Parcel Map Research Articles

Functional connectome through the human life span.

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning

Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions. Essentially, longitudinal cortical surface registration and parcellation are highly correlated tasks with inherently shared constraints on both spatial and temporal feature representations, which are unfortunately ignored in existing methods. To this end, we unprecedentedly propose a novel semi-supervised learning framework to exploit these inherent relationships from limited labeled data and extensive unlabeled data for more robust and consistent registration and parcellation of longitudinal cortical surfaces. Our method utilizes the spherical topology characteristic of cortical surfaces. It employs a spherical network to function as an encoder, which extracts high-level cortical features. Subsequently, we build two specialized decoders dedicated to the tasks of registration and parcellation, respectively. To extract more meaningful spatial features, we design a novel parcellation map similarity loss to utilize the relationship between registration and parcellation tasks, i.e., the parcellation map warped by the deformation field in registration should match the atlas parcellation map, thereby providing extra supervision for the registration task and augmented data for parcellation task by warping the atlas parcellation map to unlabeled surfaces. To enable temporally more consistent feature representation, we additionally enforce longitudinal consistency among longitudinal surfaces after registering them together using their concatenated features. Experiments on two longitudinal datasets of infants and adults have shown that our method achieves significant improvements on both registration/parcellation accuracy and longitudinal consistency compared to existing methods, especially in small and challenging cortical regions.

Implementation of Community-Based Surveying and Mapping of Land Parcels through Field Work Practice to Support Complete Systematic Land Registration (PTSL)

Cadastral surveying and mapping field work practice is an activity for National Land Collage (STPN) as one of the units under the Ministry of ATR/BPN to develop innovation that can be used as a basis for recommendations for the success of Complete Systematic Land Registration (PTSL). In the year of 2021, the Diploma I of Cadastral Surveying and Mapping Program carried out field work practice of cadastral surveying and mapping in Pagerharjo Village. This field work practice was designed by optimizing the Fit for Purpose Land Administration (FFP-LA) approach to accelerate the land parcel surveying and mapping process. The method used in the research underlying this article is a qualitative method to find out the extent to which the FFP concept can be implemented in land registration of Indonesia during the field work practice. The result of the field work practice shows several factors that determine the success of the land parcels surveying and mapping based on FFP-LA concepts, including the requirements of open areas for implementing photogrammetry-based measurements, the need of easily identified boundaries marker on Photo Map, the awareness of protecting property by the land owners, and the strength of legal guarantees based on the accuracy of measurements.

Subjective states induced by intracranial electrical stimulation matches the cytoarchitectonic organization of the human insula

Functions of the human insula have been explored extensively with neuroimaging methods and intracranial electrical stimulation studies that have highlighted a functional segregation across its subregions. A recently developed cytoarchitectonic map of the human insula has also segregated this brain region into various areas. Our knowledge of the functional organization of this brain region at the level of these fine-parceled microstructural areas remains only partially understood. We address this gap of knowledge by applying a multimodal approach linking direct electrical stimulation and task-evoked intracranial EEG recordings with microstructural subdivisions of the human insular cortex. In 17 neurosurgical patients with 142 implanted electrodes, stimulation of 40 % of the sites induced a reportable change in the conscious experience of the subjects in visceral/autonomic, anxiety, taste/olfactory, pain/temperature as well as somatosensory domains. These subjective responses showed a topographical allocation to microstructural areas defined by probabilistic cytoarchitectonic parcellation maps of the human insula. We found the pain and thermal responses to be located in areas lg2/ld2, while non-painful/non-thermal somatosensory responses corresponded to area ld3 and visceroceptive responses to area Id6. Lastly, the stimulation of area Id7 in the dorsal anterior insula, failed to induce reportable changes to subjective experience even though intracranial EEG recordings from this region captured significant time-locked high-frequency activity (HFA). Our results provide a multimodal map of functional subdivisions within the human insular cortex at the individual brain basis and characterize their anatomical association with fine-grained cytoarchitectonic parcellations of this brain structure.

