Zhen Zhen

• PhD
• Professor (Associate) at Northeast Forestry University

Natural secondary forests play a crucial role in global ecological security, climate change mitigation, and biodiversity conservation. However, accurately delineating individual tree crowns and identifying tree species in dense natural secondary forests remains a challenge. This study combines deep learning with traditional image segmentation metho...

North China is one of China’s most severely warming and drying regions, and understanding the dynamics of forest carbon sinks in North China is critical for forest management in response to climate change. This study examined the spatial and temporal dynamics of forest carbon sinks in North China from 2000 to 2020 using carbon cycle process and soi...

The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this stud...

The quantification of carbon storage (CS) within urban areas has become increasingly crucial for achieving global carbon neutrality. This study proposed a new approach to estimating CS in urban human settlements, building on an existing forestry biomass expansion factor (BEF)-based CS estimation approach, and tested it over Suwon, a city in South K...

Accurate tree species classification is essential for forest resource management and biodiversity assessment. However, classifying tree species becomes challenging in natural secondary forests due to the difficulties in outlining the tree crown boundary. In this study, an object-based framework for tree species classification in the Experimental Fo...

Large-scale forest composition mapping and change monitoring are essential for regional and national forest resource management, monitoring, and carbon stock assessment. However, the existing large-scale mapping methods are not effective enough in terms of efficiency and accuracy. To address this limitation, this study proposes a lightweight one-di...

Identifying important factors (e.g., features and prediction models) for forest aboveground biomass (AGB) estimation can provide a vital reference for accurate AGB estimation. This study proposed a novel feature of the canopy height distribution (CHD), a function of canopy height, that is useful for describing canopy structure for AGB estimation of...

Fractional vegetation cover (FVC) is a quantitative indicator for vegetation growth conditions and ecosystem change. Clarifying the spatial and temporal trends and driving factors of FVC is an important research content of global and regional ecological environment. Based on Google Earth Engine (GEE) cloud computing platform, we estimated FVC in He...

Accurate quantification of individual tree parameters is vital for precise forest inventory and sustainable forest management. However, in dense forests, terrestrial laser scanning (TLS), which can provide accurate and detailed forest structural measurements, is limited to capturing the complete tree structure due to the lack of upper canopy views,...

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014-2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geograph...

Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree AGB estimation of Changbai larch (Larix olgensis...

Currently, aboveground biomass (AGB) estimation primarily relies on an area-based approach (ABA), particularly at a large scale. With the advancement of individual tree detection techniques and the availability of multi-platform remotely sensed data, the individual tree-based approach (ITA) provides the potential of accurate and nondestructive fine...

Individual-tree aboveground biomass (AGB) estimation can highlight the spatial distribution of AGB and is vital for precision forestry. Accurately estimating individual tree AGB is a requisite for accurate forest carbon stock assessment of natural secondary forests (NSFs). In this study, we investigated the performance of three machine learning and...

Although the combination of Airborne Laser Scanning (ALS) data and optical imagery and machine learning algorithms were proved to improve the estimation of aboveground biomass (AGB), the synergistic approaches of different data and ensemble learning algorithms have not been fully investigated, especially for natural secondary forests (NSFs) with co...

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale...

To explore the influence of meteorological variables on the growth of Korean pine (Pinus koraiensis Sieb. et Zucc.) plantations and provide a scientific reference for the production and management of Korean pine, three approaches to interpolate meteorological variables during the growing season (i.e., May–September) were compared in Heilongjiang Pr...

Objective: This study investigated the relationships between PM2.5 and 5 criteria air pollutants (SO2, NO2, PM10, CO, and O3) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regres...

Airborne LiDAR-derived canopy height models (CHMs) have been widely applied in forestry inventory applications and have shown great advantages in obtaining forest-related parameters at different scales. Usually, first echoes are regarded as a representation of canopy surfaces during CHM generation, which may cause data pits and a lack of detail, th...

