Multi-scale Land Research Articles

Snowdrift‐Permitting Simulations of Seasonal Snowpack Processes Over Large Mountain Extents

AbstractThe melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift‐permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift‐permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98% while preserving land‐surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow‐covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods.

Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CECpot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R2 value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CECpot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CECpot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CECpot models.

Optimizing Urban Green Spaces for Air Quality Improvement: A Multiscale Land Use/Land Cover Synergy Practical Framework in Wuhan, China

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues.

Effects of land cover patterns on pond water nitrogen and phosphorus concentrations in a small agricultural watershed in Central China

Seasonal variations in agricultural land cover patterns and their spatial scale effects on surface water nitrogen (N) and phosphorus (P) concentrations in watersheds have been major concerns. Understanding the relationships between land cover metrics and pond nutrients are of great significance to enhance water contamination prediction efficiencies and guide land use planning in agricultural watersheds. In this context, the present study aims to investigate the seasonal variations in the N and P concentrations in 28 ponds in a developed agricultural watershed in central China in 2019. Multivariate statistical analyses were applied to assess the relationships of the pond water N and P concentrations with multi-scale land cover patterns. Besides the pond water ammonium nitrogen (NH4+-N) concentrations, the total nitrogen (TN), nitrate nitrogen (NO3−-N), total phosphorus (TP), and dissolved phosphorus (DP) concentrations in the investigated ponds were higher in summer than those observed in other seasons. The hierarchical partitioning model results demonstrated a spatial consistency in scale effects of land cover metrics on the pond hydrochemical parameters in the different seasons. Redundancy analysis (RDA) showed that the land cover patterns better predicted the variations in the pond water N and P concentrations at the 100 m buffer zone scale in spring and summer, as well as at the 50 m buffer zone scale in fall and winter. Among the land cover metrics considered in this study, the orchard area proportion (POR), landscape shape index (LSI), and largest patch index (LPI) showed the highest significant correlations with the pond water N and P concentrations. The results of this study provide valuable information for effective land use management to control pond water pollution and promote sustainable development in agricultural watersheds.

A Study on the Performance of Image Recognition Technology in Web GIS for Land Use Change Monitoring

Abstract In order to monitor land use change more accurately and rationally manage and utilize land resources, a GIS-based land use change monitoring method is proposed. Based on the satellite remote sensing images provided by the data processing module, the filtering and enhancement processing of land satellite remote sensing images has been completed. The classification module processes the remote sensing images and uses the multi-scale land use multi-categorization network to extract the land use classification results. Image recognition technology stratifies the scenes and images of the study area, utilizes GIS data and remote sensing images superimposed on each other, uses the internal GIS data as training samples, and transforms the various information existing in GIS to form a knowledge rate and a rule base, which is applied to the RS image classification and recognition process. The fastest rate of land use change in a city was found to be the construction land, with the highest rate of 0.1395%. The degree of utilization of arable land enters into a period of decline, and the unutilized land enters into a period of development. Through the change monitoring module, we can accurately monitor and calculate the dynamics of land use, the rate of change in the degree of utilization, and the results of the transfer matrix.

Geographic similarity analysis for Land System Science: opportunities and tools to facilitate knowledge integration and transfer

ABSTRACT Advances in Land System Science (LSS) rely on the evidence generated by different types of research activities, including place-based case studies, landscape/land-system mapping and synthesis research. However, these activities are usually conducted in parallel, with a lack of integration often leading to important knowledge gaps and limitations. In this article, we provide tools for the application of geographic similarity analysis (GSA), a collection of spatially-explicit methods assessing the degree of similarity between geographic locations, and thereby help to address these limitations. We identify opportunities for employing GSA to support: 1) selecting geographically representative sets of case studies; 2) integrating empirical evidence generated at different scales and levels of abstraction; and 3) facilitating context-sensitive knowledge transfer. The resulting toolbox provides approaches for facilitating researchers to get an enhanced understanding of multi-scale land change processes, as well as supporting land governance in scaling up the knowledge and solutions generated by LSS research.

