The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. data contain three-dimensional structure information that can be used estimate height, base crown depth, and diameter. This allows precision down trees. Unlike most published detect trees from a LiDAR-derived raster surface, we worked directly with separate metrics. Testing results in typical forests are encouraging. Future works will...

We developed a neural network based approach to identify urban tree species at the individual level from lidar and hyperspectral imagery. This is capable of modeling characteristics multiple spectral signatures within each using an internally unsupervised engine, able catch differences between externally supervised system. To generate species-level map for forest with high spatial heterogeneity diversity, we conducted treetop-based identification. can avoid problems double-sided...

Age is a powerful variable that can be of significant value when modelling the health forest-dominated ecosystem. Traditional investigations have attempted to extract age information from remotely sensed data by regressing spectral values with in situ derived data. statistical approaches assume (a) normally distributed remote sensing and data, (b) no collinearity among variables, (c) linear relationships. Artificial neural networks (ANNs) are not bound such assumptions may yield improved...

The spatial distribution of forest stands is one the fundamental properties forests. Timely and accurately obtained stand can help people better understand, manage, utilize development remote sensing technology has made it possible to map tree species in a timely accurate manner. At present, large amount data have been accumulated, including high-spatial-resolution images, time-series light detection ranging (LiDAR) data, etc. However, these not fully utilized. To identify stands, various...

Deer surveys play an important role in the estimation of local ecological balance. In Chitwan National Park Nepal, dense tree canopies and tall vegetation often obscure presence wild deer, which has a negative effect on accurate population deer. DJI drones equipped with infrared sensors have been widely used to regularly monitor deer conservation areas capture lot thermal images. How automatically review count number objects from images is becoming more important. Due difference between RGB...

Abstract Speckle noise is the grainy salt-and-pepper pattern present in radar imagery caused by interaction of out-of-phase waves with a target. A local adaptive median filter was developed that uses statistics to detect SAR speckle and replace it value. The performance evaluated using RADARSAT JERS-1 datasets based on both visual assessment number numerical measures. In comparison established filters, found outperformed others achieving best balance between suppression image detail preservation.

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being 'black box'. It extremely difficult document how specific decisions are reached. Fuzzy systems, on the other hand, capability represent explicitly in form of fuzzy 'if-then' rules. However, construction knowledge base, especially fine-tuning set parameters rules expert system,...

Dasymetric areal interpolation is the process by which data are transferred from a spatial unit system for they available (source units) to another required (target with aid of ancillary information (control units). We propose spatially disaggregated model population using light detection and ranging ( LiDAR )‐derived building volumes as an variable. Innovative methods proposed initialization, iterative regression adjustment, stopping criteria deal effectively control units unequal size. The...

Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose new method integrates newly developed constrained spectral variance difference (CSVD) edge penalty (EP). First, initial segments are produced by fast scan. Second, generated merged...

Abstract The collection of population by census is laborious, time consuming and expensive, often only available at limited temporal spatial scales. Remote sensing based estimation has been employed as a viable alternative for providing estimates on indicators that make use two-dimensional areal information buildings or one-dimensional length roads recent advancement LIDAR remote provides the opportunity to add third dimension height into modeling distribution. This study explores building...

The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for erosion monitoring in this region. Among the different types Plateau, bank usually regarded as most important source generation of sediment. However, approaches extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, Zhifanggou catchment Plateau. approach was...

Areal interpolation is the data transfer from one zonal system to another. A survey of previous literature on this subject points out that most effective methods for areal are intelligent approaches, which often take two-dimensional (2-D) land use or one-dimensional (1-D) road network information as ancillary give insight underlying distribution a variable. However, 2-D 1-D not always applicable variable interest in specific study area. This article introduces point-based approach problem by...

Spectral mixture analysis has been frequently applied in various fields to solve the mixed pixel problem remote sensing. So far, all research focused on sub-pixel analysis, i.e., selecting endmembers and conducting at level. Research efforts object level are very scarce, even though object-based image (OBIA) techniques have well developed. In this study, we examined applicability of an urban environment using a Landsat Thematic Mapper image. Informative accurate fraction maps (vegetation,...

Recent advancements in remote sensing technology have provided a plethora of very high spatial resolution images. From pixel-based processing designed for low data, image has shifted towards object-based analysis order to adapt the hyperspatial nature currently available data. However, standard classifiers work with only object-level summary statistics reflectance values and do not sufficiently exploit within-object pattern. In this research, novel approach utilizing distribution is...

We modeled population growth from 1990 to 2000 in the north Dallas-Fort Worth Metroplex using two different methods: a conventional model based on remote sensing land-use change detection, and newly devised approach GIS-derived road development measurements. These methods were applied at both city census-tract levels evaluated against actual growth. It was found that accurate estimates are achieved by methods. At level, our models yielded comparable result with obtained more complex...

Areal interpolation has been an active research area, but its wide adoption in the general GIS community is limited due to lack of implementation tools commercial software. To bridge this gap, areal extension developed ArcGIS with 4 popular algorithms, 10 raster and vector implementations, commonly used error measures. A comparative case study utilizing shows that it provides users a user-friendly interface for performing without dealing complexities underlying algorithms their details.

Abstract This paper describes the characteristics of a neural network image interpretation system that is designed to extract both rural land cover and urban use from high spatial resolution imagery (e.g., digitized aerial photography, IKONOS imagery) and/or relatively coarse spectral remote sensor data Landsat Thematic Mapper). The consists modules a) classify sensing into different use/land types, b) segment information homogeneous polygons in standard GIS format, c) digitize interpret...

A neuro-fuzzy system, namely Gaussian Fuzzy Learning Vector Quantization (GFLVQ), was developed based on the synergy of a neural network and fuzzy system. GFLVQ is both system with supervised learning unsupervised self-organizing capabilities. In this paper, further improved to efficiently effectively process hyperspectral data through training informed initialization simplified algorithm. geovisualization tool facilitate knowledge discovery understanding image. case study conducted using...