Objective: The purpose of this study was to explore the full distribution of children’s lead poisoning and identify “high risk” locations or areas in the neighborhood of the inner city of Syracuse (NY, USA), using quantile regression models. Methods: Global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied...

Geographically weighted regression (GWR) has become popular in recent years to deal with spatial autocorrelation and heterogeneity in forestry and ecological data. However, researchers have realized that GWR has some limitations, such as correlated model coefficients across study areas, strong influence of outliers, weak data problem, etc. In this...

Objective The purpose of this study is to identify the high-risk areas of children’s lead poisoning in Syracuse, NY, USA, using spatial modeling techniques. The relationships between the number of children’s lead poisoning cases and three socio-economic and environmental factors (i.e., building year and town taxable value of houses, and soil lead c...

In recent years, airborne Light Detection and Ranging (LiDAR) that provided three-dimensional forest information has been widely applied in forest inventory and has shown great potential in automatic individual tree crown delineation (ITCD). Usually, ITCD algorithms include treetop detection and crown boundary delineation procedures. In this study,...

Children's lead poisoning continues to compromise children's health and development, particularly in the inner cities of the United States. We applied a global Poisson model, a Poisson with random effects model, and a geographically weighted Poisson regression (GWPR) model to deal with the spatial dependence and heterogeneity of the number of child...

Children’s blood lead concentrations have been closely monitored over the last two decades in the United States. The bio-monitoring surveillance data collected in local agencies reflected the local temporal trends of children’s blood lead levels (BLLs). However, the analysis and modeling of the long-term time series of BLLs have rarely been reporte...

The data used for fitting the time series models in the study. (XLSX)

Based on 1390 fixed plots data collected in 2005 and 2010 in Heihe Region of Heilongjiang Province, biomass allometric models of tree species in northeastern China were applied to calculate aboveground biomass (AGB) and net primary productivity (NPP). Using ETM+ imageries in 2005 and 2010, Kriging and co-Kriging methods in geostatistics were applie...

Automated individual tree crown detection and delineation (ITCD) using remotely sensed data plays an increasingly significant role in efficiently, accurately, and completely monitoring forests. This paper reviews trends in ITCD research from 1990-2015 from several perspectives-data/forest type, method applied, accuracy assessment and research objec...

Individual tree crown delineation (ITCD) is a fundamental and vital component of individual tree-based forest inventory. Region-growing algorithms have been widely developed and applied in ITCD studies. Although individual treetops are typically designated as initial seeds, most region-growing algorithms do not consider tree competition when tree c...

Based on the data from Chinese National Forest Inventory (CNFI) and Key Ecological Benefit Forest Monitoring plots (5075 in total) in Heilongjiang Province in 2010 and concurrent meteorological data coming from 59 meteorological stations located in Heilongjiang, Jilin and Inner Mongolia, this paper established a spatial error model (SEM) by GeoDA u...

Taking 4163 permanent sample plots from Chinese National Forest Inventory (CNFI) and key ecological benefit forest monitoring plots in Heilongjiang Province as basic data, and by using local Moran I and local statistics (local mean and local standard deviation), the spatial pattern, spatial variation and spatial autocorrelation of forest carbon sto...

Region growing is frequently applied in automated individual tree crown delineation (ITCD) studies. Researchers have developed various rules for initial seed selection and stop criteria when applying the algorithm. However, research has rarely focused on the impact of tree-oriented growth order. This study implemented a marker-controlled region gro...

International Journal of Remote Sensing Publication details, including instructions for authors and subscription information: (2013) Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification, makes every effort to ensure the accuracy of all the...

The Liangshui National Nature Reserve, located in Northeast China, was heavily damaged by severe windstorms in 2008 and 2009, which caused abundant windthrows, especially large trees, and significantly altered the size and structure of the natural forest. A forest survey was conducted to collect data on living trees, downwood on the forest floor, a...

The data of average temperature, average relative humidity, precipitation and average wind speed were collected from 674 meteorological stations in China. A specific procedure that processes original data into a new data format needed in forest fire danger rating forecast system of China was introduced systematically, and the feasibility of this me...