#tropical — Public Fediverse posts

[#TRADESHOW] #IFEX #Kunming #International #Flowers & #Plants #Expo 2026 will be held from September 18 to 20, 2026, at Kunming Dianchi International #Convention & #Exhibition #Center. As a major flowers and plants #trade #show in #China, the #event is rooted in #Yunnan, the country’s #leading #hub for fresh-cut #flower production and #distribution, and serves as a #B2B #business #platform for #floriculture, #horticulture, #tropical plants, small #berries, and #coffee. https://cnbusinessforum.com/event/ifex-kunming-international-flowers-plants-expo-2026/

[#TRADESHOW] #IFEX #Kunming #International #Flowers & #Plants #Expo 2026 will be held from September 18 to 20, 2026, at Kunming Dianchi International #Convention & #Exhibition #Center. As a major flowers and plants #trade #show in #China, the #event is rooted in #Yunnan, the country’s #leading #hub for fresh-cut #flower production and #distribution, and serves as a #B2B #business #platform for #floriculture, #horticulture, #tropical plants, small #berries, and #coffee. https://cnbusinessforum.com/event/ifex-kunming-international-flowers-plants-expo-2026/

[#TRADESHOW] #IFEX #Kunming #International #Flowers & #Plants #Expo 2026 will be held from September 18 to 20, 2026, at Kunming Dianchi International #Convention & #Exhibition #Center. As a major flowers and plants #trade #show in #China, the #event is rooted in #Yunnan, the country’s #leading #hub for fresh-cut #flower production and #distribution, and serves as a #B2B #business #platform for #floriculture, #horticulture, #tropical plants, small #berries, and #coffee. https://cnbusinessforum.com/event/ifex-kunming-international-flowers-plants-expo-2026/

💁🏻‍♀️ ICYMI: 🦥🌿 #Carnivore ecologist Dr. Rae Wynn-Grant shares a behind-the-scenes look at filming Mutual of Omaha's Wild Kingdom in #CostaRica. The crew tracks wild #tapirs in #TapirValley, takes X-rays of a #sloth, and learns how #horses help make #snake anti-venom.

👉 Learn more: https://seethis.tv/post/tapir-valley-costa-rica-dr-rae-wynn-grant-video

#adventure #animals #wildlife #biology #birds #blackandstem #centralamerica #conservation #ecology #endangeredspecies #frogs #hiking #hummingbirds #jobs #jungle #medicine #night #rainforest #research #science #travel #tropical #venom #veterinarian #volcanoes #womeninstem #work #xray #filmmaking #conservation #nature #zoology #tksst #video

💁🏻‍♀️ ICYMI: 🦥🌿 #Carnivore ecologist Dr. Rae Wynn-Grant shares a behind-the-scenes look at filming Mutual of Omaha's Wild Kingdom in #CostaRica. The crew tracks wild #tapirs in #TapirValley, takes X-rays of a #sloth, and learns how #horses help make #snake anti-venom.

👉 Learn more: https://seethis.tv/post/tapir-valley-costa-rica-dr-rae-wynn-grant-video

#adventure #animals #wildlife #biology #birds #blackandstem #centralamerica #conservation #ecology #endangeredspecies #frogs #hiking #hummingbirds #jobs #jungle #medicine #night #rainforest #research #science #travel #tropical #venom #veterinarian #volcanoes #womeninstem #work #xray #filmmaking #conservation #nature #zoology #tksst #video

💁🏻‍♀️ ICYMI: 🦥🌿 #Carnivore ecologist Dr. Rae Wynn-Grant shares a behind-the-scenes look at filming Mutual of Omaha's Wild Kingdom in #CostaRica. The crew tracks wild #tapirs in #TapirValley, takes X-rays of a #sloth, and learns how #horses help make #snake anti-venom.

👉 Learn more: https://seethis.tv/post/tapir-valley-costa-rica-dr-rae-wynn-grant-video

#adventure #animals #wildlife #biology #birds #blackandstem #centralamerica #conservation #ecology #endangeredspecies #frogs #hiking #hummingbirds #jobs #jungle #medicine #night #rainforest #research #science #travel #tropical #venom #veterinarian #volcanoes #womeninstem #work #xray #filmmaking #conservation #nature #zoology #tksst #video

💁🏻‍♀️ ICYMI: 🦥🌿 #Carnivore ecologist Dr. Rae Wynn-Grant shares a behind-the-scenes look at filming Mutual of Omaha's Wild Kingdom in #CostaRica. The crew tracks wild #tapirs in #TapirValley, takes X-rays of a #sloth, and learns how #horses help make #snake anti-venom.

👉 Learn more: https://seethis.tv/post/tapir-valley-costa-rica-dr-rae-wynn-grant-video

#adventure #animals #wildlife #biology #birds #blackandstem #centralamerica #conservation #ecology #endangeredspecies #frogs #hiking #hummingbirds #jobs #jungle #medicine #night #rainforest #research #science #travel #tropical #venom #veterinarian #volcanoes #womeninstem #work #xray #filmmaking #conservation #nature #zoology #tksst #video

💁🏻‍♀️ ICYMI: 🦥🌿 #Carnivore ecologist Dr. Rae Wynn-Grant shares a behind-the-scenes look at filming Mutual of Omaha's Wild Kingdom in #CostaRica. The crew tracks wild #tapirs in #TapirValley, takes X-rays of a #sloth, and learns how #horses help make #snake anti-venom.

