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Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 30079

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Prof. Keith T. Weber

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GIS Training and Research Center (GIS TREC)，Idaho State University, Pocatello, ID 83209, USA
Interests: spatial analysis; land cover change; wildfire, semiarid environments; multispectral satellite imagery; GIS
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

The landscape is continually changing across the globe. Some of these changes may be considered advantageous, while others are a detriment to ecosystem health, water and air quality, wildlife habitat, and the socioeconomic services provided by the affected landscape. There are numerous drivers of land cover change, including the proximate drivers of fire, invasive plants, and herbivory along with the overarching and ultimate driver; the influence of the anthropic forces of land management.

Savanna ecosystems occupy 20% of the world’s land surface and provide numerous important socioeconomic services to our communities. These ecosystems, like so many others across the globe, are a witness to tremendous land cover change, including those noted above, as well as a transition from natural to agrarian or urban systems.

I invite you to participate in this very Special Edition of Remote Sensing and share your land cover change research in savanna ecosystems.

Prof. Keith T. Weber
Guest Editor

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Keywords

Land cover change
Savanna
Savannah
Remote sensing
Fire
Invasive plants
Herbivory
Land management
Urbanization

Published Papers (9 papers)

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Research

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24 pages, 49407 KiB

Open AccessArticle

Land Use Hotspots of the Two Largest Landlocked Countries: Kazakhstan and Mongolia

by Jing Yuan, Jiquan Chen, Pietro Sciusco, Venkatesh Kolluru, Sakshi Saraf, Ranjeet John and Batkhishig Ochirbat

Remote Sens. 2022, 14(8), 1805; https://doi.org/10.3390/rs14081805 - 8 Apr 2022

Cited by 8 | Viewed by 3387

Abstract

As the two largest landlocked countries, Kazakhstan and Mongolia have similar biophysical conditions and socioeconomic roots in the former Soviet Union. Our objective is to investigate the direction, extent, and spatial variation of land cover change at three administrative levels over three decades (1990–2020). We selected three provinces from each country (Aktobe, Akmola, and Almaty province in Kazakhstan, and Arkhangai, Tov, and Dornod in Mongolia) to classify the land cover into forest, grassland, cropland, barren, and water. Altogether, 6964 Landsat images were used in pixel-based classification method with random forest model for image processing. Six thousand training data points (300 training points × 5 classes × 4 periods) for each province were collected for classification and change detection. Land cover changes at decadal and over the entire study period for five land cover classes were quantified at the country, provincial, and county level. High classification accuracy indicates localized land cover classification have an edge over the latest global land cover product and reveal fine differences in landscape composition. The vast steppe landscapes in these two countries are dominated by grasslands of 91.5% for Dornod in Mongolia and 74.7% for Aktobe in Kazakhstan during the 30-year study period. The most common land cover conversion was grassland to cropland. The cyclic land cover conversions between grassland and cropland reflect the impacts of the Soviet Union’s largest reclamation campaign of the 20th century in Kazakhstan and the Atar-3 agriculture re-development in Mongolia. Kazakhstan experienced a higher rate of land cover change over a larger extent of land area than Mongolia. The spatial distribution of land use intensity indicates that land use hotspots are largely influenced by policy and its shifts. Future research based on these large-scale land use and land cover changes should be focused the corresponding ecosystem and society functions. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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16 pages, 2069 KiB

Open AccessArticle

Monitoring Large-Scale Restoration Interventions from Land Preparation to Biomass Growth in the Sahel

by Moctar Sacande, Antonio Martucci and Andreas Vollrath

Remote Sens. 2021, 13(18), 3767; https://doi.org/10.3390/rs13183767 - 20 Sep 2021

