scholarly journals Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging †

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5374
Author(s):  
Lei Ding ◽  
Zhenwang Li ◽  
Xu Wang ◽  
Ruirui Yan ◽  
Beibei Shen ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Vegetation Index ◽  
Regional Scale ◽  
Carbon Stocks ◽  
Estimation Accuracy ◽  
Regression Kriging ◽  
Meadow Steppe ◽  
Model Accuracy ◽  
Study Region ◽  
Typical Steppe

Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R2) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m2) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R2 = 0.63, MAE = 7.04 gC/m2, and RMSE = 8.51 gC/m2), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R2 = 0.72, MAE = 8.09 gC/m2, and RMSE = 9.89 gC/m2) was higher than that of the LR method (R2 = 0.70, MAE = 8.99 gC/m2, and RMSE = 10.69 gC/m2). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R2 = 0.69, MAE = 7.40 gC/m2, and RMSE = 9.01 gC/m2, while the LR method reaches the highest model accuracy of R2 = 0.53, MAE = 9.20 gC/m2, and RMSE = 11.10 gC/m2. The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 × 104 Mg C, and the mean carbon stock density was 47.44 gC/m2. The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.

scholarly journals Changing Trends of NDVI and Their Responses to Climatic Variation in Different Types of Grassland in Inner Mongolia from 1982 to 2011

2019 ◽  
Vol 11 (12) ◽  
pp. 3256 ◽  
Author(s):  
Jie Yang ◽  
Zhiqiang Wan ◽  
Suld Borjigin ◽  
Dong Zhang ◽  
Yulong Yan ◽  
...  
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Climatic Variation ◽  
Future Research ◽  
Desert Steppe ◽  
Vegetation Density ◽  
Meadow Steppe ◽  
Typical Steppe ◽  
Changing Trends ◽  
Different Types

Normalized difference vegetation index (NDVI) is commonly used to indicate vegetation density and condition. NDVI was mostly correlated with climate factors. We analyzed changing trends of NDVI in different types of grassland in Inner Mongolia and the response of NDVI to climatic variation from 1982 to 2011. NDVI of meadow steppe increased significantly in spring while it decreased in other seasons. The annual mean NDVI in typical steppe and desert steppe increased significantly in the last 30a. However, in the greatest area of steppe desert, the NDVI had no significant change in summer, autumn, and the growing season. In meadow steppe, typical steppe, and desert steppe, the area showed a positive correlation of NDVI to temperature as highest in spring compared to other seasons, because warming in spring is beneficial to the plant growth. However, in the greatest area of steppe desert, the correlation of NDVI to temperature was not significant. The NDVI was positively correlated to precipitation in four types of grassland. In the steppe desert, the precipitation had no significant effect on the NDVI due to the poor vegetation cover in this region. The NDVI was not significantly correlated to the precipitation in autumn because of vegetation withering in the season and not need precipitation. Precipitation was a more important factor rather than temperature to NDVI in the region. The response of NDVI to temperature and precipitation in different seasons should be studied in more detail and the effect of other factors on NDVI should be considered in future research.

scholarly journals Geospatial modeling approaches for mapping topsoil organic carbon stock in northern part of Mongolia

2019 ◽  
pp. 4-17
Author(s):  
Samdandorj M ◽  
Purevdorj Ts
Keyword(s):  
Organic Carbon ◽  
Root Mean Square Error ◽  
Geographically Weighted Regression ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Weighted Regression ◽  
Mean Square ◽  
Regression Kriging ◽  
Soc Stock ◽  
Organic Carbon Stock

Soil organic carbon (SOC) is one of the most important indicators of soil quality and agricultural productivity. This paper presents the application of Regression Kriging (RK), geographically weighted regression (GWR) and Geographically Weighted Regression Kriging (GWRK) for prediction of topsoil organic carbon stock in Tarialan. A total of 25 topsoil (0-30 cm) samples were collected from Tarialan soum of Khuvsgul aimag in Mongolia. In this study, seven independent variables were used including normalised difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalised difference moisture index (NDMI), land surface temperature (LST) and terrain factors (DEM, Slope, Aspect). We used root-mean-square error (RMSE), mean error (ME) and determination coefficient (R2) to evaluate the performance of these methods. Validation results showed that performance of the GWRK, GWR, and RK approaches were good with not only low values of root-mean-square error (1.38 kg/m2, 1.48 kg/m2, 0.69 kg/m2), mean error (0.28 kg/m2, -0.22 kg/m2, 0.17 kg/m2) but also high values of R2 (0.76, 0.72, 0.94). The estimated SOC stock values ranged from 0.28-16.26 kg/m2, 0.72–15.24 kg/m2, 0.16–15.83 kg/m2 using GWRK, GWR, RK approaches in the study area. The highest average SOC stock value was in the wetland (6.47 kg/m2, 6.08 kg/m2, 6.44 kg/m2) and the lowest was in cropland (1.63 kg/m2, 1.48 kg/m2, 1.80 kg/m2) using these approaches. According to the validation, GWRK, GWR, and RK approaches produced satisfactory results for estimating and mapping SOC stock. However, Regression Kriging was the best model, followed by GWRK and GWR to predict topsoil organic carbon stock in Tarialan.

