Yellow River Delta Area Research Articles

Mapping soil salinity using a similarity-based prediction approach: A case study in Huanghe River Delta, China

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

Sustainable Development Model and Simulation for Yellow River Delta Based on Land Use Information

Along with the rapid development of the Yellow River delta's economy, the contradiction between population, environment, and resource is highlighted, the land problem has more and more influence to the human survival, and the sustainable use of the land becomes necessary for the coordination and getting along with the human and the natural envi- ronment. Based on the land information, we analysis and simulate the sustainable development of the Yellow River delta area. By analysis the system structure of land use and the relationship between each subsystem, we construct a dynamic model of regional sustainable development based on land information. Then put the yellow triangle area of Binzhou city as a case to simulate the development models under different constraint conditions of land use. Using the principle of sys- tem dynamics to simulate the dynamic change of land using under study area, account for the regional sustainable devel- opment system, quantify the influence from the land using change on the sustainable development of the yellow triangle area, and predict the evolution trend of studying regional land using in the future.

Evolution of the Yellow River Delta and its relationship with runoff and sediment load from 1983 to 2011

Summary Long-term data from a hydrological monitoring station and remotely-sensed satellite images were used to explore the effects of runoff and suspended sediment load on evolution of the Yellow River Delta (YRD) from 1983 to 2011. During this period, an average runoff of 18 × 10 9 m 3 yr −1 and an average sediment load of 341 × 10 6 t yr −1 flowed through the delta lobes into the sea. The runoff and sediment load exhibited downward trends with time, along with large inter-annual fluctuations. Three stages were evident in the data. From 1983 to the late 1990s, the Yellow River experienced progressively severe droughts which reduced both runoff and sediment load to its delta lobe. The delta nevertheless grew to a peak area of about 3950 km 2 in 2000. From 2000 to 2003, the YRD area decreased. Meanwhile, the operation of the dam at Xiaolangdi and changes in water consumption driven by a new regulatory framework helped stabilize the runoff. Although the sediment load continued to decline, partly due to sediment check dams along the middle Yellow River and the reduced sediment carrying capacity of the river, the YRD area nevertheless increased between 2003 and 2011. The variations in runoff and sediment load directly influenced changes to the plan-form area, shoreline migration, and morphology of the YRD. From 1983 to 2011, the net land area of the delta increased by 248 km 2 , its coastline extended by 36.45 km, and its shape became increasingly irregular due to the emergence of its delta lobes. In 1996, an artificial diversion altered the position of the main delta lobe from Qingshuigou to Qing 8. A stepwise multiple regression analysis indicated that the YRD would have required average sediment loads of about 441 × 10 6 t yr −1 before 1996 and 159 × 10 6 t yr −1 after 1996 to maintain equilibrium.

Fluvial system development and subsequent marine transgression in Yellow River (Huanghe) delta and its adjacent sea regions during last glacial maximum to early Holocene

Paleotopography of the Yellow River (Huanghe) delta area and the western Bohai Sea during the last glacial maximum (LGM) is important to understand fluvial activities of the Yellow River linked with the LGM climate. By integrating data sets of both onshore and offshore borehole cores and offshore high-resolution seismic profiles, we reconstructed the paleotopography of this area from the LGM to the early Holocene. The fluvial sediment facies of the LGM identified in these cores was characterized by poorly sorted medium- to coarse-grained sands, which shows chaotic patterns in seismic profiles. REE characteristics and clay mineral components of the fluvial sediments suggest that they were derived mostly from the paleo-Yellow River. The basal and top bounding surfaces of the fluvial sediments slope very gently toward the northeast, similar to the present morphology of the North China Plain formed by the Yellow River. No incised valley morphology is detected in the basal topography, because of the long distance from the study area to the paleoshoreline during the LGM, and also because of the very gentle gradient of the paleo-Yellow River from the northern part of North China Plain to the continental shelf area, with concave-upward morphology. Aggradational stacking of the fluvial sediments over the entire North China Plain and in the study area indicates that the Yellow River flowed in these areas during the LGM to the early Holocene. The Holocene marine and coastal sediments onlap onto the underlying fluvial sediments. These basal marine or brackish sediments are diachronous from offshore areas of the Bohai Sea to the Yellow River delta area, with older sediments in the east and younger sediments in the west, which clearly reflects the early Holocene marine transgression from the North Yellow Sea to the Bohai Sea.

