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Remote Sensing
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Using Remote Sensing for Ecosystem Service Assessments in Tropical Landscapes
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Using Remote Sensing for Ecosystem Service Assessments in Tropical Landscapes

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 6670

Special Issue Editors

Dr. Melvin Lippe

E-Mail Website
Guest Editor
Thünen Institute of Forestry, Leuschnerstr. 91, 21031 Hamburg, Germany
Interests: mapping of land-use land-cover change and forestry (LULUCF) processes incl. agroforestry and mixed cropping systems; land use modelling and impact assessment (carbon, habitat fragmentation, soil erosion); participatory procedures; stakeholder elucidations; ecosystem services; MRV
Dr. Sven Günter

E-Mail Website
Guest Editor
1. Ecosystem Dynamics and Forest Management in Mountain Landscapes, Technical University of Munich, D-85354 Freising, Germany
2. Thünen Institute of Forestry, Leuschnerstr. 91, 21031 Hamburg, Germany
Interests: tropical forestry; forest policy and economics; sustainable land use; sustainable forest management; silviculture

Special Issue Information

Dear Colleagues,

Tropical landscapes and their various land system components, such as primary or secondary forests and agroforestry systems, play an important role in biodiversity conservation, terrestrial carbon cycles, and hydrological regimes, among others. Attempts to preserve the role of such tropical landscapes in providing ecosystem services requires information on spatial and temporal distribution at various scales (i.e., patch, landscape, watershed, administrative) to support environmental management and policy processes. This is important as, for example, the share of forests designated primarily for soil and water protection is increasing, while at the same time, forest biodiversity and carbon stocks are lost due to deforestation and forest degradation and the accompanying increase in fragmented habitats. Ecosystem service assessments are often limited by spatial and spatiotemporal data, a challenge that may be overcome by the use of Earth observation systems (EOS), given their many beneficial features. Despite their widespread recognition, only a few ecosystem service studies use EOS in practice. This Special Issue invites studies that highlight the link between EOS (i.e., satellite, aircraft, drone; optical, SAR, hyperspectral) and ecosystem service assessments (i.e., field inventories, stakeholder elucidations, continuous monitoring) with a particular focus on tropical landscapes with a forest or agroforestry component. Studies should illuminate new ways in which EOS can be used to assess, monitor, or model ecosystem services at patch, landscape, or larger spatial scales. Possible further topics include mapping of ecosystem processes and services under landscape change dynamics, effects of scale on monitoring ecosystem services in conjunction with EOS, and inter- or multidisciplinary approaches that combine survey or qualitative information with EOS-derived data.

Dr. Melvin Lippe
Dr. Sven Günter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tropical forests
  • ecosystem functions
  • ecosystem services
  • earth observation system (EOS)

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Published Papers (3 papers)

