Dynamic Land Research Articles

An assessment of the seasonally dependent biophysical mechanism and dynamic land use change: A Research Effort from Emerging Urban Agglomeration of West Bengal

The behavior of the biophysical components needs to be closely observed while taking seasonal perspectives into account. This study focuses on seasonal analysis of biophysical components and dynamics of land use changes. This study examines the spatiotemporal seasonal variation among land surface temperature (LST), NDVI, and NDWI, with a focus on the two main sub-tropical climatic seasons summer and winter. We use correlation and regression analysis to assess the season-wise relationship between LST and biophysical indices with the help of grouped marginal plots. The comparison analyses of the biophysical components and land use change for a longer period from 1991-2021 with their matrix was undertaken in five urban agglomerations (UA) like Asansol, Siliguri, Kolkata, English Bazar and Kharagpur. The maximum likelihood classification technique was used to categorize multi-temporal Landsat images for each of the five growing UA in West Bengal. The most intriguing finding is that in all UAs, from 1991 to 2021, built-up area increased to become the predominant land. Thus, the vegetation and agricultural land are in danger in every location of West Bengal, whether it is plain or hilly, because of the land's gradual conversion to concrete or built-up areas over time. Additionally, the result shows that in summer and winter seasons, LST was observed growth due to the rapid urban built-up area expansion. It is also found that LST and NDVI have a close negative relationship, but LST and NDWI do not have any perfect relationship. Therefore, to lessen the warming effect of urban microclimates, policymakers should be concerned about future urban expansion that is both horizontal and vertical. An appropriate strategy for environmentally friendly construction, green buildings, and cool roof technologies should be developed.

Participatory topological mapping: A novel approach for exploring and communicating situated knowledge of complex socio-ecological systems

Land use change impacts on Sámi reindeer husbandry are well-documented, but existing maps often fail to capture socio-ecological relationships between herders, reindeer and nature. Conventional Geographic Information Systems (GIS) simplify these relationships, lacking local context and excluding valuable knowledge due to their rigid structure. This paper introduces participatory topological mapping, a low-tech, knowledge co-production method that allows communities to share geospatial insights without advanced software. Combining hand-drawn maps with a standardised topological structure facilitates the visualisation of geographical relationships and dynamic land use systems from a local perspective. Developed and assessed through a Swedish case study in collaboration with Vittangi and Malå reindeer herding communities, the method aims to reduce barriers to knowledge exchange and enhance dialogue between Indigenous knowledge-holders, researchers and decision-makers. The paper discusses the authors’ experiences with participatory topological mapping, emphasising its potential and limitations for knowledge exchange. The method is advocated for broader use, recognising its value in articulating concerns about land use change beyond research settings and reindeer husbandry. Participatory topological mapping enables knowledge-holders to control the map and its narrative, deciding what is included or excluded. The maps and accompanying stories are valuable sources for documenting, sharing and analysing local perspectives on landscapes and the effects of land use change. The method treats mapping as a social process with the potential to affect how we understanding the world. When thoughtfully applied, it offers a valuable avenue for knowledge co-production, fostering deeper engagement, supporting informed decision-making and empowering communities.

Evaluation and Comparison of Curve Numbers Based on Dynamic Land Use and Land Cover Changes

Evaluation and Comparison of Curve Numbers Based on Dynamic Land Use and Land Cover Changes

Construction of Landscape Ecological Risk Collaborative Management Network in Mountainous Cities—A Case Study of Zhangjiakou