Cited by 6 | Viewed by 2887

Abstract

In this work we demonstrate that restoration interventions in arid to semi-arid landscapes can be independently assessed by remote sensing methods throughout all phases. For early verification, we use Sentinel-1 radar imagery that is sensitive to changes in soil roughness and thus able to rapidly detect disturbances due to mechanised ploughing, including identification of the time of occurrence and the surface area prepared for planting. Subsequently, time series of the normalized difference vegetation index (NDVI) derived from high-resolution imagery enabled tracking and verifying of the increase in biomass and the long-term impact of restoration interventions. We assessed 111 plots within the Great Green Wall area in Burkina Faso, Niger, Nigeria and Senegal. For 58 plots, the interventions were successfully verified, corresponding to an area of more than 7000 ha of degraded land. Comparatively, these computerised data were matched with field data and high-resolution imagery, for which the NDVI was used as an indicator of subsequent biomass growth in the plots. The trends were polynomial and presented clear vegetation gains for the monthly aggregates over the last 2 years (2018–2020). The qualitative data on planted species also showed an increase in biodiversity as direct sown seeds of a minimum of 10 native Sahel species (six woody mixed with four fodder herbaceous species) were planted per hectare. This innovative and standardised monitoring method provides an objective and timely assessment of restoration interventions and will likely appeal more actors to confidently invest in restoration as a part of zero-net climate mitigation. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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19 pages, 135477 KiB

Open AccessArticle

Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms

by Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi and Jochem Verrelst

Remote Sens. 2021, 13(17), 3433; https://doi.org/10.3390/rs13173433 - 29 Aug 2021

Cited by 4 | Viewed by 1987

Abstract

Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). In the first stage, we applied the following classification algorithms to distinguish PEUs: Minimum Distance (MD), Maximum Likelihood Classification (MLC), Neural Network-Multi Layer Perceptron (NN-MLP) and Classification Tree Analysis (CTA) for pixel based method and object based method. Then, by using the most accurate classification approach, in the second stage auxiliary data (Principal Component Analysis (PCA)) was incorporated to improve the accuracy of the PEUs classification process. At the end, test data (60%) were used for accuracy assessment of the resulting maps. Object-based maps clearly outperformed pixel-based maps, especially with CTA, NN-MLP and MD algorithms with overall accuracies of 86%, 72% and 59%, respectively. The MLC algorithm did not reveal any significant difference between the object-based and pixel-based analyses. Finally, complementing PCA auxiliary bands to the CTA algorithms offered the most successful PEUs classification strategy, with the highest overall accuracy (89%). The results clearly underpin the importance of object-based classification with the CTA classifier together with PCA auxiliary data to optimize identification of PEU classes. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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16 pages, 3535 KiB

Open AccessArticle

Characterizing Small-Town Development Using Very High Resolution Imagery within Remote Rural Settings of Mozambique

by Dong Chen, Tatiana V. Loboda, Julie A. Silva and Maria R. Tonellato

Remote Sens. 2021, 13(17), 3385; https://doi.org/10.3390/rs13173385 - 26 Aug 2021

Cited by 2 | Viewed by 2303

Abstract

While remotely sensed images of various resolutions have been widely used in identifying changes in urban and peri-urban environments, only very high resolution (VHR) imagery is capable of providing the information needed for understanding the changes taking place in remote rural environments, due to the small footprints and low density of man-made structures in these settings. However, limited by data availability, mapping man-made structures and conducting subsequent change detections in remote areas are typically challenging and thus require a certain level of flexibility in algorithm design that takes into account the specific environmental and image conditions. In this study, we mapped all buildings and corrals for two remote villages in Mozambique based on two single-date VHR images that were taken in 2004 and 2012, respectively. Our algorithm takes advantage of the presence of shadows and, through a fusion of both spectra- and object-based analysis techniques, is able to differentiate buildings with metal and thatch roofs with high accuracy (overall accuracy of 86% and 94% for 2004 and 2012, respectively). The comparison of the mapping results between 2004 and 2012 reveals multiple lines of evidence suggesting that both villages, while differing in many aspects, have experienced substantial increases in the economic status. As a case study, our project demonstrates the capability of a coupling of VHR imagery with locally adjusted classification algorithms to infer the economic development of small, remote rural settlements. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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15 pages, 6510 KiB

Open AccessArticle

Land Use and Degradation in a Desert Margin: The Northern Negev

by Stephen Prince and Uriel Safriel

Remote Sens. 2021, 13(15), 2884; https://doi.org/10.3390/rs13152884 - 23 Jul 2021

