Competition, precipitation and temperature shape deviations from scaling laws in the crown allometries of miombo woodlands DOI Creative Commons
Arthur M. Yambayamba, Fabian Jörg Fischer, Tommaso Jucker

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 2, 2024

Abstract Scaling relationships between different axes of tree size, such as height, crown radius, depth and stem diameter, play a direct role in shaping forest structure function. Theoretical models metabolic scaling theory postulate that they are optimized for biomechanical stability hydraulic sap distribution. However, empirical data often show only good enough first order approximations because do not account differences species traits environmental conditions where trees grow. Nevertheless, the vast majority research has focused on temperate systems or tropical rainforests, so we continue to lack full understanding what factors shape allometries dry forests. Here, compile radius from miombo woodlands across Zambia use Bayesian hierarchical modelling framework explore how allometric shaped by climate competition. Similar previous studies, our results revealed deviate substantially theoretical expectations. We found competition, precipitation temperature all affect relationships, with becoming more slender neighbourhood competition was greater, while crowns were wider deeper warmer wetter climates. Our study highlights function is than just water availability. Moreover, developing improved woodlands, provide new tools aid estimation aboveground biomass calibration remote sensing products these critically important ecosystems.

Language: Английский

Historical and Projected Forest Cover Changes in the Mount Kenya Ecosystem: Implications for Sustainable Forest Management DOI Creative Commons
Brian Rotich, Abdalrahman Ahmed,

Benjamin Mutuku Kinyili

et al.

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: 26, P. 100628 - 100628

Published: Feb. 7, 2025

Language: Английский

Citations

1
Forecasting Urban Sprawl Dynamics in Islamabad: A Neural Network Approach DOI Creative Commons

Saddam Sarwar,

Hafiz Usman Ahmed Khan, Falin Wu

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 492 - 492

Published: Jan. 31, 2025

In the past two decades, Islamabad has experienced significant urbanization. As a result of inadequate urban planning and spatial distribution, it significantly influenced land use–land cover (LULC) changes green areas. To assess these changes, there is an increasing need for reliable appropriate information about Landsat imagery categorized into four thematic classes using supervised classification method called support vector machine (SVM): built-up, bareland, vegetation, water. The results change detection post-classification show that city region increased from 6.37% (58.09 km2) in 2000 to 28.18% (256.49 2020, while vegetation decreased 46.97% (428.28 34.77% (316.53 bareland 45.45% (414.37 35.87% (326.49 km2). Utilizing modeler (LCM), forecasts future conditions 2025, 2030, 2035 are predicted. artificial neural network (ANN) model embedded IDRISI software 18.0v based on well-defined backpropagation (BP) algorithm was used simulate sprawl considering historical pattern 2015–2020. Selected landscape morphological measures were quantify analyze structure patterns. According data, area grew at pace 4.84% between 2015 2020 will grow rate 1.47% 2035. This growth metropolitan encroach further bareland. If existing patterns persist over next ten years, drop mean Euclidian Nearest Neighbor Distance (ENN) patches anticipated (from 104.57 m 101.46 2020–2035), indicating accelerated transformation landscape. Future prediction modeling revealed would be huge increase 49% areas until year compared 2000. rapidly urbanizing areas, urgent enhance use laws policies ensure sustainability ecosystem, development, preservation natural resources.

Language: Английский

Citations

0
SPI-based drought characteristics using CHIRPS over Zambia: 1981–2024 DOI Creative Commons
Charles Bwalya Chisanga,

Edson Nkonde,

Kabwe Harnadih Mubanga

et al.

All Earth, Journal Year: 2025, Volume and Issue: 37(1), P. 1 - 19

Published: March 7, 2025

Language: Английский

Citations

0
Modeling land use and land cover dynamics of Bale Mountains National Park using Google Earth Engine and cellular automata–artificial neural network (CA-ANN) model DOI Creative Commons
Firdissa Sadeta Tiye,

Diriba Korecha,

Tariku Mekonnen Gutema

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320428 - e0320428

Published: April 30, 2025

This research aimed to assess the observed land use and cover (LULC) changes of Bale Mountains National Park (BMNP) from 1993 2023 its future projections for years (2033 2053). The study utilized multi-date Landsat imagery 1993, 2003, 2013, 2023, leveraging 5 TM, 7 ETM+, 8 OLI-TIRS sensors LULC classification. Standard image pre-processing techniques were applied, composite images created using yearly median values in Google Earth Engine (GEE). In addition satellite data, both physical socioeconomic variables used as input modeling. Random Forest (RF) classification algorithm was classification, while Cellular Automata Artificial Neural Networks (CA-ANN) model within Modules Land Use Change Simulations (MOLUSCE) plugin QGIS employed projection. analysis revealed significant BMNP, primarily due anthropogenic activities, with further anticipated between 2053.The results showed a notable increase woodland shrubs at expense grassland Erica forest. While increased by 87.18% 36.7%, areas forest lost about 25% 22% their area, respectively, during this period. also indicated that covered are expected 15.97% 15.57%, 2053. Conversely, occupied cultivated land, forest, grassland, herbaceous plants projected decrease 28.52%, 3.28%, 19.03%, 6.55%, respectively. Proximity roads urban combined rising temperatures altered precipitation patterns emerged critical factors influencing conversion BMNP. These findings underscore complex interplay environmental human activities shaping dynamics. Hence, promoting sustainable management practices among park administration local community well enhancing habitat protection efforts recommended. Additionally, integrating advanced remote sensing technologies ground truthing will be essential accurate assessments dynamics area biodiversity.