The prevention of ecological risks is a critical determinant influencing sustainable development. Driven by rapid socio-economic development, the ecosystems of mountainous cities within agro-pastoral transition zones are increasingly vulnerable to complex disturbances, constituting a significant threat to sustainable development and human well-being. To help achieve sustainable development, it is essential to conduct research on addressing and mitigating ecological risks from the perspective of collaborative management networks in mountainous cities. Taking Zhangjiakou as the study area, this paper employed the land use transfer matrix and standard deviation ellipse methods to analyze the dynamic land use changes. Additionally, using Fragstats 4,2 to calculate the landscape indices with land use data, this paper evaluated the landscape ecological risk (LER) from 2000 to 2020. Furthermore, the social network analysis (SNA) method was utilized to explore the spatial correlation characteristics of the LER. The findings indicate that: (1) Cultivated land and grassland were the predominant land use types in Zhangjiakou. During 2000–2020, Zhangjiakou experienced significant changes in land use, dominated by the transfer among cultivated land, forestland, and grassland. It indicated that the issue of unstable ecological land use continued to exist. Affected by human activities, construction land showed a consistent upward trend, primarily concentrated in the urban built-up areas and areas along the Jing-Zhang Railway. (2) The LER of Zhangjiakou was predominantly characterized by low risk, medium risk, and high risk levels. In the transitional areas and foothills, the LER was relatively higher. During 2000–2020, Zhangjiakou showed a declining trend of LER. This suggested that the ecological protection policies in Zhangjiakou were effective, leading to an improvement in the local ecological environment. (3) The LER in Zhangjiakou demonstrated a spatial clustering pattern that exhibited an upward trend, which was supported by both spatial autocorrelation and the SNA analysis. In the LER collaborative management network, Xuanhua, Qiaodong, Qiaoxi, Wanquan and Zhangbei consistently upheld pivotal roles. Based on the number of inward and outward connections, 16 counties in Zhangjiakou were classified into four categories and three zones accompanied by corresponding recommendations. The findings of this study can serve as a valuable reference for subsequent landscape pattern optimization and ecological restoration in Zhangjiakou.

Dynamic land cover and ecosystem service changes in global coastal deltas under future climate scenarios

Coastal deltas, housing 4.5% of the global population, face profound shifts due to climate change and intensified human activities. This study utilizes the Patch-generating Land Use Simulation (PLUS) for detailed land cover dynamics and the Integrated Valuation of Ecosystem Services and Trade-Offs (InVEST) models for ecosystem services assessment, focusing on 46 global coastal deltas under future scenarios (SSP126, SSP245, SSP585). Our findings reveal a 145% increase in urban areas from 1995 to 2015, with expected continuations in urban sprawl. Grassland and barren areas are projected to diminish by up to 15% and 5%, respectively, with urban areas potentially expanding by up to 39% across scenarios. Significant decreases in surface water yield under SSP245 and SSP585 could challenge water sustainability. Moreover, a universal decline in carbon storage necessitates enhanced carbon sequestration strategies. Soil retention is anticipated to decline, leading to increased erosion risks, while habitat quality varies, improving in 29 deltas under SSP126 but worsening in 33 under SSP245 and SSP585. These insights underscore the urgency of strategic, adaptive land use planning to bolster ecosystem services, directly supporting the United Nations' Decade of Ocean Science for Sustainable Development and advancing global sustainable delta management.

Dynamic Land Degradation Assessment: Integrating Machine Learning with Landsat 8 OLI/TIRS for Enhanced Spectral, Terrain, and Land Cover Indices

Dynamic Land Degradation Assessment: Integrating Machine Learning with Landsat 8 OLI/TIRS for Enhanced Spectral, Terrain, and Land Cover Indices

Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes

Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing the intricate interplay between hydrological factors, their “black box” nature makes it challenging to identify the dynamic drivers of runoff. To overcome this challenge, we employed an interpretable machine learning method to inversely deduce the dynamic determinants within hydrological processes. In this study, we analyzed land use changes in the Ningxia section of the middle Yellow River across four periods, laying the foundation for revealing how these changes affect runoff. The sub-watershed attributes and meteorological characteristics generated by the Soil and Water Assessment Tool (SWAT) model were used as input variables of the Extreme Gradient Boosting (XGBoost) model to simulate substantial sub-watershed rainfall runoff in the region. The XGBoost was interpreted using the SHapley Additive exPlanations (SHAP) to identify the dynamic responses of runoff to the land use changes over different periods. The results revealed increasingly frequent interchanges between the land use types in the study area. The XGBoost effectively captured the characteristics of the hydrological processes in the SWAT-derived sub-watersheds. The SHAP analysis results demonstrated that the promoting effect of agricultural land (AGRL) on runoff gradually weakens, while forests (FRST) continuously strengthen their restraining effect on runoff. Relevant land use policies provide empirical support for these findings. Furthermore, the interaction between meteorological variables and land use impacts the runoff generation mechanism and exhibits a threshold effect, with the thresholds for relative humidity (RH), maximum temperature (MaxT), and minimum temperature (MinT) determined to be 0.8, 25 °C, and 15 °C, respectively. This reverse deduction method can reveal hydrological patterns and the mechanisms of interaction between variables, helping to effectively addressing constantly changing human activities and meteorological conditions.