Forty-year multi-scale land cover change and political ecology data reveal a dynamic and regenerative process of forests in Peruvian Indigenous Territories

This article explores deforestation and reforestation dynamics over 415,749 hectares of 25 titled Indigenous Community Lands (ICLs) in the Peruvian Amazon over forty years at three scales: total area, regions, and communities. We focus on ICLs as the territorial unit of analysis, as they are increasingly discussed regarding their importance for conservation. Additionally indigenous communities (ICs) are a too-marginalized group in the Amazon that merit more attention. Analyses of this kind are often short-term and use only large-scale Earth Observation methodologies. We use a multi-method approach linking remote sensing with ground verification, and qualitative historical political ecology work with ICs. We find that overall accumulated deforestation was low at 5%, but that when reforestation is considered, net deforestation was only 3.5%. At the community level deforestation and afforestation dynamics are complex, except for one period that indicates a macro state driver in the region. Results suggest inadequate accounting for forest regeneration in deforestation analyses and challenge the notion that presenting stakeholders with accumulated forest loss values is helpful in tropical areas where forests and people are dynamic. Furthermore, our work with communities highlights that categorizing them and their lands as pro-environment or not in general terms is unhelpful for determining fund flows to ICLs for environmental or development purposes.

Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape.

UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States

Very-high-resolution (VHR) land cover and land use (LCLU) is an essential baseline data for understanding fine-scale interactions between humans and the heterogeneous landscapes of urban environments. In this study, we developed a Fine-resolution, Large-area Urban Thematic information Extraction (FLUTE) framework to address multiple challenges facing large-area, high-resolution urban mapping, including the view angle effect, high intraclass and low interclass variation, and multiscale land cover types. FLUTE builds upon a teacher-student deep learning architecture, and includes two new feature extraction modules – Scale-aware Parsing Module (SPM) and View-aware Embedding Module (VEM). Our model was trained with a new benchmark database containing 52.43 million labeled pixels (from 2014 to 2017 NAIP airborne Imagery) to capture diverse LCLU types and spatial patterns. We assessed the credibility of FLUTE by producing a 1-meter resolution database named UrbanWatch for 22 major cities across the conterminous United States. UrbanWatch contains nine LCLU classes – building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. We have further made UrbanWatch freely accessible to support urban-related research, urban planning and management, and community outreach efforts: https://urbanwatch.charlotte.edu.

Land use optimization of rural production–living–ecological space at different scales based on the BP–ANN and CLUE–S models

Rural production–living–ecological (PLE) space is the essential carrier of China's rural land resource planning and management. However, the pattern identification, optimal prediction, and multi–scale integration of rural PLE space lack sufficient scientific evidence and reliable quantitative analysis. To fill this gap, this paper proposes a multi-scale land use optimization method based on benefit coupling evaluation, BP–ANN and CLUE–S models. The typical hilly area of the upper reaches of the Yangtze River in southwest China were used as a case for empirical study. The results showed that there was a high correlation between land use patterns and benefits. The key to land use optimization in hilly areas is to increase the area of ecological land and improve the capacity of regional ecosystem services. Through the evaluation of the degree of comprehensive benefit coupling, 44 sample towns with an optimal land use pattern were effectively identified to construct the optimization model. At the regional scale, the BP-ANN model predicted the optimal ratio of land use structure for each township unit based on natural, social, and economic influencing factors. The optimization results reduced production land by 8.94% on average, increased ecological land by 9.2% on average, and kept living land relatively stable. The proportions of production, living, and ecological land were adjusted to 59.85%, 8.34%, and 31.81%, respectively, which can better meet the land space demand for food security and ecological protection in the future. Regarding the smaller scale, the CLUE-S model took the regional-scale optimization results as the goal to simulate the spatial distribution of land use. The proportion of production, living, and ecological land in the town of Taiping was optimized from the previous 88.17%, 6.25%, and 5.58% to 62.92%, 7.83%, and 29.25%, respectively. The optimization results not only ensure the stability of high-quality cultivated land, but also effectively improve ecological functions such as soil conservation and water purification. This novel method was proven to be effective for quantitative optimization of land use and multi-scale functional space cohesion and integration, providing scientific support for the sustainable use of rural land resources in China.

Hierarchical Urban Land Mappings and Their Distribution with Physical Medium Environments Using Time Series of Land Resource Images in Beijing, China (1981–2021)

