Background: Jixi is a typical mining city in China that has undergone dramatic changes in its land-use pattern of mining areas over the development of its coal resources. The impacts of coal mining activities have greatly affected the regional land surface temperature and ecological system. Methods: The Landsat 8 Operational Land Imager (OLI) data from 2015 and 2019 were used from the Jiguan, Didao, and Chengzihe District of Jixi in Heilongjiang, China as the study area. The calculations to determine the land-use classification, vegetation coverage, and land surface temperature (LST) were performed using ArcGIS10.5 and ENVI 5.3 software packages. A correlation analysis revealed the impact of land-use type, vegetation coverage, and coal mining activities on LSTs. Results: The results show significant spatial differentiation in the LSTs of Jixi City. The LSTs for various land-use types were ranked from high to low as follows: mining land > construction land > grassland > cultivated land > forest land > water area. The LST was lower in areas with high vegetation coverage than in other areas. For every 0.1 increase in vegetation coverage, the LST is expected to drop by approximately 0.75 °C. An analysis of mining land patches indicates that the patch area of mining lands has a significant positive correlation with both the average and maximum patch temperatures. The average patch temperature shows a logarithmic increase with the growth of the patch area, and within 200,000 m², the average patch temperature increases significantly. The maximum patch temperature shows a linear increase with the patch area growth, and for every 100,000 m² increase in the patch area of mining lands, the maximum patch temperature increases by approximately 0.81 °C. The higher the average patch temperature of mining land, the higher the temperature in its buffer zone, and the greater its influence scope. This study provides a useful reference for exploring the warming effects caused by coal mining activities and the definition of its influence scope.

Clarifying the relationship between carbon storage and ecological risks is critical to ensuring regional sustainable development. Land use changes caused by land use policy invariably result in substantial changes in carbon storage and ecological risks. The link between carbon storage and ecological risks in green space is still unknown, even though green space is an essential ecological function carrier. According to the Blackland Conservation Utilization (BCU) policy document and natural exploitation (NP) status, this study compared and projected the carbon storage and landscape ecological risk characteristics of green space in Heilongjiang Province (HLJP) for 2030. It also quantitatively assessed the interactions and synergistic changes of the two variables in terms of coupled coordination relationships, quantitative correlations, and spatial correlations. The results demonstrated the following: (1) the green space evolution of HJLP under the BCU scenario is significantly more drastic than under the NP scenario; (2) In 2020–2030, the NP scenario’s evolution of green space results in the ecosystem losing 323.51 × 10⁶ t of carbon storage, compared to the BCU scenario’s loss of just 216.07 × 10⁶ t. The BCU policy will increase the agglomeration of high-risk ranges in the northeast and southwest will but decrease the overall landscape ecological risk level of green space; (3) BCU policy will prevent the system’s orderly development and benign coupling, but it will increase the interdependence between carbon storage and landscape ecological risks in green space; (4) Green space exchange and loss will result in the simultaneous rise or decrease in both variables. The magnitude of carbon storage increase owing to green space expansion tends to increase simultaneously with the magnitude of landscape ecological risk reduction. To a certain extent, the HLJP black land conservation and utilization policy can improve carbon storage and ensure ecological security, and the matching of dominant regions with the status of the landscape evolutionary process can support future carbon-neutral actions.

In this study, the change in green space in different scenarios and the index characteristics of landscape patterns were analyzed and were conducive to providing the decision basis for future green space planning in Harbin, a city in Northeast China. The FLUS model was used to predict the layout of green space, and the prediction results were analyzed and evaluated using the landscape index method. Combined with the MOP model and LINGO12.0, the objective function of economic benefit and ecological benefit was established to maximize the comprehensive benefit. As revealed by the outcome, from 2010 to 2020, the fragmentation degree of cultivated land, forest, and grassland decreased, and the overall landscape level tended to be diversified and uniform. In the status quo scenario, the cultivated land and the forest land were increased, whereas the water area and the wetland changed little, and its overall benefit was the lowest. The forest was increased by 137.46 km² in the ecological protection scenario, the largest among the three scenarios, and the overall water quality improved. In the economic development scenario, the cultivated land tended to expand rapidly, the connectivity was increased, and the area of forest was decreased by 69.19 km², and its comprehensive benefit is lower than that under the scenario of ecological protection. The sustainable development scenario achieved the most significant economic and ecological benefits, with a total income of CNY 435,860.88 million. Therefore, the future green space pattern should limit the expansion of cultivated land, maintain the spatial pattern of woodland and wetland, and enhance the protection of water area. In this study, Harbin green space was studied from different scenario perspectives, combined with landscape pattern index and multi-objective planning, which is of great significance for Harbin green space planning decisions in the future and improving comprehensive benefits.