scholarly journals Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010

2018 ◽  
Vol 7 (8) ◽  
pp. 290 ◽  
Author(s):  
Jun Wang ◽  
Tiancai Zhou ◽  
Peihao Peng
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Northern China ◽  
Plant Phenology ◽  
Meadow Steppe ◽  
Typical Steppe ◽  
Alpine Steppe

Because the dynamics of phenology in response to climate change may be diverse in different grasslands, quantifying how climate change influences plant growth in different grasslands across northern China should be particularly informative. In this study, we explored the spatiotemporal variation of the phenology (start of the growing season [SOS], peak of the growing season [POS], end of the growing season [EOS], and length of the growing season [LOS]) across China’s grasslands using a dataset of the GIMMS3g normalized difference vegetation index (NDVI, 1985–2010), and determined the effects of the annual mean temperature (AMT) and annual mean precipitation (AMP) on the significantly changed phenology. We found that the SOS, POS, and EOS advanced at the rates of 0.54 days/year, 0.64 days/year, and 0.65 days/year, respectively; the LOS was shortened at a rate of 0.62 days/year across China’s grasslands. Additionally, the AMT combined with the AMP explained the different rates (ER) for the significantly dynamic SOS in the meadow steppe (R2 = 0.26, p = 0.007, ER = 12.65%) and typical steppe (R2 = 0.28, p = 0.005, ER = 32.52%); the EOS in the alpine steppe (R2 = 0.16, p < 0.05, ER = 6.22%); and the LOS in the alpine (R2 = 0.20, p < 0.05, ER = 6.06%), meadow (R2 = 0.18, p < 0.05, ER = 16.69%) and typical (R2 = 0.18, p < 0.05, ER = 19.58%) steppes. Our findings demonstrated that the plant phenology in different grasslands presented discrepant dynamic patterns, highlighting the fact that climate change has played an important role in the variation of the plant phenology across China’s grasslands, and suggested that the variation and relationships between the climatic factors and phenology in different grasslands should be explored further in the future.

scholarly journals KARAKTERISTIK SPASIAL DAN POTENSI CADANGAN KARBON DI BENTANG ALAM MBELILING NUSA TENGGARA TIMUR

2021 ◽  
Vol 21 (2) ◽  
pp. 65
Author(s):  
Serlina H. Oktian ◽  
Luluk Setyaningsih ◽  
Nengsih Anen ◽  
Wahyu C. Adinugroho
Keyword(s):  
Aboveground Biomass ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Model Development ◽  
Belowground Biomass ◽  
Carbon Stocks ◽  
Carbon Pools ◽  
Average Value ◽  
Comprehensive Information ◽  
Index Value

Providing comprehensive information on carbon stock data on all carbon pools needs to be done to plan and measure climate change mitigation efforts that are carried out. This research was conducted by analyzing spatial characteristics and estimating carbon stocks with model development. Spatial analysis is carried out to provide an overview of the distribution of spatial values that can use the built model. Estimation of carbon stock is carried out by building a carbon stock estimator model that correlates the value of remote sensing parameters with the value of carbon stocks in all carbon storage sources. The characteristics of the vegetation index value in the forest category are greater than in the non-forest category and vice versa for the distribution of the digital number average value. The model development is only carried out on aboveground biomass and belowground biomass carbon pools. The results of the analysis of the estimation of carbon stocks based on the selected model showed the potential for aboveground biomass was 5,200,841.45 tC and the potential for belowground biomass was 1,317,948.10 tC.

scholarly journals Contributions of Climatic Factors to Interannual Variability of the Vegetation Index in Northern China Grasslands

2020 ◽  
Vol 33 (1) ◽  
pp. 175-183 ◽  
Author(s):  
Wei Zhao ◽  
Zhongmin Hu ◽  
Qun Guo ◽  
Genan Wu ◽  
Ruru Chen ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Air Temperature ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Explanatory Power ◽  
Regional Scale ◽  
Temperate Grasslands ◽  
Study Region ◽  
Vegetation Activities