E2EVAP: End-to-end vectorization of smallholder agricultural parcel boundaries from high-resolution remote sensing imagery

Rapid and accurate agricultural parcel mapping from high-resolution remote sensing imagery is fundamental to precision agriculture for smallholder farming systems. However, due to the narrow and small-size parcels, and the significant spatio-spectral variability, the existing two-stage segmentation methods cannot extract individual parcels automatically. In this article, the end-to-end vectorization of smallholder agricultural parcel boundaries (E2EVAP) framework is proposed for extracting the vertices of each parcel boundary individually in smallholder farming regions, where the semantic-contour interaction and topological loss through hierarchical instance representation (called “channel to instance”) are designed for aggregating the foreground features and jointly establishing the topological relationship between instances to alleviate the topological overlap between parcel objects. Vertex shift correction based on the deep attention corner snake module guided by the parcel boundaries is also proposed to adaptively correct the boundary vertex location shift of large-scale irregular parcels. The comprehensive experimental results obtained on the iFLYTEK public agricultural parcel dataset confirm that E2EVAP shows a superior performance (with a mask mAP of 0.335 and a boundary mAP of 0.201), compared with the pixel-based (such as Mask R-CNN, HTC, and SOLOv2) and contour-based (such as DeepSnake and E2EC) benchmark methods. We believe that E2EVAP has the potential to be widely used for accurate vectorization mapping of agricultural parcels in smallholder farming areas such as South Africa and southern China. The code of E2EVAP is at https://github.com/YangPanHZAU/E2EVAP.

Fine-grained functional parcellation maps of the infant cerebral cortex.

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.

Estimating the change in the rational runoff coefficient through history in Jefferson County, Texas

Jefferson County in Texas has experienced devastating storms in recent years resulting in billions of dollars in damages. The county has gone through temporal milestones in terms of population growth and industrial development. Many studies have shown that urban development increases the risk of flooding by decreasing the soil infiltration capacity. The current study focuses on estimating the extent to which the urban development in the county has led to increased imperviousness using a combination of historical and current spatial data. Rational runoff coefficients of the County were estimated and compared at three different times over a span of 120 years. A land survey map for 1898, an aerial imagery map for 1966 and a land parcel map for 2019 were obtained from various sources. The three maps available, each in different format, were analyzed to determine the land use and land cover type for the respective years. The runoff coefficient increased by 21% from 1898 to 1966 and remained the same from 1966 till 2019. The estimates are in correlation with industrial and population growth patterns of the county. These preliminary spatial analyses are useful in estimating the contributions of recent major flooding from overall development, or if they may be more impacted from other factors such as changes in weather patterns.

Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains.

Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.SIGNIFICANCE STATEMENT Genetic influences on cortical thickness (CT) and surface area (SA) are complex and could evolve throughout the lifespan. However, studies revealing distinct genetic topography of CT and SA have been limited to adults. Using brain structural magnetic resonance (MR) images of twins, we unprecedentedly discovered the distinct genetically-informed parcellation maps of CT and SA in neonatal brains, respectively. Each genetic parcellation map comprises a distinct spatial layout of cortical regions, where vertices within the same region share high genetic correlation. These genetic parcellation maps of CT and SA of neonates largely differ from those of adults, despite their highly remarkable similarities in the medial cortex of SA. These discoveries provide important insights into the genetic organization of the early cerebral cortex development.

Extraction and Mapping of Cropland Parcels in Typical Regions of Southern China Using Unmanned Aerial Vehicle Multispectral Images and Deep Learning

The accurate extraction of cropland distribution is an important issue for precision agriculture and food security worldwide. The complex characteristics in southern China pose great challenges to the extraction. In this study, for the objective of accurate extraction and mapping of cropland parcels in multiple crop growth stages in southern China, we explored a method based on unmanned aerial vehicle (UAV) data and deep learning algorithms. Our method considered cropland size, cultivation patterns, spectral characteristics, and the terrain of the study area. From two aspects—model architecture of deep learning and the data form of UAV—four groups of experiments are performed to explore the optimal method for the extraction of cropland parcels in southern China. The optimal result obtained in October 2021 demonstrated an overall accuracy (OA) of 95.9%, a Kappa coefficient of 89.2%, and an Intersection-over-Union (IoU) of 95.7%. The optimal method also showed remarkable results in the maps of cropland distribution in multiple crop growth stages, with an average OA of 96.9%, an average Kappa coefficient of 89.5%, and an average IoU of 96.7% in August, November, and December of the same year. This study provides a valuable reference for the extraction of cropland parcels in multiple crop growth stages in southern China or regions with similar characteristics.