Language: Английский

Citations

0
Comparing the process of converting land use purposes between socio-economic regions in Vietnam from 2007 to 2020 DOI Creative Commons
Nguyen Tran Tuan

Environmental & Socio-economic Studies, Journal Year: 2024, Volume and Issue: 12(3), P. 51 - 62

Published: Sept. 1, 2024

Abstract Reporting land use changes over time is important for evaluating resource management. This study applied GIS technology to determine fluctuations the entire mainland territory in Vietnam. In particular, research focused on two main issues: (1) spatial of some groups Vietnam, and(2) rate change socio-economic regions periods 2007–2016 and 2016–2020. Research results showed that Forests group a growth 14% took place all regions, except with little this group: Red River Delta (RRD) Mekong (MRD). Meanwhile, crops decreased by 16% from 2007–2020 appeared heavily Northern Midlands Mountains (NMR), North Central Coast (NCR), Highlands region (CHR). Urban increased 3% during 2007–2020. The speed conversion also different between economic inthe periods. recent period witnessed higher compared 2007–2016. NMR was largest both stages.

Language: Английский

Citations

2
Modeling Spatiotemporal Land Use/Land Cover Dynamics by Coupling Multilayer Perceptron Neural Network and Cellular Automata Markov Chain Algorithms in the Wabe River Catchment, Omo Gibe River Basin, Ethiopia DOI Creative Commons
Yonas Mathewos,

Brook Abate,

Mulugeta Dadi

et al.

Environmental Research Communications, Journal Year: 2024, Volume and Issue: 6(10), P. 105011 - 105011

Published: Sept. 27, 2024

Abstract Land Use/Land Cover (LULC) change has been a substantial environmental concern, hindering sustainable development over the past few decades. To that end, comprehending and future patterns of LULC is vital for conserving sustainably managing land resources. This study aimed to analyze spatiotemporal landscape dynamics from 1986 2022 predict situations 2041 2058, considering business-as-usual (BAU) scenario in Wabe River Catchment. The historical use image classification employed supervised technique using maximum likelihood algorithms ERDAS Imagine, identified six major cover classes. For projections changes multilayer perceptron neural network cellular automata-Markov chain were utilized, incorporating various driving factors independent spatial datasets. findings revealed significant ongoing catchment, with persistent trends expected. Notably, woodland, built-up areas, agriculture experienced net increases by 0.24%, 1.96%, 17.22% respectively, while grassland, forest, agroforestry faced notable decreases 4.65%, 3.58%, 11.20% respectively 2022. If current rate continues, agricultural lands will expand 1.28% 5.07%, forest decline 2.69% 3.63% 2058. However, woodland grassland exhibit divergent patterns, projected decrease 0.57% an anticipated increase 0.54% cover. Overall, observed indicated shift towards intensive agriculture, area expansion, potentially adverse consequences such as soil degradation, biodiversity loss, ecosystem decline. mitigate these promote development, immediate action necessary, including environmentally friendly conservation approaches, management practices, habitat protection, reforestation efforts, ensuring long-term resilience viability catchment’s ecosystems.

Language: Английский

Citations

1
Competition, precipitation and temperature shape deviations from scaling laws in the crown allometries of miombo woodlands DOI Creative Commons
Arthur M. Yambayamba, Fabian Jörg Fischer, Tommaso Jucker

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 2, 2024

Abstract Scaling relationships between different axes of tree size, such as height, crown radius, depth and stem diameter, play a direct role in shaping forest structure function. Theoretical models metabolic scaling theory postulate that they are optimized for biomechanical stability hydraulic sap distribution. However, empirical data often show only good enough first order approximations because do not account differences species traits environmental conditions where trees grow. Nevertheless, the vast majority research has focused on temperate systems or tropical rainforests, so we continue to lack full understanding what factors shape allometries dry forests. Here, compile radius from miombo woodlands across Zambia use Bayesian hierarchical modelling framework explore how allometric shaped by climate competition. Similar previous studies, our results revealed deviate substantially theoretical expectations. We found competition, precipitation temperature all affect relationships, with becoming more slender neighbourhood competition was greater, while crowns were wider deeper warmer wetter climates. Our study highlights function is than just water availability. Moreover, developing improved woodlands, provide new tools aid estimation aboveground biomass calibration remote sensing products these critically important ecosystems.

Language: Английский

Citations

0