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Special Issue "Remote Sensing of Land Degradation and Drivers of Change"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2016)

Special Issue Editors

Guest Editor
Dr. Rasmus Fensholt

Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark
Website | E-Mail
Phone: +45 35322526
Interests: land degradation; bio-geophysical vegetation metrics; climate change; net primary productivity; carbon stocks; forest degradation; plant water stress; early warning systems
Guest Editor
Dr. Stephanie Horion

Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark
Website | E-Mail
Phone: +45 35322479
Interests: global scale; EO time series analysis; vegetation productivity; climate change; drought; land degradation; natural vs. human drivers
Guest Editor
Dr. Torbern Tagesson

Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark
Website | E-Mail
Phone: +46-704-993936
Interests: carbon dynamics, land atmosphere interactions; light use efficiency; evapotranspiration; hyperspectral remote sensing, productivity modeling; micrometeorology; climate change; dryland degradation
Guest Editor
Dr. Martin Brandt

Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark
Website | E-Mail
Interests: Sahel; remote sensing applications; vegetation monitoring; land degradation; environmental change; biomass

Special Issue Information

Dear Colleagues,

Human and climate induced degradation of arable lands has been of major concern for livelihoods and food security particularly in drylands during recent decades, supporting and affecting the wellbeing of more than one-third of the global population. Monitoring vegetation productivity is of great importance because crop and livestock production is the most essential economic activity, especially in arid and semi-arid regions of the world.

The United Nations Convention to Combat Desertification’s (UNCCD) definition of desertification, or dryland degradation states that: “Land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities” followed by “land degradation means reduction or loss, in arid, semi-arid and dry sub-humid areas, of the biological or economic productivity and complexity of rainfed cropland, irrigated cropland, or range, pasture, forest and woodlands”…  (UNCCD homepage, www.unccd.int).

This definition implies that change in vegetation productivity is a key indicator (but not the only one) of land degradation. Along with land mismanagement often caused by human pressure, climatic variability is a major determinant of land degradation. Given the harsh nature of the climate in drylands, it is of great policy relevance to understand potential damaging interactions between land degradation and climate change. Indeed, climate-induced changes in air temperature and soil moisture might inflict soil erosion, salinization, crusting, and loss of soil fertility or depletion of seed banks in dryland ecosystems.

Continuous long-term Earth Observation (EO) satellite data provides the only suitable means of temporally and spatially consistent data analysis across multiple scales, and EO based metrics of vegetation productivity and land degradation are of great interest for the assessment and monitoring of environmental changes in dryland regions.

This forthcoming special issue welcomes research papers focusing on: (i) monitoring ecosystem productivity (both vegetation and economic productivity) and ecosystem complexity (i.e., biodiversity); (ii) studying the impact of climate change and human pressure on land degradation processes; (iii) uncovering the driving mechanisms of observed changes in vegetation productivity. The primary region of interest will be drylands, but studies covering other parts of the globe are also welcomed.

Dr. Rasmus Fensholt
Dr. Stephanie Horion
Dr. Torbern Tagesson
Dr. Martin Brandt
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 papers will be 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 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 1600 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.

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Keywords

  • EO-based methods for monitoring land degradation;
  • Vegetation/climate/anthropogenic productivity indicators;
  • Human versus climate-induced land degradation;
  • Land-use land-cover change in monitoring land degradation;
  • Multi-temporal/time-series analysis/multiple datasets;
  • Local to global scales;
  • Drivers attribution;
  • Case studies on land degradation and climate change;
  • Field evidence of degradation linked with EO data.