Land Cover Classification of Quad-Polarimetric RADARSAT-2 SAR Image Based on Modified Subspace Method

Compared with conventional single-polarization synthetic aperture radar (SAR), quad-polarimetric SAR observations provide more information and show potential for land cover classification. In this study, we propose a modified subspace method (MOSM) for quad-polarimetric SAR image classification, which incorporates the advantages of the learning subspace method (LSM), the averaged learning subspace method (ALSM) and the multiple similarity method (MSM). To evaluate the recognition accuracy of the proposed method, we carry out experiments with quad-polarimetric RADARSAT-2 images for the Yellow River Delta area. We compare the results obtained using the MOSM algorithm with those obtained by the traditional supervised Wishart method. According to our analyses, the results obtained by the proposed method show a 10% higher accuracy rate than the results based on the classical supervised Wishart method, and the overall accuracy rate for the proposed method exceeds 87%, which indicates a good performance. Therefore, the modified method is an alternative classification method for quad-polarimetric SAR image.Résumé. Par rapport au radar à synthèse d’ouverture (RSO) monopolarisation traditionnel, les observations RSO entièrement polarimétriques << quad-polarimetric >> fournissent plus d’informations et montrent un potentiel pour la classification de la couverture terrestre. Dans cet article, nous proposons une méthode de sous-espace modifié (MOSM) pour la classification des images RSO entièrement polarimétriques, qui intègre les avantages de la méthode LSM << learning subspace method >>, de la méthode ALSM << averaged learningsubspace method >> et de la méthode MSM << multiple similarity method >>. Pour évaluer la précision de la reconnaissance de la méthode proposée, nous effectuons des expériences avec des images RADARSAT-2 entièrement polarimétriques pour le delta du fleuve Jaune. Nous comparons les résultats obtenus en utilisant l’algorithme MOSM avec ceux obtenus par la méthode supervisée classique de Wishart. Selon nos analyses, les résultats obtenus par la méthode proposée montrent une précision 10% plus élevée que les résultats basés sur la méthode supervisée classique de Wishart et la précision globale de la méthode proposée dépasse 87%, ce qui indique une bonne performance. Par conséquent, la méthode modifiée est une méthode de classification alternative pour les images RSO entièrement polarimétriques.

Characteristics and Spatial Variability of Saline-Alkaline Soil Degradation in the Typical Yellow River Delta Area of Kenli County, China

As an important area of reserve land resources, the Yellow River Delta is faced with the problem of soil salinization. Grasping the characteristics of soil salinity as well as its spatial variation patterns is an important foundation of prevention, control and utilization of saline soil. This study selected Kenli County of the Yellow River Delta, obtained soil salinity data through field survey and lab experiment, and used statistical, GIS interpolation and buffer analysis methods to analyze the characteristics of soil salinity and its spatial variation patterns. Our results showed that the general soil salinity in the study area was mainly moderate and there was a significant positive correlation between different soil layers of 0 - 15 cm, 15 - 30 cm and 30 - 45 cm and soil salinity increased with the increase of soil depth. The areas with high soil salinity in each soil layer mainly distributed in the east near the Bo Sea in the county, while the areas with lower soil salinity mainly distributed in the southwest, centre and the two sides of the Yellow River in the northeast. Soil salinity showed a trend of decrease with the increase in distance to the Bo Sea, while stretching from the Yellow River, it showed increase tendency with the increase in distance to the Yellow River. The order from high soil salinity to low of different vegetation types was naked land → suaeda glauca → tamarix → vervain → reed → couch grass → paddy → cotton → winter wheat → maize; the order for different geomorphic types was depression → slightly sloping ground → slow hillock → high flood land. This study preliminary delineated the characteristics of soil salinity as well as its spatial variation patterns in the study area, and provided scientific basis for soil resource sustainable utilization in the Yellow River Delta.

Retrieving groundwater in Yellow River Delta area using remote sensing

The geographical position of the Yellow River Delta is unique and there is a sharp contradiction between the supply and the demand of water resources.In order to study its groundwater distribution,the authors adopted MODIS satellite remote sensing data to measure soil moisture and groundwater level.The relative soil moisture was estimated by using temperature vegetation dryness index(TVDI) and apparent thermal inertia(ATI) methods.The correlation between the soil moisture at different depths and the groundwater level was analyzed,which helped to get the linear equations and calculate the groundwater depth distribution.A comparison with the measured groundwater level data shows that it is feasible to retrieve groundwater distribution by using MODIS data,and 10 cm is the best depth for the inversion of relative soil water content and groundwater level in the study area.In case when the soil moisture data are lacking,we can estimate the groundwater depth distribution by using the factors which can reflect the relative soil water content.

