngar Basin, the difference in soil respiration under different land use/land cover types and the responses of soil respiration to temperature and soil moisture were analyzed. Results showed that the rate of soil respiration increased with temperature. During the daytime, the maximum soil respiration rate occurred at 18:00 for the Populus woodland, 12:00 for T. ramosissima Ph. communis community, and 14:00 for H. ammodendron community, while the minimum rate all occurred at 8:00. The soil respiration, with the maximum rate in June and July and then declining from August, exhibited a similar trend to the near-surface temperature from May to October. During the growing season, the mean soil respiration rates and seasonal variation differed among the land use/land cover types, and followed the order of Populus woodland >T. ramosissima Ph. communis community > H. ammodendron community. The difference in the soil respiration rate among different land use/land cover types was significant. The soil respiration of Pouplus woodland was significantly correlated with the near-surface temperature and soil temperature at 10 cm depth (P < 0.01) in an exponential manner. The soil respiration of T. ramosissima Ph. communis and H. ammodendron communities were all linearly correlated with the near-surface temperature and soil surface temperature (P < 0.01). Based on the near-surface tempera-ture, the calculated Q10 of Populus woodland, T. ramosissima Ph. communis community and H. ammodendron community were 1.48, 1.59 and 1.63, respectively. The integrated soil respiration of the three land use/land cover types showed a significant correlation with the soil moisture at 0―5 cm, 5― 15 cm and 0―15 cm depths (P < 0.01). The quadratic model could best describe the relationship between soil respiration and soil moisture at 0―5 cm depth (P < 0.01).     

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR,MODIS and Landsat data using the BFAST algorithm

Annual land use land cover(LULC) change information at medium spatial resolution(i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information,however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images(i.e., Moderate Resolution Imaging Spectroradiometer(MODIS) and Global Inventory Modelling and Mapping Studies(GIMMS) normalized difference vegetation index(NDVI)) with high spatial resolution datasets(China's Land-Use/cover Datasets(CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm(Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change.As a result, a nominal 30 m annual land use/land cover datasets(CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.     

Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau(1981―2006):Impacts of climate changes and human activities

Spatiotemporal variations of Chinese Loess Plateau vegetation cover during 1981-2006 have been investigated using GIMMS and SPOT VGT NDVI data and the cause of vegetation cover changes has been analyzed, considering the climate changes and human activities. Vegetation cover changes on the Loess Plateau have experienced four stages as follows: (1) vegetation cover showed a continued increasing phase during 1981―1989; (2) vegetation cover changes came into a relative steady phase with small fluctuations during 1990―1998; (3) vegetation cover declined rapidly during 1999―2001; and (4) vegetation cover increased rapidly during 2002―2006. The vegetation cover changes of the Loess Plateau show a notable spatial difference. The vegetation cover has obviously increased in the Inner Mongolia and Ningxia plain along the Yellow River and the ecological rehabilitated region of Ordos Plateau, however the vegetation cover evidently decreased in the hilly and gully areas of Loess Plateau, Liupan Mountains region and the northern hillside of Qinling Mountains. The response of NDVI to climate changes varied with different vegetation types. NDVI of sandy land vegetation, grassland and cultivated land show a significant increasing trend, but forest shows a decreasing trend. The results obtained in this study show that the spatiotemporal variations of vegetation cover are the outcome of climate changes and human activities. Temperature is a control factor of the seasonal change of vegetation growth. The increased temperature makes soil drier and unfavors vegetation growth in summer, but it favors vegetation growth in spring and autumn because of a longer growing period. There is a significant correlation between vegetation cover and precipitation and thus, the change in precipitation is an important factor for vegetation variation. The improved agricultural production has resulted in an increase of NDVI in the farmland, and the implementation of large-scale vegetation construction has led to some beneficial effect in ecology.     