BSNet: Boundary-semantic-fusion network for farmland parcel mapping in high-resolution satellite images

Mapping farmland parcels using satellite images is essential for agricultural remote sensing applications. Loss of spatial details and positioning of parcel boundaries are the main challenges in available deep convolution network (DCN) models. This study developed a boundary-semantic-fusion DCN (BSNet) model for delineating farmland parcels from high-resolution satellite images. Central to this method is the combination between shallow-level boundary features with accurate spatial positioning and deep-level semantic features for category identification. First, a general deep convolution framework consisting of boundary, semantic and fusion blocks was implemented for farmland parcel mapping. Second, a particular serial structure with a detaching operation for linking the boundary and semantic blocks was explored to maintain the spatial details and fine-scale boundaries in feature learning. Third, an encoder-decoder fusion block was developed to integrate the boundary and semantic features to produce the final parcel maps. We validated the proposed model with different high-resolution satellite images in two study areas. The experimental results, with improvements greater than 4% in the F1 score and 6% in the IoU score relative to other comparative methods, illustrate the effectiveness of the proposed model for fine-scale farmland parcel mapping.

Restricted language access during childhood affects adult brain structure in selective language regions

Due to the ubiquitous nature of language in the environment of infants, how it affects the anatomical structure of the brain language system over the lifespan is not well understood. In this study, we investigated the effects of early language experience on the adult brain by examining anatomical features of individuals born deaf with typical or restricted language experience in early childhood. Twenty-two deaf adults whose primary language was American Sign Language and were first immersed in it at ages ranging from birth to 14 y participated. The control group was 21 hearing non-signers. We acquired T1-weighted magnetic resonanceimages and used FreeSurfer [B. Fischl,Neuroimage 62, 774-781(2012)]to reconstruct the brain surface. Using an a priori regions of interest (ROI) approach, we identified 17 language and 19 somatomotor ROIs in each hemisphere from the Human Connectome Project parcellation map [M. F. Glasseretal.,Nature 536, 171-178 (2016)]. Restricted language experience in early childhood was associated with negative changes in adjusted grey matter volume and/or cortical thickness in bilateral fronto-temporal regions. No evidence of anatomical differences was observed in any of these regions when deaf signers with infant sign language experience were compared with hearing speakers with infant spoken language experience, showing that the effects of early language experience on the brain language system are supramodal.

Between-subject similarity of functional connectivity-based organization of the human periaqueductal gray related to autonomic processing.

The periaqueductal gray (PAG) is a brain stem area designated to play an essential role in lower urinary tract (LUT) control. Post-mortem human and animal studies have indicated that the PAG is symmetrically organized in functionally and anatomically distinct columns which are involved in sympathetic or parasympathetic autonomic control of the LUT. The current study aims to find consistency across subjects and identify homologous clusters between subjects. Here, we evaluated data from 10 female participants. During a bladder filling protocol, we ran a resting-state functional magnetic resonance imaging (fMRI) scan while participants experienced a strong desire to void. A voxel-by-voxel correlation matrix of the PAG was created and parcellated using the Louvain module detection algorithm. Resulting in a map of the PAG in which each voxel is assigned to a cluster as determined by the Louvain module detection algorithm. The spatial similarity of resulting clusters between participants was assessed by computing the Dice similarity coefficient for all cluster comparisons. Next, we designed a permutation test to create randomized parcellation maps which enabled us to statistically test the similarity values observed across participants. We observed several significantly similar clusters between subjects compared to permutations (p ≤ 0.05). These results show that the PAG can be parcellated into distinct clusters which show a similar spatial distribution at the group level. This analysis is a crucial step to determine the agreement between in vivo PAG parcellations and the functional and anatomical columnar organization of the PAG which is known from previous research. These advancements may enable us to identify the relationship between LUT symptoms, such as urgency, and activity patterns in the PAG in normal and pathological states.

Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification.

