Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted the past three decades. This paper provides a survey current using remote sensing data discusses four critical issues – collection field-based reference data, extraction selection suitable variables from identification proper algorithms to develop models, uncertainty analysis refine procedure. Additionally, we discuss impacts...

Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, their combinations) artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), linear (LR)) for...

Research on change detection techniques has long been an active topic and many have developed. In reality, is a comprehensive procedure that requires careful consideration of factors such as the nature problems, image preprocessing, selection suitable variables algorithms. This paper briefly overviews major steps involved in detection, summarises methods, discusses impacts scales complexity study areas remote-sensing data algorithms finally needs developing new methods. As high spatial...

The global availability of high spatial resolution images makes mapping tree species distribution possible for better management forest resources. Previous research mainly focused on single species, but information about the all kinds trees, especially plantations, is often required. This aims to identify suitable variables and algorithms classifying land cover, forest, species. Bi-temporal ZiYuan-3 multispectral stereo were used. Spectral responses textures from imagery, canopy height...

Impervious surface area (ISA) is an important parameter for many environmental or socioeconomic relevant studies. The unique characteristics of remote sensing data made it the primary source ISA mapping at various scales. This paper summarizes general procedure and major techniques discusses impacts scale issues on selection corresponding algorithms. Previous studies have indicated that remains a challenge, especially in urban–rural frontiers covering large area. Effectively employing rich...

The sustainable intensification of African agriculture is gaining momentum with the compelling need to increase food and agricultural production. In Southern Africa, smallholder farming systems are predominately maize-based subject erratic climatic conditions. Farmer crop soil management decisions influenced by a plethora complex factors such as market access resource availability, social relations, environment, various messages on practices. Such pose barriers increasing in Africa. This...

Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes forested lands, limited research has addressed challenges extraction plain sparse fragmented forest distributions. In this study, we proposed innovative processing approaches to extract ICESat-2 photons GEDI footprints Anhui Province, China. Specifically, a sectional...

Residential population estimation was explored based on impervious surface coverage in Marion County, Indiana, USA. The developed by spectral unmixing of a Landsat Enhanced Thematic Mapper (ETM+) multispectral image. residential then derived geographic information system (GIS) overlay land class and surface. Regression analysis conducted to develop density models. We found that the surface‐based approach provided best result, with mean median relative errors 38% 23%, respectively. An overall...

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to area. Landsat images acquired during years 1990/1991, 1999/2000, 2008/2010 were used examine LULC change trajectories post-classification comparison approach. A classification system composed six classes – forest, savanna, other vegetation (secondary...

Abstract Many data fusion methods are available, but it is poorly understood which method suitable for integrating Landsat Thematic Mapper (TM) and radar land cover classification. This research explores the integration of TM images (i.e., ALOS PALSAR L-band RADARSAT-2 C-band) classification in a moist tropical region Brazilian Amazon. Different methods—principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), normalized...

Abstract Population growth, climate sensitivity, and edaphic properties are important factors that influence decision‐making risk mitigation for agricultural production. Within the sector in Malawi, continuous cropping without use of long‐term sustainable strategies frequent cultivation on marginal lands have resulted continually declining soil fertility. Improving quality using innovative technologies is imperative increasing productivity improving food security. Here, we propose an...

Remote sensing techniques have been previously used in urban analysis, settlement detection, and population estimation. This research explores the potentials of integra- tion Landsat ETMdata with census data for estimation density City Indianapolis, Indiana. Spectral signatures, principal components, vegetation indices, fraction images, textures, temperature were as predictive indicators. Correlation analysis was to explore relationships between remote variables population, stepwise...

This research aims to improve land-cover classification accuracy in a moist tropical region Brazil by examining the use of different remote-sensing-derived variables and algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data derived vegetation indices textural images algorithms, maximum likelihood (MLC), artificial neural network (ANN), tree analysis (CTA) object-based (OBC), were explored. The results indicate that combination as extra bands into TM...

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional global regulation climate changes. A timely investigation of the spatial distribution characteristics forest aboveground biomass (AGB) is essential to assess stocks. Lidar (light detection ranging) regarded as most reliable data source for accurate estimation AGB. However, previous studies that used lidar often beenbased on a single model developed from relationships between...

Soil and water erosion has long been regarded as a serious environmental problem in the world. Thus, research on reducing soil received continuous attention. Different conservation measures such restoring low-function forests, closing hillsides for afforestation, planting trees grass, constructing terraces slope land have implemented controlling problems promoting vegetation cover change. One important task is to understand effects of different problems. However, directly conducting...

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there lack of accurate FCH distribution in large areas. To address this issue, study selected Wuyishan National Park China as case to explore the calibration method mapping complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance causal...

Trees are indispensable to ecosystems, yet mortality rates have been increasing due the abnormal changes in forest growth environments caused by frequent extreme weather events associated with global climate warming. Consequently, need monitor, assess, and predict tree has become increasingly urgent better address change protect ecosystems. Over past few decades, remote sensing widely applied vegetation observation its significant advantages. Here, we reviewed analyzed major research...

The accurate estimation of forest carbon stocks with remote sensing technologies helps reveal the spatial patterns within national parks, but limited number sample plots in one site often results difficulty developing robust models. This study employed a Bayesian hierarchical model to estimate stock based on data from 193 collected across 37 UAV (unmanned aerial vehicle) Lidar sites. developed was predict distribution 17 sites Wuyishan National Park (WNP). Then, characteristics along...

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land spatial distribution a challenge, particularly moist tropical regions, due to complex biophysical environment and limitations sensing data per se. This paper reviews experiments related Brazilian Amazon for decade. Through comprehensive analysis results, it concluded that information inherent plays an essential role improving classification. Incorporation suitable...

Research on separation of successional stages has been an active topic for the past two decades because vegetation plays important role in carbon budget and restoration soil fertility Brazilian Amazon. This article examines classification by conducting a comparative analysis algorithms (maximum likelihood classifier – MLC, artificial neural network ANN, K-nearest neighbour KNN, support vector machine SVM, tree CTA, object-based OBC) varying remote-sensing data-sets (Landsat ALOS PALSAR)....