Rapid urban expansion and structural changes are taking place in China’s capital city, Beijing, but without an update of urban land features in a timely manner our understanding of the new urban heterogeneity is restricted, as land-background data is indispensable for bio-geophysical and bio-geochemical processes. In this plain region, the investigations of multi-scale urban land mappings and physical medium environmental elements such as slope, aspect, and water resource services are still lacking, although Beijing can provide an exemplary case for urban development and natural environments in plains considering the strategic function of China’s capital city. To elucidate these issues, a remote-sensing methodology of hierarchical urban land mapping was established to obtain the urban land, covering structure and its sub-pixel component with an overall accuracy of over 90.60%. During 1981–2021, intense and sustained urban land expansion increased from 467.13 km2 to 2581.05 km2 in Beijing, along with a total growth rate of 452.53%. For intra-urban land structures, a sharp growth rate of over 650.00% (i.e., +1649.54 km2) occurred in terms of impervious surface area (ISA), but a greening city was still evidently observed, with a vegetation-coverage rate of 8.43% and 28.42% in old and newly expanded urban regions, respectively, with a more integrative urban ecological landscape (Shannon’s Diversity Index (SHDI) = −0.164, Patch Density (PD) = −8.305). We also observed a lower rate of ISA (0.637 vs. 0.659) and a higher rate of vegetation cover (0.284 vs. 0.211) in new compared to old urban regions, displaying a higher quality of life during urban expansion. Furthermore, the dominant aspect of low, medium, and high density ISA was captured with the north–south orientation, considering the sunlight conditions and traditional house construction customs in North China, Over 92.00% of the ISA was distributed in flat environment regions with a slope of less than 15°. When the water-resource service radius shifted from 0.5 km to 0.5–1 km and 1–2 km, high density vegetation displayed a dependence on water resources. Our results provide a new survey of the evolution of hierarchical urban land mapping during 1981–2021 and reveals the relationship with physical medium environments, providing an important reference for relevant research.

A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

Mapping land surface water automatically and accurately is closely related to human activity, biological reproduction, and the ecological environment. High spatial resolution remote sensing image (HSRRSI) data provide extensive details for land surface water and gives reliable data support for the accurate extraction of land surface water information. The convolutional neural network (CNN), widely applied in semantic segmentation, provides an automatic extraction method in land surface water information. This paper proposes a new lightweight CNN named Lightweight Multi-Scale Land Surface Water Extraction Network (LMSWENet) to extract the land surface water information based on GaoFen-1D satellite data of Wuhan, Hubei Province, China. To verify the superiority of LMSWENet, we compared the efficiency and water extraction accuracy with four mainstream CNNs (DeeplabV3+, FCN, PSPNet, and UNet) using quantitative comparison and visual comparison. Furthermore, we used LMSWENet to extract land surface water information of Wuhan on a large scale and produced the land surface water map of Wuhan for 2020 (LSWMWH-2020) with 2m spatial resolution. Random and equidistant validation points verified the mapping accuracy of LSWMWH-2020. The results are summarized as follows: (1) Compared with the other four CNNs, LMSWENet has a lightweight structure, significantly reducing the algorithm complexity and training time. (2) LMSWENet has a good performance in extracting various types of water bodies and suppressing noises because it introduces channel and spatial attention mechanisms and combines features from multiple scales. The result of land surface water extraction demonstrates that the performance of LMSWENet exceeds that of the other four CNNs. (3) LMSWENet can meet the requirement of high-precision mapping on a large scale. LSWMWH-2020 can clearly show the significant lakes, river networks, and small ponds in Wuhan with high mapping accuracy.

Devising spatio-temporal adaptation from land suitability inputs: Measures and options for sub-alpine agricultural systems

The paper introduces a decision-making approach with the intention to guide agricultural adaptation to climate change during the 21st century. Using two orographically dissimilar sub-alpine systems located in the Australian Alps and Chilean Dry Andes, the rational selection of adaptation measures and local options is framed into a spatio-temporal adaptation model. Decision-making here is supported by the interpretation of adaptation pathway analogues following key adaptation typologies (like flexibility and deepness) and representative landscape functionalities found in specific rural spaces. The model is based on estimating multi-scale land suitability gaps and feasible biophysical and irrigation-potential combination of adjustments seen as necessary to propose specific adaptation strategies for a baseline, mid-century and end-century periods. Decision-making insights resulting from this exercise could improve the evaluation when corroborating and generalising regional-type adaptation pathways in the light of adaptation opportunities and limits.

Coupling Cellular Automata and a Genetic Algorithm to Generate a Vibrant Urban Form-A Case Study of Wuhan, China.

