scholarly journals Responses of the electron transfer capacity of soil humic substances to agricultural land-use types

RSC Advances ◽  
2018 ◽  
Vol 8 (57) ◽  
pp. 32588-32596 ◽  
Author(s):  
Beidou Xi ◽  
Zhurui Tang ◽  
Jie Jiang ◽  
Wenbing Tan ◽  
Caihong Huang ◽  
...  
Keyword(s):  
Land Use ◽  
Electron Transfer ◽  
Humic Substances ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Chemical Structures ◽  
Land Use Types ◽  
Electron Transfer Capacity

Agricultural land-use types could affect the transformation and decomposition of HS in soils, and thus further change the intrinsic chemical structures associated with ETC.

Validation of SWEEP for Contrasting Agricultural Land Use Types in the Tarim Basin

Soil Science ◽  
2014 ◽  
Vol 179 (9) ◽  
pp. 433-445 ◽  
Author(s):  
Huawei Pi ◽  
Gary Feng ◽  
Brenton S. Sharratt ◽  
Xinhu Li ◽  
Zehao Zheng
Keyword(s):  
Land Use ◽  
Tarim Basin ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Land Use Types

Decadal Changes in Subsoil Physical Properties as Affected by Agricultural Land Use Types in Korea

2018 ◽  
Vol 36 (4) ◽  
pp. 567-575
Author(s):  
Hee-Rae Cho ◽  
Yong-Seon Zhang ◽  
Kyung-Hwa Han ◽  
Jung-Hun Ok ◽  
Seon-Ah Hwang ◽  
...  
Keyword(s):  
Land Use ◽  
Physical Properties ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Land Use Types

scholarly journals Different types of agricultural land use drive distinct soil bacterial communities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shin Ae Lee ◽  
Jeong Myeong Kim ◽  
Yiseul Kim ◽  
Jae-Ho Joa ◽  
Seong-Soo Kang ◽  
...  
Keyword(s):  
Land Use ◽  
Bacterial Communities ◽  
Agricultural Land ◽  
Agricultural Management ◽  
Agricultural Land Use ◽  
Community Structures ◽  
Soil Bacterial Communities ◽  
Microbial Network ◽  
Land Use Types ◽  
Different Types

Abstract Biogeographic patterns in soil bacterial communities and their responses to environmental variables are well established, yet little is known about how different types of agricultural land use affect bacterial communities at large spatial scales. We report the variation in bacterial community structures in greenhouse, orchard, paddy, and upland soils collected from 853 sites across the Republic of Korea using 16S rRNA gene pyrosequencing analysis. Bacterial diversities and community structures were significantly differentiated by agricultural land-use types. Paddy soils, which are intentionally flooded for several months during rice cultivation, had the highest bacterial richness and diversity, with low community variation. Soil chemical properties were dependent on agricultural management practices and correlated with variation in bacterial communities in different types of agricultural land use, while the effects of spatial components were little. Firmicutes, Chloroflexi, and Acidobacteria were enriched in greenhouse, paddy, and orchard soils, respectively. Members of these bacterial phyla are indicator taxa that are relatively abundant in specific agricultural land-use types. A relatively large number of taxa were associated with the microbial network of paddy soils with multiple modules, while the microbial network of orchard and upland soils had fewer taxa with close mutual interactions. These results suggest that anthropogenic agricultural management can create soil disturbances that determine bacterial community structures, specific bacterial taxa, and their relationships with soil chemical parameters. These quantitative changes can be used as potential biological indicators for monitoring the impact of agricultural management on the soil environment.

scholarly journals Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series

2021 ◽  
Vol 13 (2) ◽  
pp. 289
Author(s):  
Misganu Debella-Gilo ◽  
Arnt Kristian Gjertsen
Keyword(s):  
Land Use ◽  
Time Series ◽  
Deep Learning ◽  
Agricultural Land ◽  
Satellite Image ◽  
Agricultural Land Use ◽  
Monitoring Method ◽  
Multiple Observations ◽  
Land Use Types ◽  
Sentinel 2