Land cover changes based on plant successions: Deforestation,rehabilitation and degeneration of forest in the upper Dadu River watershed

The land use/cover classification system is the foundation for land use/cover change study. Remote sensing data were firstly used for land use and land cover classification in the United States in 1971 and the Anderson Classification System was proposed in 1976[1], which was put into use in mapping 1:250000 land use and land cover of the United States. A series of new land cover classification systems have been established in recent years through research projects such as FAO LCCS and Bi…     

A multi-resolution global land cover dataset through multisource data aggregation

Recent developments of 30 m global land characterization datasets(e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover mapping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC(Finer Resolution Observation and Monitoring-Global Land Cover) and FROM-GLC-seg(Segmentation), with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area(NL-ISA) and MODIS urban extent(MODIS-urban), to produce an improved 30 m global land cover map—FROM-GLC-agg(Aggregation). It was post-processed using additional coarse resolution datasets(i.e., MCD12Q1, GlobCover2009, MOD44 W etc.) to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion aggregation approaches were employed to create a multi-resolution hierarchy(i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map(at 30 m) and the three maps subsequently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.     

A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST

Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised classification approaches. This article used a fast clustering method—Clustering by Eigen Space Transformation(CBEST) to produce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clustered by combining CBEST and K-means in each pre-defined ecological zone(50 in total for China). Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test samples indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products(i.e., Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) and GlobCover 2009) also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.     

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

Land use changes and their relations with carbon cycles over the past 300 a in China

Land use and land cover in China have changed greatly during the past 300 a, indicated by the rapid abrupt decrease of forest land area and the rapid increase of cropland area, which can affect terrestrial carbon cycle greatly. The first-hand materials are used to analyze main characteristics for land use and land cover changes in China during the study period. The following conclusions can be drawn from this study. The cropland area in China kept increasing from 60.78×106 hm2 in 1661 to 96.09×106 hm2 in 1998. Correspondingly, the forest land area decreased from 248.13×106 hm2 in 1700 to 109.01×106 hm2 in 1949. Affected by such changes, the terrestrial ecosystem carbon storage decreased in the mean time. Car-bon lost from land use and land cover changes mainly consist of the loss from vegetation biomass and soil. In the past 300 a, about 3.70 PgC was lost from vegetation biomass, and emissions from soil ranged from 0.80 to 5.84 PgC. The moderate evaluation of soil losses was 2.48 PgC. The total loss from vegetation and soil was between 4.50 and 9.54 PgC. The moderate and optimum evaluation was 6.18 PgC. Such carbon losses distribution varied spatially from region to region. Carbon lost more significantly in Northeast China and Southwest China than in other regions, because losses of forest land in these two regions were far greater than in the other regions during the past 300 a. And losses of carbon in the other regions were also definite, such as Inner Mongolia, the western part of South China, the Xinjiang Uygur Autonomous Region, and the Qinghai-Tibet Plateau. But the carbon lost very little from the traditional agricultural regions in China, such as North China and East China. Studies on the relationship between land use and land cover change and carbon cycle in China show that the land use activities, especially those related to agriculture and forest management, began to affect terrestrial carbon storage positively in recent years.     

Continuous land cover change monitoring in the remote sensing big data era

<正>Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems(Verburg et al.,2015).A set of national and global scale land cover/use products with higher spatial and temporal resolutions have been developed to fill this gap.In China,existing efforts include China’s National     

Carbon emissions induced by cropland expansion in Northeast China during the past 300 years

Assessments of the impacts of land use and land cover changes(LUCC) on the terrestrial carbon budget, atmospheric CO2 concentration, and CO2-related climatic change are important to understand the environmental effects of LUCC and provide information about the effects of historical carbon emissions. Using regional land cover reconstructions from historical records, with a bookkeeping model, we estimated the carbon sink changes caused by historical cropland expansion in Northeast China during the past 300 years. The conclusions are as follows:(1) There was a dramatic land reclamation of cropland during the past 300 years in Northeast China. Approximately 26% of the natural land was cultivated, and 38% of the grassland and 20% of the forest and shrubland were converted to cropland.(2) The carbon emission induced by cropland expansion between 1683 and 1980 was 1.06–2.55 Pg C, and the estimation from the moderate scenario was 1.45 Pg C. The carbon emissions of the soil carbon pool was larger than that from the vegetation carbon pool and comprised more than 2/3 of the total carbon emissions.(3) The carbon emissions of the three provinces in Northeast China were different. Heilongjiang Province had the largest carbon emissions, and Jilin Province had the second largest emissions.(4) The primary source of carbon emissions was forest reclamation(taking 60% of the total emissions in the moderate scenario), the secondary source was grassland cultivation(taking 27%), and the tertiary sources were shrubland and wetland reclamation(taking 13%). Examination on the data accuracy revealed that the high-resolution regional land cover data allowed the carbon budget to be evaluated at the county level and improved the precision of the results. The carbon emission estimation in this study was lower than those in previous studies because of the improved land use data quality and various types of land use change considered.     