AbstractUnderstanding the atmosphere–land surface interaction is crucial for clarifying the responses and feedbacks of terrestrial ecosystems to climate change. However, quantifying the effects of multiple climatic factors to vegetation activities is challenging. Using the geographical detector model (GDM), this study quantifies the relative contributions of climatic factors including precipitation, relative humidity, solar radiation, and air temperature to the interannual variation (IAV) of the normalized difference vegetation index (NDVI) in the northern grasslands of China during 2000 to 2016. The results show heterogeneous spatial patterns of determinant climatic factors on the IAV of NDVI. Precipitation and relative humidity jointly controlled the IAV of NDVI, illustrating more explanatory power than solar radiation and air temperature, and accounting for higher proportion of area as the determinant factor in the study region. It is noteworthy that relative humidity, a proxy of atmospheric aridity, is as important as precipitation for the IAV of NDVI. The contribution of climatic factors to the IAV of NDVI varied by vegetation type. Owing to the stronger explanatory power of climatic factors on NDVI variability in temperate grasslands, we conclude that climate variability may exert more influence on temperate grasslands than on alpine grasslands. Our study highlights the importance of the role of atmospheric aridity to vegetation activities in grasslands. We suggest focusing more on the differences between vegetation types when addressing the climate–vegetation relationships at a regional scale.

scholarly journals Estimation of Carbon Stock Stands using EVI and NDVI Vegetation Index in Production Forest of Lembah Seulawah Sub-District, Aceh Indonesia

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Jhon Pandapotan Situmorang ◽  
Sugianto Sugianto ◽  
Darusman .
Keyword(s):  
Regression Analysis ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Close Correlation ◽  
Carbon Stocks ◽  
Total Carbon ◽  
Coefficient Of Determination ◽  
Average Value ◽  
Vegetation Indexes ◽  
Aceh Province

This study aims to determine the distribution of the vegetation indexes to estimate the carbon stocks of forest stands in the Production Forest of Lembah Seulawah sub-district. Aceh Province, Indonesia. A non-destructive method using allometric equations and landscape scale method were applied, where in carbon stocks at the points of samples are correlated with the index values of each transformation of the vegetation indexes; EVI and NDVI.  Results show that EVI values of study area from 0.05 to 0.90 and NDVI values from 0.17 to 0.85. The regression analysis between EVI with carbon stock value of sample locations equation is Y = 151.7X-39.76. with the coefficient of determination (R2) is 0.83. From this calculation, the total carbon stocks in the Production Forest area of Lembah Seulawah sub-district using EVI is estimated 790.344.41 tonnes, and the average value of carbon stocks in average is 51.48 tons per hectare.  Regression analysis between NDVI values at the research locations for the carbon stack measured samples is Y = 204.Xx-102.1 with coefficient of determination (R2) is 0.728. Total carbon stocks in production forest of Lembah Seulawah sub-district using NDVI is estimated 711.061.81 tones. and the average value of carbon stocks is 46.32 tons per hectare. From the above results it can be concluded that the vegetation indexes: EVI and NDVI are vegetation indexed that have a very close correlation with carbon stocks stands estimation. The correlation between EVI with carbon stock and the correlation between NDVI with carbon stock is not significantly different

scholarly journals INCREASING ACCURACY VALUE IN THE ESTIMATES OF CARBON STOCK BY USING VEGETATION INDEX FROM ALOS AVNIR 2 SATELLITE IMAGERY

2018 ◽  
Author(s):  
Ketut Wikantika
Keyword(s):  
Global Warming ◽  
Satellite Imagery ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Correlation Equation ◽  
Carbon Stocks ◽  
Exact Equation ◽  
The Public ◽  
Modified Soil

The existence of carbon stock began to be noticed by the public, especially after the global warming phenomenon, because one of the causes of global warming is the increasing amount of carbon in the atmosphere. There are several approaches that can be used to calculate carbon stocks, one of which is through remote sensing. In the study of carbon stocks in Meru Betiri National Park Indonesia, the vegetation index from ALOS-AVNIR satellite imagery is used to estimate carbon reserves by finding an exact equation. If it uses the Modified Soil Adjusted Vegetation Index (MSAVI) only, the correlation value is 0.49. Meanwhile, if Infrared Percentage Vegetation Index (IPVI) is used, the correlation value is 0.47. However, if some vegetation indices such as Soil-Adjusted Vegetation Index (SAVI), Normalize Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) are combined, the correlation value of the equation is 0.63. The comparison showed that by combining several variables of vegetation indices will increase the value of the correlation equation significantly.

