Ecological sensitivity is one of the important indicators of regional ecological fragility, which can represent the sensitivity of ecosystems to natural environmental conditions and human activity disturbances in the region. In this study, the ecological sensitivity of land resources in the Tumen River Basin of China was quantitatively evaluated by taking 3 ecologically sensitive impact types, including the natural environment, human disturbance, and soil erosion, as evaluation criteria, and 11 ecologically sensitive factors were selected to build an evaluation system using the analytic hierarchy process (AHP) method, to determine the weights of the evaluation factors, combined with geographic information system (GIS) technology. The results show that: (1) Among the three types of ecological sensitivity factors, the influence of human disturbance is the most obvious, and the two factors of land use type and distance from construction land have the highest weights in the comprehensive ecological sensitivity evaluation. (2) There are no extremely sensitive areas or insensitive areas in the Tumen River Basin in China. Highly sensitive areas account for only 0.59% of the total area and are mainly concentrated in the lakes, rivers, and reservoirs in the study area. Moderately sensitive areas account for 54.12%, which are concentrated in the central part of the Tumen River Basin Slightly sensitive areas are mainly located in the mountainous areas in the north and south of the study area. (3) Among the various land resource types, the proportion of slightly sensitive areas and moderately sensitive areas of woodland is close (about 50%), while cultivated land, grassland, construction land, and bare land are mainly moderately sensitive areas (73.95%, 82.07%, 96.59%, and 78.78%), and water bodies are mostly distributed within highly sensitive areas (60.97%), and all wetlands with the smallest area are moderately sensitive. The results of the study can provide data support and a scientific basis for regional ecological protection and development planning.

Soil organic matter (SOM) is an important component of soil and plays an important role in improving the soil’s physical and chemical properties. Ascertaining the spatial distribution of soil organic matter and its main controlling factors in the context of provincial scale farming is of important guiding significance for soil carbon sequestration, emission reduction and sustainable utilization. Using 257 soil profiles from the second soil survey in Shandong Province, GIS, we applied geostatistical methods to study the spatial distribution characteristics of SOM in topsoil. In addition, correlation and regression analyses were used to explore the main controlling factors over the spatial variation of SOM. The results showed that the mean amount of SOM in Shandong province ranged from 1.20–74.90 g·kg⁻¹, with a coefficient of variation of 73.52%, which is a medium level of variation. The distribution of SOM in the study area was patchy, with higher levels of organic matter in the central, eastern, and northern parts, and lower levels of organic matter in the south-west. The comprehensive explanatory ability of all factors reached 52.30%. Soil type and parent material were the main controlling factors for the spatial variability of SOM in the Shandong Province, followed by soil texture and land use type, with topography and climatic factors having relatively little influence.

Improving the justice of public parks is of great significance to the well-being of residents, and it is also an important goal of green space planning. In this paper, the spatial justice of park green space under five travel modes and at three scales was analyzed using the travel-behavior-based Gaussian two-step floating catchment area method (TB-G2SFCA) and Gini coefficient method for Qingdao City Center. The main results are as follows: Under walking mode, walking–bus mode, and walking–subway mode, there were unserved areas in terms of urban park green space, while there were no unserved areas in the cases of cycling and driving. Residents’ choice of travel time and travel mode would affect the service scope of the park green space, and the increase in travel time would reduce the unserved areas in the urban park green space. The choice of travel time and travel mode affected the accessibility of urban park green space for residents in each residential patch, as well as the justice of the distribution of park green space resources at the scales of street blocks, districts, and the whole study area. The increase in residents’ travel speed and travel time could promote the equitable allocation of urban park green space to a certain extent. The results of this study provide a scientific basis for the planning and construction of urban park green space in Qingdao City.