Soil organic carbon storage changes in coastal wetlands of the modern Yellow River Delta from 2000 to 2009

Abstract. Soil carbon sequestration plays an essential role in mitigating atmospheric CO2 increases and the subsequently global greenhouse effect. The storages and dynamics of soil organic carbon (SOC) of 0–30 cm soil depth in different landscape types including beaches, reservoir and pond, reed wetland, forest wetland, bush wetland, farmland, building land, bare land (severe saline land) and salt field in the modern Yellow River Delta (YRD) were studied based on the data of the regional survey and laboratory analysis. The landscape types were classified by the interpretation of remote sensing images of 2000 and 2009, which were calibrated by field survey results. The results revealed an increase of 10.59 km2 in the modem YRD area from 2000 to 2009. The SOC density varied ranging from 0.73 kg m−2 to 4.25 kg m−2 at depth of 0–30 cm. There were approx. 3.559 × 106 t and 3.545 × 106 t SOC stored in the YRD in 2000 and 2009, respectively. The SOC storages changed greatly in beaches, bush wetland, farm land and salt field which were affected dominantly by anthropogenic activities. The area of the YRD increased greatly within 10 years, however, the small increase of SOC storage in the region was observed due to landscape changes, indicating that the modern YRD was a potential carbon sink and anthropogenic activity was a key factor for SOC change.

Effects of N, P, and K Fertilizer Application on Cotton Growing in Saline Soil in Yellow River Delta

This study aimed at guiding fertilizer management for Bt cotton cultivar SCRC28 growing in coastal saline soil in the Yellow River Delta area in Shandong Province, China. SCRC28 was planted in the soils with high, middle, and low salinity under 3 fertilizer treatments, namely control (no fertilizers), NPK, NP, and NK. The effects of different fertilization treatments on nutrient assimilation, Na + assimilation, net photosynthetic rate ( P n), dry matter accumulation, and lint yield of Bt cotton were investigated. Compared to the corresponding controls, the NP and NPK treatments had significantly higher nutrient uptake levels in plants in the 3 types of saline soils, and significantly lower levels of Na + uptake, especially the NPK treatment. The nutrient use efficiencies in agronomy of cotton in the NPK treatment were the highest among treatments regardless the salinity level. The N use efficiencies in agronomy were 0.20, 1.95, and 2.07 kg lint kg −1 under the low, middle, and high salinity level, respectively; the P use efficiencies in agronomy were 0.87, 8.35, and 8.71 kg lint kg −1, respectively; and the K use efficiencies in agronomy were 0.26, 2.89, and 3.77 kg lint kg −1, respectively. The NPK treatment also maintained the highest levels of leaf area, chlorophyll content, and P n among fertilization treatments in the 3 types of saline soils. The NPK treatment obtained the highest biomass and lint yield among the treatments, and the link yields were enhanced, compared to the corresponding controls, by 2.53%, 28.67%, and 30.47% in the low, middle, and high salinity soils, respectively. The effect of fertilization was obviously in the middle or high salinity fields. In this study, the fertilization quantities were based on the salinity level of soil. The NPK treatments were applied with N 165 kg ha −1 plus P 2O 5 38.57 kg ha −1 plus K 2O 111.5 kg ha −1 in the middle salinity soil and N 135 kg ha −1 plus P 2O 5 32.14 kg ha −1 plus K 2O 74.35 kg ha −1 in the high salinity soil. Therefore, rational fertilization schemes are recommended according to the salinity level of soil in order to alleviate nutrition obstacles and improve cotton nutrition, which ultimately result in the increases of nutrient use efficiencies in agronomy and cotton yield.

Application of the SEBAL method in water resources management in the Yellow River Delta of China

Evapotranspiration (ET) from agricultural fi elds, a consumptive use of fresh water for irrigation, is an important factor in water resources management in the Yellow River Delta (YRD) of China. Because the Surface Energy Balance Algorithm for Land (SEBAL) methodology requires a less ground measurements and includes the complete energy balance calculation, the algorithm was used to estimate ET as a component of the energy balance on a pixel-by-pixel basis in the YRD. Advanced Very High Resolution Radiometer and eight Landsat images covering the YRD were used to estimate both daily and monthly actual ET (ET a ) with the SEBAL method. The net crop water requirements (WRn) for the irrigated land were also estimated by subtracting the effective precipitation from the potential evapotranspiration (ET p ). The volume of net crop water requirements was obtained for the YRD area jointly with the digital crop maps and WRn values. Assessment of the performance of irrigated crops revealed that water delivery did not exceed the crop requirements, and the WRn for cotton and wheat were smaller than the irrigation demand. The result confirmed that the SEBAL model can estimate the ET a and ET p with higher accuracy, which will be an important tool in integrated water resources management and can provide spatial information to help decision-makers improve the current water use policies in the YRD.

Soil salt content estimation in the Yellow River delta with satellite hyperspectral data

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487–1527, 1971–1991, 2032–2092, and 2163–2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area.