Enhancing urban vitality is a key goal for both the government and ordinary urban residents, and creating this vitality is emphasized in China’s urban development strategy. Enhancing urban vitality through the rational design of urban forms is a leading topic of Western urban research. An urban growth pattern (UGP) reflects the dual characteristics of a static pattern and the dynamic evolution of the external urban form. It affects urban vitality by influencing the spatial allocation of internal structural elements and patterns in the adjacent location. The cellular automata (CA) mode can effectively simulate the aggregation process of urban growth (infilling expansion or edge expansion). However, it does not simulate the diffusion of urban growth, specifically the evolution of outlying expansion. In addition, CA focuses on learning, simulating, and building knowledge about geographic processes, but does not spatially optimize collaborative land use against multiple objectives or model multi-scale land use. As such, this paper applies a coupling model called the “promoting urban vitality model,” based on cellular automata (CA) and genetic algorithm (GA) (abbreviated as UV-CAGA). UV-CAGA optimally allocates cells with different UGPs, creating a city form that promotes urban vitality. Wuhan, the largest city in Central China, was selected as a case study to simulate and optimize its urban morphology for 2025. The main findings were as follows. (1) The urban vitality of the optimized urban form scheme was 4.8% higher than the simulated natural expansion scheme. (2) Compared to 2015, after optimization, the simulated sizes of the newly increased outlying, edge, and infilling areas in 2025 were 6.51 km2, 102.69 km2, and 23.48 km2, respectively; these increases accounted for 4.90%, 77.32%, and 17.68%, respectively, of the newly increased construction land area. This indicated that Wuhan is expected to have a very compact urban form. (3) The infilling expansion type resulted in the highest average urban vitality level (0.215); the edge expansion type had the second highest level (0.206); outlying growth achieved the lowest vitality level (0.199). The UV-CAGA model proposed in this paper improves on existing geographical process simulation and spatial optimization models. The study successfully couples the “bottom-up” CA model and “top-down” genetic algorithm to generate dynamic urban form optimization simulations. This significantly improves upon traditional CA models, which do not simulate the “diffusion” process. At the same time, the spatial optimization framework of the genetic algorithm in the model also provides insights related to other effects related to urban form optimization, such as urban environmental security, commuting, and air pollution. The integration of related research is expected to enrich and improve urban planning tools and improve the topic’s scientific foundation.

Adaptive monitoring for multiscale land management: Lessons learned from the Assessment, Inventory, and Monitoring (AIM) principles

• The BLM Assessment, Inventory, and Monitoring (AIM) strategy recommends five principles for building multiscale monitoring programs: standardized methods and indicators; data management and stewardship; appropriate sample designs; remote sensing integration; and structured implementation. These principles guide monitoring across public lands. • We find the AIM principles are sound and worthy of consideration for design and adaptation of rangeland monitoring programs worldwide. • An emergent principle, standard workflows and analysis frameworks for using data, connects data to land management decision-making and empowers land managers. • The AIM principles inspire and provide opportunities for the rangeland management community to implement adaptive management.

Production–Living–Ecological Conflict Identification Using a Multiscale Integration Model Based on Spatial Suitability Analysis and Sustainable Development Evaluation: A Case Study of Ningbo, China

Production–living–ecological space (PLES) basically covers the scope of spatial activities in people’s material production and spiritual life and is the basic carrier of human social development and economic activities. The coordinated development of PLES is an effective method to mitigate land-use conflicts to achieve balanced and coordinated development of the region. However, so far, compared with the single-scale study based on administrative unit, the PLES conflicts between microcosmic grid-scale receives less attention. Considering the important scale problems of the geographical study, this study aims to analyze the synergetic degree of PLES under different scales (administrative-unit, grid, and integrated multiscale) and to scientifically diagnose land use conflicts in Ningbo, China. Results indicated that production land and ecological land in Ningbo were continuously occupied by human activities from 2010 to 2018. The lowest and lower suitability areas of ecological space in Ningbo increased from 2010 to 2018. Land ecological suitability was seriously affected by urban expansion, its ecological value was reduced, and the PLES developed towards the trend of being uncoordinated. Multiscale coupling analysis showed that the PLES in Ningbo was in less conflict on the whole, but with the development of the economy, the coupling coordination degree of PLES was also damaged. This study establishes the different scales of a PLES coupling coordination development degree evaluation index system and enriches the methods of multiscale land use fusion conflict diagnosis and also provides a scientific reference for the optimized and sustainable development of regional territorial space.