A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data

AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface tem-perature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM,the difference of different frequen-cies can eliminate the influence of water in soil and atmosphere,and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately,the land surface should be at least classified into three types:water covered surface,snow covered surface,and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm,we built different equations for different ranges of temperature. The average land surface temperature er-ror is about 2―3℃ relative to the MODIS LST product.     

Effects of future land use change on the regional climate in China

Land use and land cover change(LUCC)is one of the important human forcing on climate.However,it is difficult to infer how LUCC will affect climate in the future from the effects of previous LUCC on regional climates in the past.Thus,based on the land cover data recommended by the Coupled Model Intercomparison Project Phase 5(CMIP5),a regional climate model(Reg CM4)was used to investigate the climate effects of future land use change over China.Two 15-year simulations(2036–2050),one with the current land use data and the other with future land cover scenario(2050)were conducted.It is noted that future LUCC in China is mainly characterized by the transition from the grassland to the forest.Results suggest that the magnitudes and ranges of the changes in temperature and precipitation caused by future LUCC show evident seasonality,which are more prominent in summer and autumn.Significant response of climate to future LUCC mainly happens in Northeast China,North China,the Hetao Area,Eastern Qinghai-Tibetan Plateau and South China.Further investigation shows that future LUCC can also produce significant impacts on the atmospheric circulation.LUCC results in abnormal southwesterly wind over extensive areas from the Indian peninsula to the coasts of the South China Sea and South China through the Bay of Bengal.Furthermore,Indian tropical southwest monsoons and South Sea southwest monsoons will both be strong,and the abnormal water vapor convergence from the South China Sea and the Indian Ocean will result in more precipitation in South China.     

Simulating the impacts of climate variation and land-cover changes on basin hydrology: A case study of the Suomo basin

1 Motivation In the summer of 1998, areas along the middle and lower reaches of the Yangtze River suffered a damag- ing flood. Causes of the flooding became a hot topic on mass media after the disaster. Deforestation on the upstream areas was widely blamed as the major reason for the flooding. Some scientists, however, disproved the point of view. They believed that the impact of land use and land cover changes (LUCC) was over- stated[1]. Actually, the controversy over forest hydrol- ogy h…     

Global desert variation under climatic impact during 1982-2020

Deserts are important landscapes on the earth and their variations have impacts on global climate through feedback processes.However,there is a limited understanding of the climatic controls on the spatial and temporal variations of global deserts.Here,we use climate reanalysis datasets,global land use/land cover(LULC)products and the CMIP6(Coupled Model Intercomparison Project)model outputs to calculate the changing of global deserts during 1982-2020,and estimate future spatial trends of global...     

Carbon emissions induced by farmland expansion in China during the past 300 years

Scientific assessment of the accounting over carbon in the terrestrial ecosystem in the process land use/land cover changes caused by human activities will help reduce the uncertainty in estimating carbon emissions from the terrestrial ecosystem. This study employs a bookkeeping model to estimate the carbon emissions from farmland reclamation in China during the past 300 years based on the annual rate of land use changes(derived from historical natural vegetation, farmland data), preset carbon density and coefficients of disturbance curves. We find out that:(1) there was a net increase of 79.30×10~4km~2 in national farmland; about 65% of reclaimed farmland had been forest land and 26% of that had been grass land previously;(2) the total amount of carbon emissions from farmland expansion in China had been between 2.94 and 5.61 Pg with the median 3.78 Pg during the past 300 years; specifically, carbon emissions of vegetation were 1.58 Pg while those of soil ranged from 1.35 Pg to4.03 Pg with the median 2.20 Pg;(3) carbon emissions vary greatly across various ecosystems: the emissions were most from forest land, and then grass land and swamps, and the least from shrubs; deserts functioned more likely to be carbon stock in the process of land reclamation;(4) along the time line, carbon emissions had decreased first and then increased while the peak emissions occurred in the first half of 20 th century; and spatially, carbon emissions were most released in Northeast and Southwest China; Northwest China was of the minimum carbon emissions.