Iron-rich deep brain nuclei (DBN) of the human brain are involved in various motoric, emotional and cognitive brain functions. The abnormal iron alterations in the DBN are closely associated with multiple neurological and psychiatric diseases. Quantitative susceptibility mapping (QSM) provides the spatial distribution of the magnetic susceptibility of human brain tissues. Compared to traditional structural imaging, QSM provides superiority for imaging the iron-rich DBN owing to the susceptibility difference existing between brain tissues. In this study, we constructed a Montreal Neurological Institute (MNI) space unbiased QSM human brain atlas via group-wise registration from 100 healthy subjects aged 19-29 years. The atlas construction process was guided by hybrid images that were fused from multi-modal magnetic resonance images (MRI). We named it as Multi-modal-fused magnetic Susceptibility (MuSus-100) atlas. The high-quality susceptibility atlas provides extraordinary image contrast between iron-rich DBN with their surroundings. Parcellation maps of DBN and their subregions that are highly related to neurological and psychiatric pathology were then manually labeled based on the atlas set with the assistance of an image border-enhancement process. Especially, the bilateral thalamus was delineated into 64 detailed subregions referring to the Schaltenbrand-Wahren stereotactic atlas. To our best knowledge, the histological-consistent thalamic nucleus parcellation map is well defined for the first time in the MNI space. Compared with existing atlases that emphasizing DBN parcellation, the newly proposed atlas outperforms on the task of atlas-guided individual brain image DBN segmentation both in accuracy and robustness. Moreover, we applied the proposed DBN parcellation map to conduct detailed identification of the pathology-related iron content alterations in subcortical nuclei for Parkinson's Disease (PD) patients. We envision that the MuSus-100 atlas can play a crucial role in improving the accuracy of DBN segmentation for the research of neurological and psychiatric disease progress and also be helpful for target planning in deep brain stimulation surgery.

Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset

Recently, deep neural networks have shown great potential for solving dipole inversion of quantitative susceptibility mapping (QSM) with improved results. However, these studies utilized their limited dataset for network training and inference, which may lead to untrustworthy conclusions. Thus, a common dataset is needed for a fair comparison between different QSM reconstruction networks. Additionally, finding an in vivo reference susceptibility map that matches acquired single-orientation phase data remains an open problem. Susceptibility tensor imaging (STI) χ33 and Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) are considered reference susceptibility candidates. However, a large number of multi-orientation GRE data for both STI and COSMOS reconstruction are now unavailable for training supervised neural networks for QSM. In this study, we reported the largest multi-orientation dataset, to the best of our knowledge in the QSM research field, with a total of 144 scans from 8 healthy subjects collected using a 3D GRE sequence from the same MR scanner. In addition, the parcellation of deep gray matter is also provided for automatically extracting susceptibility values. Five recently developed deep neural networks, i.e., xQSM, QSMnet, autoQSM, LPCNN, and MoDL-QSM were performed on this dataset. This potential data source could provide a common framework and labels to test the accuracy and robustness of deep neural networks for QSM reconstruction. This dataset has the potential to provide a benchmark of reference susceptibility for the deep learning-based QSM methods. Additionally, the trained COSMOS-labeled and χ33-labeled networks were tested on the pathological data to explore their potential applications. The data together with deep gray matter parcellation maps are now publicly available via an open repository at https://osf.io/yfms7/, and the raw multi-orientation GRE data are also available at https://osf.io/y6rc3/.

The Bounty of Buffers: Spatial Measurement of Displacement and Diffusion of Benefits of Alarms on Burglary

This study analyzes the extent to which alarm systems impact geographical displacement and/or diffusion of benefits on burglary, which regards as a substitute for the absence of capable guardians. A quasi-experimental design with three nested concentric zones—target, buffer, and control—are utilized by incorporating the WDQ conceptual approach with GIS and a parcel map. The datasets include burglary incidents and alarm permit records. Alarms produce a sizeable impact on burglary reduction. No indication of spatial displacement is observed from protected houses to nearby houses. Alarms create a short geographic ambit and a wider spatial range of diffusion of benefits. A burglar alarm can protect the house without displacing burglary to nearby houses and provides neighboring houses with protection as well.

