Multistage, multiband and sequential imagery to identify and quantify non-forest vegetation resources

Earth Resources photographs from Apollo 6, 7, and 9 and photographs taken during Gemini 4, were used in the research along with high altitude and conventional aerial photography. A unified land use and resource analysis system was devised and used to develop a mapping legend. The natural vegetation, land use, macrorelief, and landforms of northern Maricopa County, Arizona, were analyzed and inventoried. This inventory was interpreted in relation to the critical problem of urban expansion and agricultural production in the study area. The central thrust of the research program has been to develop methods for use of space and small-scale, high-altitude aerial photography to develop information for land use planning and resource allocation decisions

Deep Learning-Based Part Labeling of Tree Components in Point Cloud Data

Point cloud data analysis plays a crucial role in forest management, remote sensing, and wildfire monitoring and mitigation, necessitating robust computer algorithms and pipelines for segmentation and labeling of tree components. This thesis presents a novel pipeline that employs deep learning models, such as the Point-Voxel Transformer (PVT), and synthetic tree point clouds for automatic tree part-segmentation. The pipeline leverages the expertise of environmental artists to enhance the quality and diversity of training data and investigates alternative subsampling methods to optimize model performance. Furthermore, we evaluate various label propagation techniques to improve the labeling of synthetic tree point clouds. By comparing different community detection methods and graph connectivity inference techniques, we demonstrate that K-NN connectivity inference and carefully selected community detection methods significantly enhance labeling accuracy, efficiency, and coverage. The proposed methods hold the potential to improve the quality of forest management and monitoring applications, enable better assessment of wildfire hazards, and facilitate advancements in remote sensing and forestry fields

Deep Learning-Based Part Labeling of Tree Components in Point Cloud Data

Point cloud data analysis plays a crucial role in forest management, remote sensing, and wildfire monitoring and mitigation, necessitating robust computer algorithms and pipelines for segmentation and labeling of tree components. This thesis presents a novel pipeline that employs deep learning models, such as the Point-Voxel Transformer (PVT), and synthetic tree point clouds for automatic tree part-segmentation. The pipeline leverages the expertise of environmental artists to enhance the quality and diversity of training data and investigates alternative subsampling methods to optimize model performance. Furthermore, we evaluate various label propagation techniques to improve the labeling of synthetic tree point clouds. By comparing different community detection methods and graph connectivity inference techniques, we demonstrate that K-NN connectivity inference and carefully selected community detection methods significantly enhance labeling accuracy, efficiency, and coverage. The proposed methods hold the potential to improve the quality of forest management and monitoring applications, enable better assessment of wildfire hazards, and facilitate advancements in remote sensing and forestry fields

Monitoring the defoliation of hardwood forests in Pennsylvania using LANDSAT

An automated system for conducting annual gypsy moth defoliation surveys using LANDSAT MSS data and digital processing techniques is described. A two-step preprocessing procedure was developed that uses multitemporal data sets representing forest canopy conditions before and after defoliation to create a digital image in which all nonforest cover types are eliminated or masked out of a LANDSAT image that exhibits insect defoliation. A temporal window for defoliation assessment was identified and a statewide data base was established. A data management system to interface image analysis software with the statewide data base was developed and a cost benefit analysis of this operational system was conducted

Potential applications of randomised graph sampling to invasive species surveillance and monitoring.

Abstract Many invasive plants and animals disperse preferentially through linear networks in the landscape, including road networks, riparian corridors, and power transmission lines. Unless the network of interest is small, or the budget for surveillance is large, it may be necessary to draw inferences from a sample rather than a complete census on the network. Desired features of a surveillance system to detect and quantify invasion include: (1) the ability to make unbiased statements about the spatial extent of invasion, the abundance of the invading organism, and the degree of impact; (2) the ability to quantify the uncertainty associated with those statements; (3) the ability to sample by moving within the network in a reasonable fashion, and with little wasted non-measurement time; and (4) the ability to incorporate auxiliary information (such as remotely sensed data, ecological models, or expert opinion) to direct sampling where it will be most fruitful. Randomised graph sampling (RGS) has all of these attributes. The network of interest (such as a road network) is recomposed into a graph, consisting of vertices (such as road intersections) and edges (such as road segments connecting nodes). The vertices and edges are used to construct paths representing reasonable sampling routes through the network; these paths are then sampled, potentially with unequal probability. Randomised graph sampling is unbiased, and the incorporation of auxiliary information can dramatically reduce sample variances. We illustrate RGS using simplified examples, and a survey of Polygonum cuspidatum (Siebold & Zucc.) within a high-priority conservation region in southern Maine, USA

Small area estimation of county-level forest attributes using forest inventory data and remotely sensed auxiliary information

The Forest Inventory and Analysis (FIA) program of the United States Department of Agriculture Forest Service collects forest inventory data that provide estimates with reasonable accuracy at the national scale. However, for smaller domains, these estimates are often not as accurate due to the small sample size. Small area estimation improves the accuracy of the estimates at smaller domains by relying on auxiliary information. This study compared direct (FIA estimates), indirect (multiple linear regression), and composite estimators (Fay-Herriot) using auxiliary information derived from Landsat and Global Ecosystem Dynamics Investigation (GEDI) to obtain county-level estimates of forest attributes namely total and merchantable volume (m3 ha-1), aboveground biomass (Mg ha-1), basal area (m2 ha-1), and Lorey’s mean height (m). Compared with FIA estimates, the composite estimator reduced error by 75-78% for all the variables of interest. This shows that a reasonable amount of precision can be achieved with auxiliary information from Landsat and GEDI, improving FIA estimates at the county level

Food Security Survey: Phase I, Agricultural Production and Land Use Season 2000A

REPUBLIC OF RWANDA, MINISTRY OF AGRICULTURE, ANIMAL RESOURCES, AND FORESTRY, Food Security Research Project (FSRP) and Division of Agricultural Statistics (DSA)food security, food policy, Rwanda, agricultural production, land use, Food Security and Poverty, Q18,

Modelling soil erosion and transport in the Burrishoole catchment, Newport, Co. Mayo, Ireland

The Burrishoole catchment is situated in County Mayo, on the northwest coast of the Republic of Ireland. Much of the catchment is covered by blanket peat that, in many areas, has become heavily eroded in recent years. This is thought to be due, primarily, to the adverse effects of forestry and agricultural activities in the area. Such activities include ploughing, drainage, the planting and harvesting of trees, and sheep farming, all of which are potentially damaging to such a sensitive landscape if not managed carefully. This article examines the sediment yield and hydrology of the Burrishoole catchment. Flow and sediment concentrations were measured at 8-hourly intervals from 5 February 2001 to 8 November 2001 with an automatic sampler and separate flow gauge, and hourly averages were recorded between 4 July 2002 and 6 September 2002 using an automatic river monitoring system [ARMS]. The authors describe the GIS-based model of soil erosion and transport that was applied to the Burrishoole catchment during this study. The results of these analyses were compared, in a qualitative manner, with the aerial photography available for the Burrishoole catchment to see whether areas that were predicted to contribute large proportions of eroded material to the drainage network corresponded with areas where peat erosion could be identified through photo-interpretation

Monitoring forest canopy alteration around the world with digital analysis of LANDSAT imagery

There are no author-identified significant results in this report