The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback

Abstract. Terrestrial biosphere models are a key tool in investigating the role played by land surface in the global climate system. However, few models simulate the geographic distribution of biomes dynamically, opting instead to prescribe them using remote sensing products. While prescribing land cover still allows for the simulation of the impacts of climate change on vegetation growth and the impacts of land use change, it prevents the simulation of climate-change-driven biome shifts, with implications for the projection of future terrestrial carbon sink. Here, we isolate the impacts of prescribed vs. dynamic land cover implementations in a terrestrial biosphere model. We first introduce a new framework for evaluating dynamic land cover (i.e., the spatial distribution of plant functional types across the land surface), which can be applied across terrestrial biosphere models alongside standard benchmarking of energy, water, and carbon cycle variables in model intercomparison projects. After validating simulated land cover, we then show that the simulated terrestrial carbon sink differs significantly between simulations with dynamic vs. prescribed land cover for a high-CO2 future scenario. This is because of important range shifts that are only simulated when dynamic land cover is implemented: tree expansion into the Arctic and Amazonian transition from forest to grassland. In particular, the projected change in net land–atmosphere CO2 flux at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover. Our results illustrate the importance of climate-change-driven biome shifts for projecting future terrestrial carbon sink.

Forecasting Land Use Dynamics in Talas District, Kazakhstan, Using Landsat Data and the Google Earth Engine (GEE) Platform

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas.

Transitioning complex socioeconomic modeling to informed and visualized decision-making: A tightly coupled planning support system

The integration of urban models into decision-making remains challenging, primarily attributed to the complex nature of urban mechanisms. This study develops a PSS with sufficient methodological complexity to examine socioeconomic dynamics while simultaneously delivering modeling results in a manner that is understandable to non-expert stakeholders for informed decision-making. This paper tests, applies and implements the system in a real-life case at a fine scale collaborating with Sangamon County Regional Planning Commissions. It explores regional socioeconomic mechanisms across economic sectors addressing multicollinearity, projects potential consequences of an investment plan, and develops a web-based PSS tool for their informed decision-making. Our results identify how each economic sector behaves differently concerning location choices for future developments impacted by the investment, necessitating the use of differentiated planning approaches tailored to each sector. Findings underscore the value of assessing the heterogeneity of dynamic land development with spatial explicitness for informed decisions. We advocate for the delivery of this modeling via an interactive PSS platform, fostering responsive and collaborative dialogues. This paper contributes not only to the development of an integrated modeling framework but also to the planning literature, bridging the gap between complexity and accessibility in decision-making.

An advanced classification method for urban land cover classification

This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment. The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features. Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.

Optimization of land use to accommodate nutritional transformation of food systems: a case study from the Beijing–Tianjin–Hebei region

The transformation and reconstruction of China’s food system not only faces many risks, such as the unceasing growth of food consumption on the demand side and the structural imbalance of dietary nutrition, but also must address serious challenges, such as constraints of resources, environment, and production capacity on the supply side. The optimal allocation of land use structure is an important method to realizing a transformation of sustainable food systems, achieving the goal of nutrition security, and guiding coordinated spatial development. This study takes the Beijing–Tianjin–Hebei region as an example, analyzing the development trends of the region’s dietary nutrition structure clarifies the objectives for improving dietary nutrition. This study uses comprehensive optimization model and dynamic land system model, exploring land use optimization schemes under different nutritional goals and development scenarios. The result show that the dietary structure in the Beijing–Tianjin–Hebei region is transitioning from “food based” to “intake balance” and gradually evolved to “intake diversity,” with the main objectives being to maintain stable calorie intake while moderately increasing protein intake and reducing fat intake. Achieving this goal will gradually increase demand for cultivated land and intensify spatial competition for land use. However, by optimizing land use allocation, it is possible to free up more spatial resources to balance economic development and ecological protection and reduce land use fragmentation, thereby significantly enhancing regional economic benefits and the value of ecosystem services based on improvements in dietary nutrition.