Cited by 1 | Viewed by 1830

Abstract

Degradation in a range of land uses was examined across the transition from the arid to the semi-arid zone in the northern Negev desert, representative of developments in land use taking place throughout the West Asia and North Africa region. Primary production was used as an index of an important aspect of dryland degradation. It was derived from data provided by Landsat measurements at 0.1 ha resolution over a 2500 km² study region—the first assessment of the degradation of a large area of a desert margin at a resolution suitable for interpretation in terms of human activities. The Local NPP Scaling (LNS) method enabled comparisons between the observed NPP and the potential, nondegraded, reference NPP. The potential was calculated by normalizing the actual NPP to remove the effects of environmental conditions that are not related to anthropogenic degradation. Of the entire study area, about 50% was found to have a significantly lower production than its potential. The degree of degradation ranged from small in pasture, around informal settlements, minimally managed dryland cropping, and a pine plantation, to high in commercial cropping and extreme in low-density afforestation. This result was unexpected as degradation in drylands is often attributed to pastoralism, and afforestation is said to offer remediation and prevention of further damage. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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20 pages, 55338 KiB

Open AccessArticle

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

by Jiamin Liu, Bin Xiao, Yueshi Li, Xiaoyun Wang, Qiang Bie and Jizong Jiao

Remote Sens. 2021, 13(8), 1499; https://doi.org/10.3390/rs13081499 - 13 Apr 2021

Cited by 28 | Viewed by 2662

Abstract

Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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24 pages, 9075 KiB

Open AccessArticle

Mapping Land Use/Cover Dynamics of the Yellow River Basin from 1986 to 2018 Supported by Google Earth Engine

by Qiulei Ji, Wei Liang, Bojie Fu, Weibin Zhang, Jianwu Yan, Yihe Lü, Chao Yue, Zhao Jin, Zhiyang Lan, Siya Li and Pan Yang

Remote Sens. 2021, 13(7), 1299; https://doi.org/10.3390/rs13071299 - 29 Mar 2021

Cited by 33 | Viewed by 4439

Abstract

Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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21 pages, 34352 KiB

Open AccessArticle

Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

by Niels Souverijns, Marcel Buchhorn, Stéphanie Horion, Rasmus Fensholt, Hans Verbeeck, Jan Verbesselt, Martin Herold, Nandin-Erdene Tsendbazar, Paulo N. Bernardino, Ben Somers and Ruben Van De Kerchove

Remote Sens. 2020, 12(22), 3817; https://doi.org/10.3390/rs12223817 - 20 Nov 2020

Cited by 17 | Viewed by 5215

Abstract

Historical land cover maps are of high importance for scientists and policy makers studying the dynamic character of land cover change in the Sudano-Sahel, including anthropogenic and climatological drivers. Despite its relevance, an accurate high resolution record of historical land cover maps is currently lacking over the Sudano-Sahel. In this study, 30 m resolution historically consistent land cover and cover fraction maps are provided over the Sudano-Sahel for the period 1986–2015. These land cover/cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification/regression model, while historical consistency is achieved using the hidden Markov model. Using these historical maps, a multitude of variability in the dynamic Sudano-Sahel region over the past 30 years is revealed. On the one hand, Sahel-wide cropland expansion and the re-greening of the Sahel is observed in the discrete land cover classification. On the other hand, subtle changes such as forest degradation are detected based on the cover fraction maps. Additionally, exploiting the 30 m spatial resolution, fine-scale changes, such as smallholder or subsistence farming, can be detected. The historical land cover/cover fraction maps presented in this study are made available via an open-access platform. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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Review

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28 pages, 5069 KiB

Open AccessReview

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

by Hilma S. Nghiyalwa, Marcel Urban, Jussi Baade, Izak P. J. Smit, Abel Ramoelo, Buster Mogonong and Christiane Schmullius

Remote Sens. 2021, 13(19), 3870; https://doi.org/10.3390/rs13193870 - 27 Sep 2021

Cited by 6 | Viewed by 2959

Abstract

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing. Full article

(This article belongs to the Special Issue Analysis of Land Cover Change within Semiarid Environments Using Satellite Imagery and GIS)

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