scholarly journals Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1430
Author(s):  
Yingying Li ◽  
Zhengyong Zhao ◽  
Sunwei Wei ◽  
Dongxiao Sun ◽  
Qi Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Forest Soil ◽  
Soil Nutrients ◽  
Predictive Power ◽  
Remote Sensing Data ◽  
Soil Nutrient ◽  
Soil Maps ◽  
Soil Layers ◽  
Coarse Resolution

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

scholarly journals RS & GIS-Based Spatialtemporal Analysis of Ecological Footprint and Biocapacity Pattern of Jinghe River Watershed in China: Does Supply Meet Demand?

2011 ◽  
Vol 356-360 ◽  
pp. 2820-2832
Author(s):  
Dong Xia Yue ◽  
Jin Hui Ma ◽  
Jian Jun Guo ◽  
Jia Jing Zhang ◽  
Jun Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Ecological Footprint ◽  
Spatial Scales ◽  
Supply And Demand ◽  
Remote Sensing Data ◽  
Spatiotemporal Dynamics ◽  
River Watershed ◽  
Per Capita ◽  
Jinghe River

The Ecological Footprint methodology is a framework that tracks Ecological Footprint (humanity’s demands on the biosphere) by comparing human demand against the regenerative capacity (Biocapacity) of the planet (WWF, 2010) to advance the science of sustainability. As such, the spatiotemporal dynamics of the Ecological Footprint (EF) and Biocapacity (BC) in a given watershed are important topics in the field of sustainability research based on remote sensing (RS) data and geographic information system (GIS) techniques．This paper reports on a case study of the Jinghe River Watershed using improved EF methodology with the help of GIS and high resolution remote sensing data, to quantitatively estimate the relationship between EF demand and BC supply and analyze their spatial distribution patterns at multiple spatial scales for four periods (1986, 1995, 2000 and 2008). We predict the future BC both overall, and of six categories of biological productivity area for the next four decades using the Markov Chain Method．The results showed that the spatial distribution of EF demand and BC supply were significantly uneven in the region, in which the per-capita EF of all counties located in the watershed increased continually from 1986 to 2008, and the EF per person of counties in the middle and lower reaches area was markedly greater than that in the upper reaches over time. On the supply side, the per-capita BC of all counties decreased gradually from 1986 to 2008, and the per-capita BC of counties in the upper reaches area was greater than that in the middle and lower reaches during the period, causing the uneven spatial distribution of Ecological budget-the gap between supply and demand, showed that the Jinghe River Watershed on the whole has begun to be unsustainable since 2008, with each county exhibiting differential temporal patterns. The prediction results showed that the total BC will increase continually from 2020 to 2050, and the BC of six categories will reduce, indicating that unsustainability in the region will escalate. As a whole, The EF demand has exceeded the BC supply, and the gap was widening in the Jinghe Watershed. This paper provided an in-depth portrait of the spatiotemporal dynamics of EF and BC, as well as their interactions with humanity and ecosystems.

scholarly journals Prediction of soil nutrient regime based on a model of DEM-generated clay content for the province of Nova Scotia, Canada

2013 ◽  
Vol 93 (2) ◽  
pp. 193-203 ◽  
Author(s):  
Zhengyong Zhao ◽  
M. Irfan Ashraf ◽  
Kevin S. Keys ◽  
Fan-Rui Meng
Keyword(s):  
Nova Scotia ◽  
High Resolution ◽  
Soil Nutrient ◽  
Clay Content ◽  
Parent Material ◽  
Forest Growth ◽  
Soil Maps ◽  
Large Area ◽  
Nutrient Regime ◽  
Coarse Resolution

Zhao, Z., Ashraf, M. I., Keys, K. S. and Meng, F-R. 2013. Prediction of soil nutrient regime based on a model of DEM-generated clay content for the province of Nova Scotia, Canada. Can. J. Soil Sci. 93: 193–203. Soil nutrient regime (SNR) maps are widely required by ecological studies as well as forest growth and yield assessment. Traditionally, SNR is assessed in the field using vegetation indicators, topography and soil properties. However, field assessments are expensive, time consuming and not suitable for producing high-resolution SNR maps over a large area. The objective of this research was to develop a new model for producing high-resolution SNR maps over a large area (in this case, the province of Nova Scotia). The model used 10-m resolution clay content maps generated from digital elevation model data to capture local SNR variability (associated with topography) along with coarse-resolution soil maps to capture regional SNR variability (associated with differences in landform/parent material types). Field data from 1385 forest plots were used to calibrate the model and another 125 independent plots were used for model validation. Results showed field-identified SNRs were positively correlated with predicted clay content, with some variability associated with different landform/parent material types. Accuracy assessment showed that 63.7% of model-predicted SNRs were the same as field assessment, with 96.5% within ±1 class compared with field-identified SNRs. The predicted high-resolution SNR map was also able to capture the influence of topography on SNR which was not possible when predicting SNR from coarse-resolution soil maps alone.

scholarly journals Using remote sensing data in population density estimation

2021 ◽  
Author(s):  
E.G. Shvetsov ◽  
N.M. Tchebakova ◽  
E.I. Parfenova
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Population Density ◽  
Vegetation Index ◽  
Census Data ◽  
Remote Sensing Data ◽  
Satellite System ◽  
Population Census ◽  
Krasnoyarsk Krai ◽  
Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

Analysis of Soil Organic Matter and Soil Total Nitrogen Condition under Different Vegetation Patterns of Huanghuadianzi Small Watershed

2013 ◽  
Vol 807-809 ◽  
pp. 1839-1842
Author(s):  
Yue Feng Guo ◽  
Yun Feng Yao ◽  
Fu Cang Qin ◽  
Wei Qi
Keyword(s):  
Soil Nutrients ◽  
Total Nitrogen ◽  
Land Use Planning ◽  
Secondary Forest ◽  
Soil Layer ◽  
Soil Nutrient ◽  
Vegetation Patterns ◽  
Small Watershed ◽  
Obvious Effect ◽  
Soil Layers

Different vegetation patterns have difference influenced on the soil nutrients and the soil nutrient contents of different soil layers in the same vegetation patterns are also different in size. In this paper, we analyze the main soil nutrients of different soil layers in different vegetation patterns in Huanghuadianzi small watershed in Ao HanQI of Chifeng in China. The result shows that in different vegetation patterns, the secondary forest of natural bush have an obvious effect on the nutrient and concentration of organic; in the artificial forest, mingled forest has a better improving effect on soil than the pure forest and natural grassland has the smallest effect; in the same vegetation patterns, organic, total nitrogen present an overall reduction trend with the deepening of soil layer accept 40-60cm soil layers. The analysis result of this paper can provide a theoretical basis for further researching the dynamic nutrient change, tree variety optimization arrangement and regional land use planning in forest grass zone.

scholarly journals Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

2020 ◽  
Vol 10 (14) ◽  
pp. 4919
Author(s):  
Guoqing Li ◽  
Alona Armstrong ◽  
Xueli Chang
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Time Interval ◽  
Sensing Data ◽  
Significant Linear Correlation ◽  
Sebal Model ◽  
Moderate Resolution Imaging Spectroradiometer

Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image.

Sign in / Sign up

Export Citation Format

Share Document