High temporal-and spatial-resolution green vegetation coverage generation and its application in soil erosion monitoring

2019 ◽  
Vol 39 (15) ◽  
Author(s):  
方天纵 FANG Tianzong ◽  
秦朋遥 QIN Pengyao ◽  
王黎明 WANG Liming ◽  
李晓松 LI Xiaosong
Keyword(s):  
Soil Erosion ◽  
Spatial Resolution ◽  
Vegetation Coverage ◽  
Green Vegetation ◽  
Temporal And Spatial

Study on Dynamic Changes of Soil Erosion in the North and South Mountains of Lanzhou

2021 ◽  
Author(s):  
Hua Zhang ◽  
Jinping Lei ◽  
Cungang Xu ◽  
Yuxin Yin
Keyword(s):  
Soil Erosion ◽  
Environmental Factors ◽  
Water Area ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
The North ◽  
Joint Influence ◽  
Bare Land ◽  
Temporal And Spatial ◽  
North And South

Abstract This study takes the north and south mountains of Lanzhou as the study area, calculates the soil erosion modulus of the north and south mountains of Lanzhou based on the five major soil erosion factors in the RUSLE model and analyzes the temporal and spatial dynamic changes of soil erosion and the characteristics of soil erosion under different environmental factors. The results show that the soil erosion intensity of the north and south mountains of Lanzhou is mainly micro erosion in 1995, 2000, 2005, 2010, 2015 and 2018. They are distributed in the northwest and southeast of the north and south mountains. Under different environmental factors, the soil erosion modulus first increased and then decreased with the increase of altitude; the soil erosion modulus increased with the increase of slope; the average soil erosion modulus of grassland and woodland was larger, and the average soil erosion modulus of water area was the smallest; except for bare land, the average soil erosion modulus decreased with the increase of vegetation coverage. The soil erosion modulus in the greening range is lower than that outside the greening scope, mainly the result of the joint influence of precipitation, soil and vegetation.

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

scholarly journals Effect of Forest Management Operations on Aggregate-Associated SOC Dynamics Using a 137Cs Tracing Method

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 859
Author(s):  
Geng Guo ◽  
Xiao Li ◽  
Xi Zhu ◽  
Yanyin Xu ◽  
Qiao Dai ◽  
...  
Keyword(s):  
Forest Management ◽  
Soil Erosion ◽  
Management Practices ◽  
Negative Impact ◽  
Dynamic Change ◽  
Aggregate Size ◽  
Nutrient Loss ◽  
Vegetation Coverage ◽  
Forest Conversion ◽  
Mountain Areas

Although forest conversions have long been a focus in carbon (C) research, the relationship between soil erosion and the dynamic change of soil organic carbon (SOC) has not been well-quantified. The objective of this study was to investigate the effects of converting CBF (coniferous and broad-leaved mixed forests) to economic forests, including CF (chestnut forest), HF (hawthorn forest), and AF (apple forest), on the soil structure and nutrient loss in the Huaibei Rocky Mountain Areas, China. A 137Cs tracer method was used to provide soil erosion data in order to quantify the loss of aggregate-associated SOC. The results showed that forest management operations caused macro-aggregates to decrease by 1.69% in CF, 4.52% in AF, and 3.87% in HF. Therefore, the stability of aggregates was reduced. The SOC contents in each aggregate size decreased significantly after forest conversion, with the largest decreases occurring in AF. We quantified the loss of 0.15, 0.38, and 0.31 Mg hm−2 of aggregate-associated SOC after conversion from CBF to CF, AF, and HF, respectively. These results suggest that forest management operations have a negative impact on soil quality and fertility. CF has better vegetation coverage and less human interference, making it more prominent among the three economic forests species. Therefore, when developing forest management operations, judicious selection of tree varieties and appropriate management practices are extremely critical. In addition, measures should be taken to increase surface cover to reduce soil erosion and achieve sustainable development of economic forests.

scholarly journals Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Yahui Guo ◽  
Jing Zeng ◽  
Wenxiang Wu ◽  
Shunqiang Hu ◽  
Guangxu Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Linear Trend ◽  
Spectral Characteristics ◽  
Growth Conditions ◽  
Temporal Changes ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
Spatial And Temporal Changes ◽  
Temporal And Spatial