A Multi-Level Convolution Pyramid Semantic Fusion Framework for High-Resolution Remote Sensing Image Scene Classification and Annotation

High spatial resolution (HSR) imagery scene classification has become a hot research topic in remote sensing. Scene classification method based on the handcrafted features, such as the bag-of-visual-words (BoVW) model, describes an image by extracting local features of the scene and mapping them to the dictionary space, but usually uses a shallow structure and loses the spatial distribution characteristics of the scene. The method based on deep learning extracts hierarchical features to describe the scene, which can maintain the spatial position information well. However, deep features in different levels have scale recognition restrictions for multi-scale ground objects, and cannot understand complex scenes well. In this paper, the multi-level convolutional pyramid semantic fusion (MCPSF) framework is proposed for HSR imagery scene classification. Differing from previous scene classification methods, which integrate the feature of different levels directly, of which the fusion features have large differences in both sparsity and eigenvalue magnitude, MCPSF integrates multi-level semantic features extracted by BoVW model and convolutional neural network (CNN) model. In MCPSF, two convolution pyramid feature expression strategies are proposed to enhance the ability of capturing multi-scale land objects, i.e., local and convolutional pyramid based BoVW (LCPB) model and local and convolutional pyramid based pooling-stretched (LCPP) model. The effectiveness of the proposed method is verified on 21-class UC Merced (UCM) dataset and 30-class Aerial Image Dataset (AID). The framework was also transferred toa case study of scene annotation in Wuhan. The proposed framework significantly improves the performance when compared with other state-of-the-art methods.

Integrated evaluation on multi-scale land surface temperature grading and bio-temperature suitability—a case study in Tianjin China

ABSTRACT The suitable thermal environment is a prerequisite for biological survival. In order to evaluate the change of land surface temperature (LST) and its effect on organisms, in this study, the Land satellite (Landsat) series data was used to invert the LST from 1988 to 2018 in Tianjin. The main surface features were extracted from the remotely sensed images using normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and normalized difference impervious surface index (NDISI). A multi-scale temperature grading method based on biological heat reaction was proposed to classify the LST. In terms of the suitability of the organism to temperature, the land surface temperature suitability (LSTS) model was established based on the niche suitability analysis method, and the suitability of organisms to temperature was evaluated based on the model. In the aspect of land area change, both the land surface temperature area (LSTA) model and the average relative contribution (ARC) were proposed to comprehensively evaluate the impact of the area change of surface features on LST. The results indicated that the temperature suitability of most organisms had decreased. The change of LST was related to the comprehensive change of surface features. In conclusion, the rise of LST had adverse effect on most organisms in Tianjin. Limiting the occupation of water area and cultivated land may inhibit the continuous rise in LST. Besides, the evaluation method novelty proposed in this study could provide new ideas to study the development trend of spatial objects in their environment thus would be applied widely.

Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference

In this paper, automatic extraction of multi-context and multi-scale land use/land cover vegetation from high-resolution remote sensing images is tackled, aiming to solve typical challenges in classifying remote sensing images at a pixel level. To solve small inter-class differences and large intra-class differences between the vegetation and background, we introduce a vegetation-feature-sensitive focus perception (FP) module. Considering the intrinsic properties of vegetation objects, we established an adaptive context inference (ACI) model under a supervised setting that includes a context model to represent relationships between a center pixel and its neighbors under semantic constraints, as well as the spatial structures of vegetation features. Comparative experiments on the ZY-3 and Gaofen Image Dataset (GID) datasets demonstrate the effectiveness of our proposed automatic vegetation extraction model against the baseline Deeplab v3+ model. Taking precision, kappa coefficient, mean intersection over union (miou), precision rate, and F1-score as the evaluation indexes, the results showed an improvement in the precision by at least 1.44% and miou by 2.47%, over the baseline Deeplab v3+ model. In addition, the ACI module improved the precision and miou by 2% and 3.88%, and the FP module improved the precision and miou by 1.13% and 1.65%. These results and statistics of these comprehensive experiments illustrated that our adaptive and effective vegetation extraction model could satisfy different requirements of land use/land cover mapping applications.

Land use balance for urban economy: A multi-scale and multi-type perspective

In a situation in which land is in short supply, attention has focused largely on the direct use rather than on the indirect use of land embodied in regional, national and international flows of goods and services. To fill this gap, the multi-scale input-output method is used to identify the impact of final demand and trade activities on the requirements of a single metropolis, Beijing as a case, for land use embodied in its supply chains and commodity trade. While Beijing has limited land at its disposition, the land embodied in its final demand exceeds its own land area by a factor of more than ten, indicating the importance of co-ordinated land use planning and of supporting assessments of land use balance. Covering both direct and indirect land utilization through supply chains, the multi-scale land use analysis plays a significant role to extend the urban land use planning to different scales’ sources and sinks. It is thus essential to identify land use balance at urban economy, which could provide potential policy implications for dynamic adjustments of land use allocation and land-intensive industries from a multi-scale and multi-type perspective.