Geospatial modeling approaches for mapping topsoil organic carbon stock in northern part of Mongolia

2020 ◽  
Author(s):  
Samdandorj Manaljav ◽  
Purevdorj Tserengunsen
Keyword(s):  
Organic Carbon ◽  
Root Mean Square Error ◽  
Geographically Weighted Regression ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Weighted Regression ◽  
Mean Square ◽  
Regression Kriging ◽  
Soc Stock ◽  
Organic Carbon Stock

&lt;p&gt;Soil organic carbon (SOC) is one of the most important indicators of soil quality and agricultural productivity. This paper presents the application of Regression Kriging (RK), Geographically Weighted Regression (GWR) and Geographically Weighted Regression Kriging (GWRK) for prediction of topsoil organic carbon stock in Tarialan. A total of 25 topsoil (0-30 cm) samples were collected from Tarialan soum of Khuvsgul aimag in Mongolia. In this study, seven independent variables were used including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalized difference moisture index (NDMI), land surface temperature (LST) and terrain factors (DEM, Slope, Aspect). We used root mean square error (RMSE), mean error (ME) and determination coefficient (R&lt;sup&gt;2&lt;/sup&gt;) to evaluate the performance of these methods. Validation results showed that performance of the GWRK, GWR, and RK approaches were good with not only low values of root-mean-square error (1.38 kg m&lt;sup&gt;-2&lt;/sup&gt;, 1.48 kg m&lt;sup&gt;-2&lt;/sup&gt;, 0.69 kg m&lt;sup&gt;-2&lt;/sup&gt;), mean error (0.28 kg m&lt;sup&gt;-2&lt;/sup&gt;, -0.22 kg m&lt;sup&gt;-2&lt;/sup&gt;, 0.17 kg m&lt;sup&gt;-2&lt;/sup&gt;) but also high values of R&lt;sup&gt;2&lt;/sup&gt; (0.76, 0.72, 0.94). The estimated SOC stock values ranged from 0.28-16.26 kg m&lt;sup&gt;-2&lt;/sup&gt;, 0.72&amp;#8211;15.24 kg m&lt;sup&gt;-2&lt;/sup&gt;, 0.16&amp;#8211;15.83 kg m&lt;sup&gt;-2&lt;/sup&gt; using GWRK, GWR, RK approaches in the study area. The highest average SOC stock value was in the wetland (6.47 kg m&lt;sup&gt;-2&lt;/sup&gt;, 6.08 kg m&lt;sup&gt;-2&lt;/sup&gt;, 6.44 kg m&lt;sup&gt;-2&lt;/sup&gt;) and the lowest was in cropland (1.63 kg m&lt;sup&gt;-2&lt;/sup&gt;, 1.48 kg m&lt;sup&gt;-2&lt;/sup&gt;, 1.80 kg m&lt;sup&gt;-2&lt;/sup&gt;) using these approaches. According to the validation, GWRK, GWR, and RK approaches produced satisfactory results for estimating and mapping SOC stock. However, Regression Kriging was the best model, followed by GWRK and GWR to predict topsoil organic carbon stock in Tarialan.&lt;/p&gt;

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals Soil Mycobiome Is Shaped by Vegetation and Microhabitats: A Regional-Scale Study in Southeastern Brazil

2021 ◽  
Vol 7 (8) ◽  
pp. 587
Author(s):  
Danielle Hamae Yamauchi ◽  
Hans Garcia Garces ◽  
Marcus de Melo Teixeira ◽  
Gabriel Fellipe Barros Rodrigues ◽  
Leila Sabrina Ullmann ◽  
...  
Keyword(s):  
Fungal Community ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Deciduous Forest ◽  
Regional Scale ◽  
Southeastern Brazil ◽  
Transitional Region ◽  
Illumina Platform ◽  
Ecological Processes ◽  
Life On Earth

Soil is the principal habitat and reservoir of fungi that act on ecological processes vital for life on Earth. Understanding soil fungal community structures and the patterns of species distribution is crucial, considering climatic change and the increasing anthropic impacts affecting nature. We evaluated the soil fungal diversity in southeastern Brazil, in a transitional region that harbors patches of distinct biomes and ecoregions. The samples originated from eight habitats, namely: semi-deciduous forest, Brazilian savanna, pasture, coffee and sugarcane plantation, abandoned buildings, owls’ and armadillos’ burrows. Forty-four soil samples collected in two periods were evaluated by metagenomic approaches, focusing on the high-throughput DNA sequencing of the ITS2 rDNA region in the Illumina platform. Normalized difference vegetation index (NDVI) was used for vegetation cover analysis. NDVI values showed a linear relationship with both diversity and richness, reinforcing the importance of a healthy vegetation for the establishment of a diverse and complex fungal community. The owls’ burrows presented a peculiar fungal composition, including high rates of Onygenales, commonly associated with keratinous animal wastes, and Trichosporonales, a group of basidiomycetous yeasts. Levels of organic matter and copper influenced all guild communities analyzed, supporting them as important drivers in shaping the fungal communities’ structures.

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