A STUDY OF SOIL CONSERVATION MONITORING INFORMATION SYSTEM BASED ON REMOTELY SENSED DATA FOR A CATCHMENT ON THE LOESS PLATEAU

The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.

Study on Soil Salinization Information in Arid Region Using Remote Sensing Technique

Extracting information about saline soils from remote sensing data is useful, particularly given the environmental significance and changing nature of these areas in arid environments. One interesting case study to consider is the delta oasis of the Weigan and Kuqa rivers, China, which was studied using a Landsat Enhanced Thematic Mapper Plus （ETM＋） image collected in August 2001. In recent years, decision tree classifiers have been successfully used for land cover classification from remote sensing data. Principal component analysis （PCA） is a popular data reduction technique used to help build a decision tree; it reduces complexity and can help the classification precision of a decision tree to be improved. A decision tree approach was used to determine the key variables to be used for classification and ultimately extract salinized soil from other cover and soil types within the study area. According to the research, the third principal component （PC3） is an effective variable in the decision tree classification for salinized soil information extraction. The research demonstrated that the PC3 was the best band to identify areas of severely salinized soil; the blue spectral band from the ETM＋ sensor （TM1） was the best band to identify salinized soil with the salt-tolerant vegetation of tamarisk （Tamarix chinensis Lour）; and areas comprising mixed water bodies and vegetation can be identified using the spectral indices MNDWI （modified normalized difference water index） and NDVI （normalized difference vegetation index）. Based upon this analysis, a decision tree classifier was applied to classify landcover types with different levels of soil saline. The results were checked using a statistical accuracy assessment. The overall accuracy of the classification was 94.80%, which suggested that the decision tree model is a simple and effective method with relatively high precision.

Hyperspectral Inversion Model of Available Potassium Content in Red Soil of Eucalyptus Plantation in Northern Guangxi

Soil information is the basis of soil management and precise variable fertilization. The traditional method of obtaining soil information through chemical detection of laboratory has high cost and poor timeliness, which is difficult to meet the needs of digital forestry, soil monitoring and real-time management of nutrients. Taking red soil of Eucalyptus plantation in northern Guangxi as the research object, the spectral data of samples with different soil available potassium contents were measured, and the spectral characteristics were analyzed, and the inversion model was established by using PLS method. The results showed that the spectral sensitive bands of available potassium content in red soil of the region mainly concentrated in 400-600, 1 450, 2 200 nm and so on. After the first derivative transformation, the redundant information in the original spectral data can be significantly reduced, and the correlation between spectral indexes and soil available potassium content can be improved. The full-band modeling results of R and FDR were better than those of significant bands. The optimal model was full-band-FDR-PLS, R2=0.862, and RMSE=2.718. The results of this study can be used for the application of near-earth remote sensing in Guangxi, such as soil digital mapping, precise variable fertilization and real-time monitoring of soil available potassium.

基于LoRa的农业大田土壤多参数监测系统设计

农业大田中农作物在不同的生长阶段对水分和养分的需求不同,为了对精准施肥提供有效数据,设计实现了基于LoRa的农业大田土壤多测点多参数监测系统,在实时监测土壤温湿度和养分变化的基础上,实现按需测土配方的精准施肥。监测系统由数据采集、传输以及测土配方服务管理系统组成。部署在大田中的多传感器采集节点采用LoRa低功耗无线传输网络以载波监听从机轮询的方式,实时传输土壤温湿度、PH值、氮、磷和钾等数据到网关节点,网关节点通过4G移动互联网将数据传输至服务器。系统在田间测试表明,多终端节点和网关节点距离1~2 km时,通信的成功率可达到90%以上,距离超过2 km时通信成功率达86.3%,比采用LoRa自组网协议的通信成功率提高了20.5%。田间监测结果显示,同一地块不同位置的土壤墒情和养分有明显的差异,系统不但可以监测土壤墒情和养分数据,还可提供测土配方的精准施肥方案,有较强的实际应用价值。

Limited Spatial Transferability of the Relationships Between Kriging Variance and Soil Sampling Spacing in Some Grasslands of Ireland:Implications for Sampling Design

Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.

Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agro-ecological characteristics of Hungary

The 4M crop model was used to investigate the prospective effects of climate change on the agro-ecological characteristics of Hungary.The model was coupled with a detailed meteorological database and spatial soil information systems covering the whole territory of Hungary.Plant-specific model parameters were determined by inverse modeling.Future meteorological data were produced from the present meteorological data by combining a climate change scenario and a stochastic weather generator.Using the available and the generated data,the present and the prospective agro-ecological characteristics of Hungary were determined.According to the simulation results,average yields will decrease considerably(-30%)due to climate change.The rate of nitrate leaching will prospectively decrease as well.The fluctuations of both the yields and the annual nitrate leaching rates will most likely increase approaching the end of the twenty-first century.On the basis of the simulation results,the role of autumn crops is likely to become more significant in Hungary.The achieved results can be generalized for more extended regions based on the concept of spatial(geographical)analogy.

Spatio-temporal analysis of flowering using Li DAR topography

Spatio-temporal patterns of flowering in forest ecosystems are hard to quantify and monitor. The objectives of this study were to investigate spatio-temporal patterns（e.g. soilssimple slope classesslope aspectand flow accumulation） of flowering around Lake IssaqueenaSouth Carolina（SCUSA） using plant-flowering database collected with GPS- enabled camera（stored in Picasa 3 web albums and project website） on a monthly basis in 2012 and Li DAR-based topography. Pacolet fine sandy loam had the most flowering plantsfollowed by Madison sandy loamboth dominant soil types around the lake. Most flowering plants were on moderately steep（17%–30%） and gently sloping（4%–8%） slopes. Most flowering plants were on west（247.5°–292.5°）southwest（202.5°–247.5°）and northwest（292.5°–337.5°） aspects. Most flowering plants were associated with minimum and maximum flows within the landscape. Chi-square tests indicated differences in the distributions of the proportions of flowering plants were significant by soil typeslopeaspectand flow accumulation for each month（February-November）for all months（overall）and across months. The Chi-square test on area-normalized data indicated significant differences for all months and individual differences by each month with some months not statistically significant. Cluster analysis on flowering counts for nine plant families with the most flowering counts indicated no unique separation by clusterbut implied that the majority of these families were flowering on strongly sloping（9%–16%） slopeson southwest（202.5°–247.5°） aspectsand low flow accumulation（0–200）. Presented methodology can serve as a template for future efforts to quantify spatio-temporal patterns of flowering and other phenological events.