Dynamic land cover evapotranspiration model algorithm: DyLEMa

This study presents the “Dynamic Land Cover Evapotranspiration Model Algorithm: DyLEMa” for continuous spatiotemporal evapotranspiration (ET) estimates across diverse land uses. DyLEMa employs a coupled Random Forest model with a novel dynamic recalibrating strategy to improve pre-optimized seasonal hyperparameters following satellite acquisition alongside land cover classes. An analysis of feature importance indicated the significant variability in ET processes across different land cover classes and seasons. Hence, DyLEMa was applied to 20 years of daily 30x30 m pixel resolution Landsat-derived ET data in Illinois to address spatial and temporal discontinuities due to cloud contamination and sensor failures. DyLEMa performance was evaluated on Eddy Covariance measurements to find out that DyLEMa predictions reduced the average PBIAS error from + 31 % to −7% compared to existing US Geological Survey ET products. Spatially, DyLEMa underscores the value of a land cover-aware approach in ET estimation under varied cloud cover rates and their ability to preserve landscape features. However, the performance of DyLEMa was affected by the quality of land cover classification, suggesting the need for a refined region-specific land cover classification. DyLEMa’s flexibility and performance suggest its applicability to other regions and satellite datasets, offering a promising reduction in uncertainty of ET estimates with impacts on environmental and water resources assessments on regional scales.

Analyzing land cover change dynamism through a GIS-based method: application to Gran Canaria (Canary Islands, Spain)

Landscapes are dynamic areas which can be studied from the perspective of different, but related, disciplines. The application of GIS methods to study landscape change is an interesting resource to help understand and explain landscape dynamics. Thus, the objective of this paper is to characterize land cover dynamism and identify land cover change ‘hotspots’. The method used here generates raster maps in which each pixel value represents the number of times each pixel has changed land cover between 1990-2018. Furthermore, it provides three of these maps, since it works with the 3 taxonomic levels of Corine Land Cover datasets, giving us different levels of detail in the analysis. On the other hand, the statistical treatment of the data has been done at the municipal level. The most important results reveal that Agüimes is the municipality with the most dynamic land cover change in the three levels, while Tejeda, in level 1, and Valsequillo, in levels 2 and 3, have the least changing land cover. These outputs are complemented with other statistical analyses which allow the integration of different data types such as those related to population, tourism and agriculture. Subsequently, some of the methodological issues and findings are discussed and put in context with the scientific literature.

Assessment of carbon emissions due to landscape fires in Ukraine during war in 2022

The russian military aggression and the related socio-economic and environmental consequences have significantly affected the climate and production of ecosystem services through damage to forests, ecosystems, landscape fires and emissions of gases into the atmosphere. The study aims to estimate carbon dioxide emissions due to landscape fires in Ukraine during the year 2022. The OroraTech wildfire monitoring technology was used to detect fires, while perimeters of burned areas were delineated with Sentinel 2 time series. The Copernicus Dynamic Land Cover map was used to extract burned land covers. Emissions were calculated based on the intensity of fires (dNBR) with the share of burned biomass in different types of land cover. Biomass models were selected considering the dominant tree species within a specific region and the species structure of the sown areas of croplands. The volume of biomass losses was estimated as a result of fires of different severities. It was estimated that during in 2022, landscape fires burned 749.5 thousand hectares thereof: croplands – 419.1 thousand hectares; other natural vegetation – 273.8 thousand hectares; conifer forests – 31.1 thousand hectares; other forests – 25.5 thousand hectares. The impact of the war on landscape fires is confirmed by the large proportion of fires in the 60-kilometre buffer zone along the frontline – 68.9% of the total area of fire. Among all fires, 42.5% of fires occurred in the occupied territory. Total CO2 emissions from all types of landscape fires reached 5.20 million tons and other greenhouse gases – 0.28 million tons. It is the first detailed mapping of landscape fires with an analysis of each polygon for the whole territory of Ukraine. The results provide important information for assessing the loss of ecosystem services and estimating carbon dioxide emissions as well as for confirming the impact of hostilities on landscape fires

A novel and dynamic land use/cover change research framework based on an improved PLUS model and a fuzzy multiobjective programming model