Utilizing idle space for agricultural planting can generate production profits while greening the urban environment. How can the space available for agricultural planting in cities be inventoried and quantified, and what is the production potential? With the help of GIS, spatial analysis was carried out, and more objective conclusions were drawn through calculation. This paper takes the roof space in Shibei District of Qingdao as an example to study: First, we analyzed the influencing factors and inventory standards of roof agricultural planting; second, we selected inventory standards for idle roof space inventory and conducted statistical analysis of the data obtained from the inventory. Furthermore, combining the annual yield of vegetables per unit area and the annual consumption standards of vegetables by citizens, we calculated the yield and supply and demand; next, we summarized the research results, analyzed their reliability and the advantages and disadvantages of this inventory, and derived formulas for calculating the agricultural production potential of rooftops and urban spaces. Finally, we concluded that the idle roof area in cities and the potential for agricultural production are significant. The research results indicate that the roof area of urban buildings in the Shibei District of Qingdao is 383.3 hectares, with an annual vegetable production potential of 25,800 tons, which can supply the urban population of 184,300. Based on the above research, it is proposed that rooftop agriculture should be given priority in the future development of urban agriculture, and it is recommended that other urban spaces should be inventoried to promote the development of urban agriculture.

Soil salinization is a significant threat to agricultural production, making accurate salinity prediction essential. This study addresses key challenges in the Yellow River Delta (YRD) soil salinity inversion, including (1) determining which Landsat 8 OLI level performs better, (2) identifying the most suitable month for salinity inversion, and (3) improving model performance and identifying important variables in modeling. Thus Landsat 8 OLI images (Level-1 and Level-2) for 12 months were collected, then images having less than 10% cloud cover were selected and processed to extract spectral values. A total of 86 sampled points were processed to measure soil salinity. Using Pearson correlation and expert insights, January 15 and August 26 were identified as suitable dates for inversion. Then, seven original bands, 29 spectral indicators, and 39 derived variables which created through six mathematical transformations, were used to construct the following three models: partial least squares regression (PLSR), random forest (RF), and backpropagation neural network (BPNN). The results showed the following: (1) The Level-1 data, after FLAASH atmospheric correction, outperforms Level-2 data. (2) January is optimal for salinity inversion. (3) Among the three models, RF outperformed the others, achieving test set R² = 0.55, RMSE = 3.4, suggesting that the combination of spectral indicators and mathematically transformed variables can effectively enhance model accuracy for predicting soil salinity in the YRD. Furthermore, SWIR1, SWIR2, CLEX, second-order difference of SWIR1, and first-order difference of SWIR2 along with NIR played a key role in modeling.

Soil organic matter (SOM) plays a crucial role in promoting soil tillage, improving soil fertility and providing crop nutrients. Investigation and sampling are the premise and basis for understanding the spatial distribution of SOM. The number of sampling points will affect the accuracy of spatial variation of SOM. Therefore, it is important scientific work to determine a reasonable number of sampling points under the premise of ensuring accuracy. In this study, Kenli County, a typical area of the Yellow River Delta in China, was taken as an example to investigate the effect of different sampling points on spatial-variation expression of SOM. A total of 12 sample subsets (including 900 samples) were randomly sampled at equal intervals from the 900 sample points, using geographic information system (GIS) technology and geostatistical analyses to explore the optimal number of samples. The results showed that the SOM content in the study area had a lower-middle degree of variation. As the number of sample points decreased, the spatial distribution of SOM showed the gradual weakening of detail-characterization ability; and when the number of sample points was too small (<100), there was a wrong expression that was not consistent with the actual situation. The value of RMSE has no obvious regularity with the change of sample number. The values of both ME and ASE showed a significant inflection point when the number of samples was 150 and remained around 0 and 4 as the number of samples increased, respectively. Combined with the three indicators of ME, RMSE and ASE, collecting at least 150 samples can satisfy the spatial-variation expression of SOM, equivalent to 107 sample points within the area of 1000 km². The research results could provide important references for investigation of SOM content in areas with similar natural geographical conditions.