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Land
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Spatial Big Data for Rangeland Ecology and Management
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Spatial Big Data for Rangeland Ecology and Management

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1700

Special Issue Editors

Dr. Matt Reeves

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Guest Editor
US Forest Service, Rocky Mountain Research Station, Human Dimensions Program, Fort Collins, CO, USA
Interests: rangelands; modelling; climate change; remote sensing
Dr. Humberto L. Perotto-Baldivieso

E-Mail Website
Guest Editor
Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, 305 Horticulture/Forest Science Building (HFSB), College Station, TX 77843-2138, USA
Interests: landscape ecology; remote sensing; spatial ecology; drones
Special Issues, Collections and Topics in MDPI journals
Dr. Edward C. Rhodes

E-Mail Website
Guest Editor
Texas Water Resources Institute, Texas A&M AgriLife Research, Texas A&M University, 570 John Kimbrough Blvd, Suite 150, 2260 TAMU, College Station, TX 77843, USA
Interests: remote sensing of rangeland and surface water; landscape ecology; land-use/land-cover; rangeland production modeling; watershed assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rangelands cover about 50% of the world's landmass and provide critical ecosystem services in the form of food, fiber, water security, carbon sequestration, wildlife habitats, and aesthetics, to name a few. Rangeland management is an applied science that studies the use of rangelands to produce, manage, and conserve natural resources.

Improper management of rangeland resources results in decreased long-term rangeland sustainability. To realize a sound scientific approach to rangeland management, managers should take into consideration all aspects of ecological and economic resources, as well as social wellbeing to maintain a balance between healthy rangelands and societal needs. However, to support an effective and sustainable system, decision-making processes must be based on sound knowledge derived from reliable data.

Spatial big data can be obtained from many different sources, such as satellites, drones, UAV-mounted sensors, GPS, and wearable devices. Big data, especially in spatially explicit formats, are becoming increasingly prevalent in providing managers with a comprehensive view of time and space variations. Through monitoring and modelling vegetation performance corresponding to land-use and land-cover changes, geospatial big data could assist in optimizing rangeland management towards sustainable use.

This Special Issue focuses on research and development regarding multi-source geospatial big data for rangeland monitoring, and modelling to support sustainable rangeland management.

Topics include, but are not limited to, the following:

  • Land-use change modelling and land-cover mapping with geospatial big data;
  • Landscape level questions for rangelands;
  • Implementation of big data analysis to understand spatial and temporal variations;
  • Land management, including the conservation of ecosystem services;
  • Multi-source remotely sensed data and analysis;
  • Decision support systems for sustainable rangeland management.

We look forward to receiving your original research articles and reviews.

Dr. Matt Reeves
Dr. Humberto L. Perotto-Baldivieso
Dr. Edward C. Rhodes
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. Land is an international peer-reviewed open access monthly 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 2600 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

  • ranchland management
  • land cover mapping
  • spatial big data
  • productivity
  • sustainable land use
  • decision support system

Published Papers (2 papers)

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Research

18 pages, 5994 KiB  
Article
Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (Dzud) in Mongolian Nomadic Pasturelands
by Sinkyu Kang, Nanghyun Cho, Amartuvshin Narantsetseg, Bolor-Erdene Lkhamsuren, Otgon Khongorzul, Tumendemberel Tegshdelger, Bumsuk Seo and Keunchang Jang
Land 2024, 13(3), 391; https://doi.org/10.3390/land13030391 - 19 Mar 2024
Viewed by 672
Abstract
In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level soum districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers [...] Read more.
In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level soum districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers retrievable from multi-sensor satellite data. Three alternative methods (principal component analysis, PCA; stepwise multiple regression, SMR; and random forest machine learning model, RF) were used to determine the key drivers for livestock changes and Dzud outbreaks. The countrywide Dzud in 2010 was well-characterized by the PCA as cold with a snowy winter and low summer foraging biomass. The RF estimated the annual livestock change with high accuracy (R2 > 0.9 in most soums). The SMR was less accurate but provided better intuitive insights on the regionality of the key factors and its relationships with local climate and Dzud characteristics. Summer and winter variables appeared to be almost equally important in both models. The primary factors of livestock change and Dzud showed regional patterns: dryness in the south, temperature in the north, and foraging resource in the central and western regions. This study demonstrates a synergistic potential of models and satellite data to understand climate–vegetation–livestock interactions in Mongolian nomadic pastures. Full article
(This article belongs to the Special Issue Spatial Big Data for Rangeland Ecology and Management)
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13 pages, 2346 KiB  
Article
Assessing Trends in Tree Cover, Wildfire and Population Growth in Zimbabwe since 2000
by Emma C. Underwood, Allan D. Hollander and Beth A. Hahn
Land 2024, 13(2), 160; https://doi.org/10.3390/land13020160 - 30 Jan 2024
Viewed by 679
Abstract
Zimbabwe’s woodland and forests have experienced substantial change over the last two decades. In this study, our objective was to assess national-scale spatio-temporal changes in tree loss, wildfire, and population growth since 2000 using global data. Our results showed rates of tree loss [...] Read more.
Zimbabwe’s woodland and forests have experienced substantial change over the last two decades. In this study, our objective was to assess national-scale spatio-temporal changes in tree loss, wildfire, and population growth since 2000 using global data. Our results showed rates of tree loss were highest in the urbanized Harare and Bulawayo provinces between 2000–2004, followed by Masvingo and Manicaland provinces. We found agricultural versus non-agricultural land type classes had higher tree loss, with the highest rates in small resettlement farms (‘A1’ farms, averaging 5 ha in size) between 2000–2008. The findings from our analysis of wildfire showed burning peaked in 2010, impacting 12% of the country. In the peak fire years of 2008–2012, 30% of A2 self-contained resettlement farms (‘A2’ farms, averaging 318 ha in size) burned, along with 19% of A1 resettlement farms. Analysis of global population data showed increases across all provinces, particularly in large-scale commercial farming areas, with gradual increases seen in A1 and A2 farms. Understanding the trends over two decades and the patterns in three key pressures—tree loss, population change, and fire—provides an important contribution to help guide regional assistance efforts in Zimbabwe. Full article
(This article belongs to the Special Issue Spatial Big Data for Rangeland Ecology and Management)
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