Spatial reconstruction and scenario simulation of historical processes and future trends of land use/cover change (LUCC) can help to reveal the historical background of land conversion and the spatial distribution of future land. Moreover, there is a close relationship between the spatiotemporal dynamics of land use/cover and changes in different ecosystem services (ESs). Using this relationship to simulate future land use scenarios is important. In this study, an LUCC dynamic analysis framework (LSTM-PLUS-FMOP) was constructed based on a deep learning time series forecasting model (LSTM), a parallelized urban land use simulation (PLUS) model and a fuzzy multiobjective programming (FMOP) model. The PLUS model was used to analyze the driving mechanism of land expansion and explore the land conversion pattern. In addition, three land conversion scenarios were established: natural land expansion (NLE), economic development priority (EDP) and regional sustainable development (RSD). The FMOP model and the relationship between LUCC and ES were used to perform a spatial simulation of land conversion. The uncertainty parameters in the model were treated by intuitionistic fuzzy numbers (IFSs). This study applied the constructed framework to the Yellow River Basin of Shaanxi Province (YRB-SX). The results showed that (1) from 2000 to 2020, the cropland area of the YRB-SX continuously decreased by 12.67 × 104 ha, while the built-up area continuously increased by 28.25 × 104 ha. The net reduction in woodland and grassland area was 13.90 × 104 ha. (2) The relative error range of land prediction using the LSTM model was 0.0003– 0.0042. This model had better accuracy than the Markov chain prediction model. (3) The cropland area decreased by 0.26% (NLE), 0.85% (EDP) and 1.68% (RSD) under the three scenarios. The built-up area increased by 25.01%, 32.76% and 14.72%, respectively. The RSD scenario followed the principles of ecological protection and spatial constraints, which mitigated the degradation of the ecosystem to some extent. This coupled simulation framework will help to obtain land allocation schemes that meet the requirements of ecological protection and provide solutions for rational land management.

Impacts of highway induced land use changes on transport demand

Highway investment appraisals usually do not account for land use changes triggered by the investment. However, previous studies argue that new highways promote sprawling land use development associated with greater car dependency and increased motorized transport demand. This paper aims to increase the knowledge of how land use changes resulting from new highways impact transport demand. We model induced transport demand from seven highway investments, assuming both fixed and dynamic land use between the build and the no build scenarios. When land use is allowed to change, relocation of population is estimated with a regression model building on existing dynamics between population density and job accessibility. Our results indicate that land use changes resulting from highway investments impact induced transport demand. Highways that provide substantial travel time savings near urban areas are found to generate most induced transport demand from land use changes. However, induced demand from land use changes constitute a modest proportion of total induced demand resulting from the highways.

Phosphorus limitation on CO2 fertilization effect in tropical forests informed by a coupled biogeochemical model

Tropical forests store more than half of the world's terrestrial carbon (C) pool and account for one-third of global net primary productivity (NPP). Many terrestrial biosphere models (TBMs) estimate increased productivity in tropical forests throughout the 21st century due to CO2 fertilization. However, phosphorus (P) limitations on vegetation photosynthesis and productivity could significantly reduce the CO2 fertilization effect. Here, we used a carbon-nitrogen-phosphorus coupled model (Dynamic Land Ecosystem Model; DLEM-CNP) with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests during 1860–2018. Our model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem productivity. We further compared the simulations from DLEM-CNP, DLEM-CN, and DLEM-C and the results showed that the inclusion of P processes reduced the CO2 fertilization effect on gross primary production (GPP) by 25% and 45%, and net ecosystem production (NEP) by 28% and 41%, respectively, relative to CN-only and C-only models. From the 1860s to the 2010s, the DLEM-CNP estimated that in tropical forests GPP increased by 17%, plant respiration (Ra) increased by 18%, ecosystem respiration (Rh) increased by 13%, NEP increased by 121% per unit area, respectively. Additionally, factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO2 fertilization effect had been offset by 135% due to deforestation from the 1860s to the 2010s. Our study highlights the importance of P limitation on the C cycle and the weakened CO2 fertilization effect resulting from P limitation in tropical forests.

Experimental Nonrobocentric Dynamic Landing of Quadrotor UAVs With On-Ground Sensor Suite

Experimental Nonrobocentric Dynamic Landing of Quadrotor UAVs With On-Ground Sensor Suite