Multiscale and efficient recognition of land cover by using both decomposition and deep visual feature analysis

As we know that when we use the colour bands that are pure in nature it will help some of the public authorities to update the land information. It will be a sensing platform so it will take less time and the cost we will be spending will not be much heavy, in that manner aircraft system will be used that should be unmanned. Here we will be using a method for optical aerial imagery that will come under land use classification with decision tree. In the beginning the ownership parcel map will come under play for taking the land cover information through maximum likelihood classifier. Then, to make the relation between land usage and land coverage we will generate the decision tree. A well structured parcel map will be produced by the merits of geographical characteristics of parcels.Hence the aftereffects of the spatial resolution under the classification approach will be discussed completely by sampling them again from the 20 then 50 then 100cm. This land use can be widely used in landscape and urban planning because of the flexible classification method. Using aerial images the object oriented approach for image based analysis is used for urban land cover mapping.

Ecojustice Poetry in The BreakBeat Poets Anthologies

Ecological modes of thinking and an awareness of environmental (in)justice are becoming increasingly pronounced in the ethics and aesthetics of hip hop. One area in which the culture’s growing interest in ecology as practice and metaphor is particularly visible is hip hop poetry’s turn to ecojustice, or an intersectional concern with social and environmental justice, liberation, diversity, and sustainability. This article examines selected works from the first two volumes of anthologies published by Haymarket Books as part of their BreakBeat Poets series, focusing on three ecojustice-oriented poems that address animal rights, (un)natural disasters, and gentrification. Their authors–all Black women– draw from African American history and culture to illuminate the intertwined ideological, political, and economic dimensions of some of the most pressing humanitarian and environmental crises of today. Samantha Thornhill’s “Ode to a Killer Whale” takes the form of a poetic monologue by the fictional character of Kunta Kinte, revealing similarities between human and animal subjugation and inscribing animal liberation in the Black revolutionary tradition. Candace G. Wiley’s “Parcel Map for the County Assessor” re-members and re-creates a culture of place that permeated the speaker’s countryside childhood to present the larger-than-human cost of rural gentrification. Finally, Mariahadessa Ekere Tallie’s “Global Warming Blues” juxtaposes the personal and the elemental dimensions of climate change in a blues remix that advocates for ecojustice for the disenfranchised.

Accuracy Issues for Spatial Update of Digital Cadastral Maps

All geospatial data are updated periodically. Cadastral parcel mapping, however, has special update requirements that set it apart from other geospatial data. Mapped boundaries change continuously to fit with new survey plans. Additionally, new parcels have to be fitted and aligned with adjoining parcels to merge them into existing cadastral mapping. This is preferably performed by a spatial adjustment approach to systematically improve its accuracy over time. This paper adapts methods for analysis and adjustment of survey networks to improve the accuracy of cadastral mapping with better coordinate positioning and survey plan dimensions. Case studies for both hypothetical and real cadastral mapping are used to illustrate the issues and spatially resolve errors. Adjustment results achieve an accuracy consistent with other GIS layers and boundary features visible in high-resolution orthoimagery. Graphical charts based on stress–strain relationships provide a simplified means to interpret post-adjustment results to identify and fix potential errors.

Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions.

Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders.

Small-Area Estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a Fine-Scale Land Cover and Ownership Layer to Support County-Level Population Estimates

Small area estimation is a powerful modeling technique in which ancillary data can be utilized to “borrow” additional information, effectively increasing sample sizes in small spatial, temporal, or categorical domains. Though more commonly applied to biophysical variables within the study of forest inventory analyses, small area estimation can also be implemented in the context of understanding social values, behaviors, and trends among types of forest landowners within small domains. Here, we demonstrate a method for deriving a continuous fine-scale land cover and ownership layer for the state of Delaware, United States, and an application of that ancillary layer to facilitate small-area estimation of several variables from the USDA Forest Service’s National Woodland Owner Survey. Utilizing a proprietary parcel layer alongside the National Land Cover Database, we constructed a continuous layer with 10-meter resolution depicting land cover and land ownership classes. We found that the National Woodland Owner Survey state-level estimations of total acreage and total ownerships by ownership class were generally within one standard error of the population values calculated from the raster layer, which supported the direct calculation of several population-level summary variables at the county levels. Subsequently, we compare design-based and model-based methods of predicting commercial harvesting by family forest ownerships in Delaware in which forest ownership acreage, taken from the parcel map, was utilized to inform the model-based approach. Results show general agreement between the two modes, indicating that a small area estimation approach can be utilized successfully in this context and shows promise for other variables, especially if additional variables, e.g., United States Census Bureau data, are also incorporated.