👉 Learn more: https://seethis.tv/post/tapir-valley-costa-rica-dr-rae-wynn-grant-video

#adventure #animals #wildlife #biology #birds #blackandstem #centralamerica #conservation #ecology #endangeredspecies #frogs #hiking #hummingbirds #jobs #jungle #medicine #night #rainforest #research #science #travel #tropical #venom #veterinarian #volcanoes #womeninstem #work #xray #filmmaking #conservation #nature #zoology #tksst #video

H.M Stanley (1873) by Frederick Watty, from Cartoon Portraits and Biographical Sketches of Men of the Day.

Source: University of Toronto Libraries / Internet Archive

https://pdimagearchive.org/images/b3f11ed5-14dc-4ca5-acc8-3676216c8898

#caricatures #tropical #cartoons #colonial #drawing #celebrities #bookillustration #art #publicdomain

H.M Stanley (1873) by Frederick Watty, from Cartoon Portraits and Biographical Sketches of Men of the Day.

Source: University of Toronto Libraries / Internet Archive

https://pdimagearchive.org/images/b3f11ed5-14dc-4ca5-acc8-3676216c8898

#caricatures #tropical #cartoons #colonial #drawing #celebrities #bookillustration #art #publicdomain

H.M Stanley (1873) by Frederick Watty, from Cartoon Portraits and Biographical Sketches of Men of the Day.

Source: University of Toronto Libraries / Internet Archive

https://pdimagearchive.org/images/b3f11ed5-14dc-4ca5-acc8-3676216c8898

#caricatures #tropical #cartoons #colonial #drawing #celebrities #bookillustration #art #publicdomain

H.M Stanley (1873) by Frederick Watty, from Cartoon Portraits and Biographical Sketches of Men of the Day.

Source: University of Toronto Libraries / Internet Archive

https://pdimagearchive.org/images/b3f11ed5-14dc-4ca5-acc8-3676216c8898

#caricatures #tropical #cartoons #colonial #drawing #celebrities #bookillustration #art #publicdomain

H.M Stanley (1873) by Frederick Watty, from Cartoon Portraits and Biographical Sketches of Men of the Day.

Source: University of Toronto Libraries / Internet Archive

https://pdimagearchive.org/images/b3f11ed5-14dc-4ca5-acc8-3676216c8898

#caricatures #tropical #cartoons #colonial #drawing #celebrities #bookillustration #art #publicdomain

[#TRADESHOW] #IFEX #Kunming #International #Flowers & #Plants #Expo 2026 will be held from September 18 to 20, 2026, at Kunming Dianchi International #Convention & #Exhibition #Center. As a major flowers and plants #trade #show in #China, the #event is rooted in #Yunnan, the country’s #leading #hub for fresh-cut #flower production and #distribution, and serves as a #B2B #business #platform for #floriculture, #horticulture, #tropical plants, small #berries, and #coffee. https://cnbusinessforum.com/event/ifex-kunming-international-flowers-plants-expo-2026/

[#TRADESHOW] #IFEX #Kunming #International #Flowers & #Plants #Expo 2026 will be held from September 18 to 20, 2026, at Kunming Dianchi International #Convention & #Exhibition #Center. As a major flowers and plants #trade #show in #China, the #event is rooted in #Yunnan, the country’s #leading #hub for fresh-cut #flower production and #distribution, and serves as a #B2B #business #platform for #floriculture, #horticulture, #tropical plants, small #berries, and #coffee. https://cnbusinessforum.com/event/ifex-kunming-international-flowers-plants-expo-2026/

[#TRADESHOW] #IFEX #Kunming #International #Flowers & #Plants #Expo 2026 will be held from September 18 to 20, 2026, at Kunming Dianchi International #Convention & #Exhibition #Center. As a major flowers and plants #trade #show in #China, the #event is rooted in #Yunnan, the country’s #leading #hub for fresh-cut #flower production and #distribution, and serves as a #B2B #business #platform for #floriculture, #horticulture, #tropical plants, small #berries, and #coffee. https://cnbusinessforum.com/event/ifex-kunming-international-flowers-plants-expo-2026/

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
--
https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
--
H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
--
“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
--
https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
--
H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
--
“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
--
https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
--
H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
--
“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
--
https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
--
H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
--
“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
--
https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
--
H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
--
“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
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https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
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"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Voila une nouvelle fiche sur une espèce de #poisson #tropical dont le #papa fait un gros boulot pour l'arrivée des #bébés : l'#apogon irisé.

BOURJON Philippe, SITTLER Alain-Pierre in : #DORIS, 09/02/2026 :
Pristiapogon kallopterus (Bleeker, 1856), https://doris.ffessm.fr/ref/specie/4634

#biodiversite #merRouge #IndoPacifique #biodiversity #Apogonidae #fishes #teleostei