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.

scholarly journals Characteristics of Soil Parameters of Agricultural Land Use Types, Their Location and Development Forecast

Land ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 197 ◽  
Author(s):  
Jozef Vilček ◽  
Štefan Koco ◽  
Eva Litavcová ◽  
Stanislav Torma
Keyword(s):  
Land Use ◽  
Information Systems ◽  
Agricultural Land ◽  
Arable Land ◽  
Agricultural Land Use ◽  
Soil Parameters ◽  
Production Potential ◽  
Gravel Content ◽  
Permanent Grassland ◽  
Land Use Types

In this paper we point out the basic soil parameters characterizing current arable land, permanent grassland, vineyards, and orchards in Slovakia. While the area of permanent land use types is more or less stable, there is a noticeable decrease in the area of arable land. In Slovakia, arable land is located mainly on the plain. The value of its production potential is 67 points (the highest quality soil has 100 points). Permanent grassland is found at higher altitudes on slopes, with a higher gravel content, and the value of their production potential is 35 points. Vineyards are predominantly located in the warm regions of southern Slovakia on the middle slopes. These soils are generally loamy, without significant gravel content, and the value of their production potential is 59 points. Most orchards are located on the plains. The soils are predominantly loamy and deep, without significant gravel content, and the value of their production potential is 63 points. Characteristics of agricultural land use types were determined using vector databases of soil parameters obtained from Soil Science and Conservation Research Institute information systems and a current vector layer for identification of agriculturally used soils, the Land Parcel Identification System, using geographic information systems. Moreover, our analysis tries to determine what developments can be expected in the use of four agricultural land use types. The modeling assumptions concern the future performance of these variables using exponential smoothing and Box–Jenkins methodology.

scholarly journals Multi-Criteria Decision Analysis for the Land Evaluation of Potential Agricultural Land Use Types in a Hilly Area of Central Vietnam

Land ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 90 ◽  
Author(s):  
Ronja Herzberg ◽  
Tung Gia Pham ◽  
Martin Kappas ◽  
Daniel Wyss ◽  
Chau Thi Minh Tran
Keyword(s):  
Land Use ◽  
Decision Analysis ◽  
Local Knowledge ◽  
Agricultural Land ◽  
Research Area ◽  
Land Evaluation ◽  
Agricultural Land Use ◽  
Multi Criteria Decision Analysis ◽  
Land Use Types ◽  
Central Vietnam

Land evaluation is a process that is aimed at the sustainable development of agricultural production in rural areas, especially in developing countries. Therefore, land evaluation involves many aspects of natural conditions, economic, and social issues. This research was conducted in a hilly region of Central Vietnam to assess the land suitability of potential agricultural land use types that are based on scientific and local knowledge. In the frame of this research, Participatory Rural Appraisal (PRA); Analytical Hierarchy Analysis (AHP); Geographic Information System (GIS); and, scoring based scientific literature and local knowledge were applied for Multi-Criteria Decision Analysis (MCDA) for land use evaluation. The results of the PRA survey reveal that five plants offer great agricultural potential in the research area, namely rice, cassava, acacia, banana, and rubber. The land suitability of each plant type varies, depending on physical conditions as well as economic and social aspects. Acacia and cassava represent the most suitable plant types in the research area. Recommendations regarding agricultural land use planning in the A Luoi district are brought forward based on the land evaluation results. The combination of scientific and local knowledge in land assessment based on GIS technology, AHP, and PRA methods is a promising approach for land evaluation.

scholarly journals Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

2020 ◽  
Vol 12 (18) ◽  
pp. 2919
Author(s):  
Ann-Kathrin Holtgrave ◽  
Norbert Röder ◽  
Andrea Ackermann ◽  
Stefan Erasmi ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Optical Data ◽  
Agricultural Land Use ◽  
Land Use Type ◽  
Auxiliary Data ◽  
Land Use Types ◽  
Sentinel 2

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.

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