The aim of the construction of Northeast Tiger-Leopard National Park is to protect the integrity of endangered wild animals and natural resources. There is a close relationship between land use and ecosystem service value. Improving the quality of ecological environment is the demand of ecological civilization construction. Therefore, this paper aims to evaluate and analyze the ecosystem service value of Tiger and Leopard Park, which is particularly based on big data land use change. Firstly, using ArcGIS software as the support, we have discussed the impact of the dynamic trend of land use change, transfer matrix, and sensitivity coefficient on ecosystem services in different periods of Tiger and Leopard Park. Secondly, we constructed an evaluation model based on the ecological service value method, which is primarily based on geographical conditions and surface coverage. We have completed the value evaluation and change analysis of ecosystem service in Tiger and Leopard Park by analyzing the generation process of ecosystem service data and information characteristics, as well as the ecosystem classification standard of Tiger and Leopard Park and equivalent factor of ecosystem service value per unit area. Simulation experiments show that the method proposed in this paper can reflect the service value of the ecosystem of the Tiger and Leopard Park, lay the foundation for the construction of ecotourism, and promote the green and sustainable development of the regional ecosystem of the Tiger and Leopard Park.

The garden landscape of urban residential areas has significant environmental benefits, allowing people to get closer to nature and integrate with nature to relax and have some social benefits at the same time. This article is aimed at studying the effect of urban greening remote sensing on residential landscape construction. This paper uses the greening remote sensing image processing algorithm combined with the analysis method of SURF color remote sensing image and conducts the experiment based on the urban greening remote sensing on the residential area landscape architecture research. This article combines sustainable development strategies with innovative design methods to deeply study the role of urban greening remote sensing on the construction of residential landscape architecture. At the same time, the experiment in this article also proposes that the garden landscape of urban residential areas can provide entertainment for people, soothe residents’ mood, and achieve the best use of residential buildings. However, there are still some constraints in the development process of residential industry: insufficient government support, relatively backward technology of residential industrialization, lack of support and input from enterprises, and prejudice of people’s traditional concept towards it. The experimental results of this paper show that the remote sensing image features of buildings in residential areas and public areas are obvious. In the recognition of 46 training features and 34 detection features, the number of correct remote sensing recognition features is 32, and the green building image remote sensing recognition is good. The accuracy is 94%, which can meet the identification requirements of greening in the building. Combined with the specific conditions of urban greening and garden construction in my country, it can meet people’s entertainment needs and, to a certain extent, can also improve people’s life and cultural taste.

Quality water plays a huge role in human life. Chlorophyll-a (Chl-a) in water bodies is a direct reflection of the population size of the primary productivity of various phytoplankton species in the water body and can provide critical information on the health of water ecosystems and the pollution status of water quality. Case 2 Regional CoastColour (C2RCC) is a networked atmospheric correction processor introduced by the Sentinel Application Platform for various remote sensing products. Among them, the Extreme Case-2 Waters (C2X) process has demonstrated advantages in inland complex waters, enabling the generation of band data, conc_chl product for Chl-a, and kd_z90max product for Secchi Depth (SD). Accurate in situ data are essential for the development of reliable Chl-a models, while in situ data measurement is limited by many factors. To explore and improve the uncertainties involved, we combined the C2X method with Sentinel-2 imagery and water quality data, taking lakes in Wuhan from 2018 to 2021 as a case. A Chl-a model was developed and validated using an empirical SD model and a neural network incorporating Trophic Level Index (TLI) to derive the predicted correction result, Chl-a_t. The results indicated that (1) the conc_chl product measured by C2X and in situ Chl-a exhibited consistent overall trends, with the highest correlation observed in the range of 2–10 μg/L. (2) The corrected Chl-a_t using the conc_chl product had a mean absolute error of approximately 10–15 μg/L and a root-mean-square error of approximately 8–10 μg/L, while using in situ Chl-a had a root-mean-square error (RMSE) of approximately 15 μg/L and a mean absolute error (MAE) of approximately 20 μg/L; both errors decreased by double after correction. (3) The correlation coefficient (R) between Chl-a_t and each data point in the Chl-a model results was lower than that of SD-a_t with each data point in the SD model results. Additionally, the difference in R-value between Chl-a_t and each data point (0.45–0.60) was larger than that of SD-a_t with each data point (0.35–0.5). (4) When using corrected Chl-a_t data to calculate the TLI estimation model, both RMSE and MAE decreased, which were 1μg/L lower than those derived from uncorrected data, while R increased, indicating an improvement in accuracy and reliability. These findings demonstrated the presence of in situ errors in Chl-a measurements, which must be acknowledged during research. This study holds practical significance as some of these errors can be effectively corrected through the use of C2X atmospheric correction on spectral bands.