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Research

16 pages, 6435 KiB  
Article
Tree Species Classification by Multi-Season Collected UAV Imagery in a Mixed Cool-Temperate Mountain Forest
by Ram Avtar, Xinyu Chen, Jinjin Fu, Saleh Alsulamy, Hitesh Supe, Yunus Ali Pulpadan, Albertus Stephanus Louw and Nakaji Tatsuro
Remote Sens. 2024, 16(21), 4060; https://doi.org/10.3390/rs16214060 - 31 Oct 2024
Viewed by 531
Abstract
Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of [...] Read more.
Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species. Full article
(This article belongs to the Special Issue Using Remote Sensing for Ecosystem Service Assessments in Tropical Landscapes)
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18 pages, 2667 KiB  
Article
Application of Free Satellite Imagery to Map Ecosystem Services in Ungwana Bay, Kenya
by Daina Mathai, Sónia Cristina and Margaret Awuor Owuor
Remote Sens. 2023, 15(7), 1802; https://doi.org/10.3390/rs15071802 - 28 Mar 2023
Cited by 1 | Viewed by 2507
Abstract
A major obstacle to mapping Ecosystem Services (ES) and the application of the ES concept has been the inadequacy of data at the landscape level necessary for their quantification. This study takes advantage of free satellite imagery to map and provide relevant information [...] Read more.
A major obstacle to mapping Ecosystem Services (ES) and the application of the ES concept has been the inadequacy of data at the landscape level necessary for their quantification. This study takes advantage of free satellite imagery to map and provide relevant information regarding ES and contribute to the sustainable management of natural resources in developing countries. The aim is to assess the flow of ES in mangrove ecosystem of Ungwana Bay, located on the northern coast of Kenya, by adopting the Land Use Land Cover (LULC) matrix approach. This study characterized LULC classes present in the study area, identified the most important ES, and collected data on expert opinions via a survey on ES flow supplied by the mangrove ecosystem. A qualitative and quantitative analysis of the expert scoring produced a LULC matrix which, when integrated with the LULC maps, showed the spatial distribution of ES flow. The assessment indicates very high flow (5.0) for the regulating and supporting services, high flow (4.0) for the cultural services, and medium flow (3.0) for the provisioning services as supplied by mangroves. In addition, the analysis indicates there are sixteen major ES supplied by the mangrove ecosystem of Ungwana bay as of the year 2021. This study highlights the importance of mangroves as a coastal ecosystem and how the visualization of the spatial distribution of ES flow using maps can be useful in informing natural resource management. In addition, the study shows the possibilities of using freely accessible satellite imagery and software to bolster the ES assessment studies lacking in developing countries. Full article
(This article belongs to the Special Issue Using Remote Sensing for Ecosystem Service Assessments in Tropical Landscapes)
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20 pages, 2867 KiB  
Article
Transformation of Agricultural Landscapes and Its Consequences for Natural Forests in Southern Myanmar within the Last 40 Years
by Phyu Thaw Tun, Thanh Thi Nguyen and Andreas Buerkert
Remote Sens. 2023, 15(6), 1537; https://doi.org/10.3390/rs15061537 - 11 Mar 2023
Cited by 2 | Viewed by 2344
Abstract
Kyunsu township comprises coastal regions and a multitude of small islands covered by vast tropical evergreen and mangrove forests, and a large water body in the Adman Sea of Myanmar. Due to population growth, residents have increasingly expanded their agricultural land areas into [...] Read more.
Kyunsu township comprises coastal regions and a multitude of small islands covered by vast tropical evergreen and mangrove forests, and a large water body in the Adman Sea of Myanmar. Due to population growth, residents have increasingly expanded their agricultural land areas into natural tropical evergreen and mangrove forests, leading to deforestation. Understanding the processes and consequences of landscape transformation for surrounding ecosystems is crucial for local policy making and for fostering sustainable crop production in this area. Landsat datasets from 1978, 1989, 2000, 2011, and 2020 were used in a time-series post-classification approach to investigate land use land cover (LULC) changes in the Kyunsu township of Southern Myanmar across the last 40 years. Our study also attempted to assess the effects of the transformation of LULC on carbon stocks. Between 1978 and 2020, major LULC changes occurred with the expansion of Paddy Fields (+90%), Plantations (+11%), Open Forests (+81%), Settlement Areas (+115%), Aquaculture Areas (+1594%), and Others (+188%) while the area covered with Closed Forests shrunk by 44% and with Mangrove Forests by 9%. Water Bodies expanded by 0.13%. Our analyses show that between 1978 and 2020 2453 ha of Paddy Fields expanded into Plantations, 1857 ha to Open Forests, and 1146 ha to Mangrove Forests. Additionally, 12,135 ha of Open Forests, 8474 ha of Closed Forests, and 2317 ha of Mangrove Forests became Plantations. Across the 40 year study period, a total of 40,523 ha of Closed Forests were transformed to Open Forests. Our findings show that transformation of agricultural landscapes in the study area significantly affected deforestation and forest degradation of tropical evergreen rain forests and mangrove forests which are vital sources of ecosystem services. These transformations led to estimated losses of carbon stocks between 1978 and 2020 ranged from 89,260–5,106,820 Mg (average of 1,723,250 Mg) in our study area. Our findings call for sustainable resource intensification to increase production efficiency in existing cultivated areas rather than crop land expansion into natural forests. In addition, our data highlight the need for rigorous policies to conserve and protect tropical natural evergreen and mangrove forest, as key local resources providing multiple ecosystem services. Full article
(This article belongs to the Special Issue Using Remote Sensing for Ecosystem Service Assessments in Tropical Landscapes)
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