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

Potential of Arabic documentary sources for reconstructing past climate in the western Mediterranean region from AD 680 to 1815

The Holocene ◽  
2021 ◽  
pp. 095968362110332
Author(s):  
Yassin Meklach ◽  
Chantal Camenisch ◽  
Abderrahmane Merzouki ◽  
Ricardo Garcia Herrera
Keyword(s):  
Spatial Resolution ◽  
Mediterranean Region ◽  
Climate Extremes ◽  
Western Mediterranean ◽  
Historical Documents ◽  
Atmospheric Conditions ◽  
Documentary Sources ◽  
Drought Conditions ◽  
Temporal And Spatial ◽  
Climate Proxies

Archival records and historical documents offer direct observation of weather and atmospheric conditions and have the highest temporal and spatial resolution, and precise dating, of the available climate proxies. They also provide information about variables such as temperature, precipitation and climate extremes, as well as floods, droughts and storms. The present work studied Arab-Islamic documentary sources covering the western Mediterranean region (documents written by Arab-Islamic historians that narrate social, political and religious history) available for the period AD 680–1815. They mostly provide information on hydrometeorological events. In Iberia the most intense droughts were reported during AD 747–753, AD 814–822, AD 846–847, AD 867–874 and AD 914–915 and in the Maghreb AD 867–873, AD 898–915, AD 1104–1147, AD 1280–1340 and AD 1720–1815 had prevalent drought conditions. Intense rain episodes are also reported.

Regulation and Critical Threshold of Grass Cover on Slope Runoff in LoessHilly Gully Region under Artificial

2021 ◽  
Author(s):  
Qiufen Zhang ◽  
Xizhi Lv ◽  
Rongxin Chen ◽  
Yongxin Ni ◽  
Li Ma
Keyword(s):  
Soil Erosion ◽  
Rainfall Intensity ◽  
Climatic Conditions ◽  
Vegetation Coverage ◽  
Critical Threshold ◽  
Runoff Generation ◽  
Grassland Vegetation ◽  
Slope Flow ◽  
The Impact ◽  
Slope Runoff

<p>The slope runoff caused by rainstorm is the main cause of serious soil and water loss in the loess hilly area, the grassland vegetation has a good inhibitory effect on the slope runoff, it is of great significance to reveal the role of grassland vegetation in the process of runoff generation and control mechanism for controlling soil erosion in this area. In this study, typical grassland slopes in hilly and gully regions of the loess plateau were taken as research objects. Through artificial rainfall in the field, the response rules of slope rainfall-runoff process to different grass coverage were explored. The results show that: (1) The time for the slope flow to stabilize is prolonged with the increase of vegetation coverage, and shortened with the increase of rainfall intensity; (2) At 60 mm·h <sup>−1</sup> rainfall intensity, the threshold of grassland vegetation coverage is 75.38%; at 90 mm·h<sup> −1</sup> rainfall intensity, the threshold of grassland vegetation coverage is 90.54%; at 120 mm·h <sup>−1</sup> rainfall intensity, the impact of grassland vegetation coverage on runoff is not significant; (3) the Reynolds number and Froude number of slope flow are 40.07‒695.22 and 0.33‒1.56 respectively, the drag coefficient is 1.42‒43.53. Under conditions of heavy rainfall, the ability of grassland to regulate slope runoff is limited. If only turf protection is considered, about 90% of grassland coverage can effectively cope with soil erosion caused by climatic conditions in loess hilly and gully regions. Therefore, in loess hilly areas where heavy rains frequently occur, grassland's protective effect on soil erosion is obviously insufficient, and investment in vegetation measures for trees and shrubs should be strengthened.</p>

scholarly journals Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

2018 ◽  
Vol 10 (10) ◽  
pp. 3459
Author(s):  
Shu-Di Fan ◽  
Yue-Ming Hu ◽  
Lu Wang ◽  
Zhen-Hua Liu ◽  
Zhou Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Factor Model ◽  
Original Method ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Modified Model ◽  
The North ◽  
Moderate Resolution Imaging Spectroradiometer

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

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