Published Papers (12 papers)

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Research

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Open AccessArticle MODIS Time Series to Detect Anthropogenic Interventions and Degradation Processes in Tropical Pasture
Remote Sens. 2017, 9(1), 73; doi:10.3390/rs9010073
Received: 1 September 2016 / Revised: 28 December 2016 / Accepted: 1 January 2017 / Published: 14 January 2017
PDF Full-text (3618 KB) | HTML Full-text | XML Full-text
Abstract
The unavoidable diet change in emerging countries, projected for the coming years, will significantly increase the global consumption of animal protein. It is expected that Brazilian livestock production, responsible for close to 15% of global production, be prepared to answer to the increasing
[...] Read more.
The unavoidable diet change in emerging countries, projected for the coming years, will significantly increase the global consumption of animal protein. It is expected that Brazilian livestock production, responsible for close to 15% of global production, be prepared to answer to the increasing demand of beef. Consequently, the evaluation of pasture quality at regional scale is important to inform public policies towards a rational land use strategy directed to improve livestock productivity in the country. Our hypothesis is that MODIS images can be used to evaluate the processes of degradation, restoration and renovation of tropical pastures. To test this hypothesis, two field campaigns were performed covering a route of approximately 40,000 km through nine Brazilian states. To characterize the sampled pastures, biophysical parameters were measured and observations about the pastures, the adopted management and the landscape were collected. Each sampled pasture was evaluated using a time series of MODIS EVI2 images from 2000–2012, according to a new protocol based on seven phenological metrics, 14 Boolean criteria and two numerical criteria. The theoretical basis of this protocol was derived from interviews with producers and livestock experts during a third field campaign. The analysis of the MODIS EVI2 time series provided valuable historical information on the type of intervention and on the biological degradation process of the sampled pastures. Of the 782 pastures sampled, 26.6% experienced some type of intervention, 19.1% were under biological degradation, and 54.3% presented neither intervention nor trend of biomass decrease during the period analyzed. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Woody Vegetation Die off and Regeneration in Response to Rainfall Variability in the West African Sahel
Remote Sens. 2017, 9(1), 39; doi:10.3390/rs9010039
Received: 9 November 2016 / Revised: 26 December 2016 / Accepted: 1 January 2017 / Published: 5 January 2017
Cited by 3 | PDF Full-text (17575 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The greening in the Senegalese Sahel has been linked to an increase in net primary productivity, with significant long-term trends being closely related to the woody strata. This study investigates woody plant growth and mortality within greening areas in the pastoral areas of
[...] Read more.
The greening in the Senegalese Sahel has been linked to an increase in net primary productivity, with significant long-term trends being closely related to the woody strata. This study investigates woody plant growth and mortality within greening areas in the pastoral areas of Senegal, and how these dynamics are linked to species diversity, climate, soil and human management. We analyse woody cover dynamics by means of multi-temporal and multi-scale Earth Observation, satellite based rainfall and in situ data sets covering the period 1994 to 2015. We find that favourable conditions (forest reserves, low human population density, sufficient rainfall) led to a rapid growth of Combretaceae and Balanites aegyptiaca between 2000 and 2013 with an average increase of 4% woody cover. However, the increasing dominance and low drought resistance of drought prone species bears the risk of substantial woody cover losses following drought years. This was observed in 2014–2015, with a die off of Guiera senegalensis in most places of the study area. We show that woody cover and woody cover trends are closely related to mean annual rainfall, but no clear relationship with rainfall trends was found over the entire study period. The observed spatial and temporal variation contrasts with the simplified labels of “greening” or “degradation”. While in principal a low woody plant diversity negatively impacts regional resilience, the Sahelian system is showing signs of resilience at decadal time scales through widespread increases in woody cover and high regeneration rates after periodic droughts. We have reaffirmed that the woody cover in Sahel responds to its inherent climatic variability and does not follow a linear trend. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Detection of the Coupling between Vegetation Leaf Area and Climate in a Multifunctional Watershed, Northwestern China
Remote Sens. 2016, 8(12), 1032; doi:10.3390/rs8121032
Received: 18 September 2016 / Revised: 1 December 2016 / Accepted: 14 December 2016 / Published: 18 December 2016
Cited by 2 | PDF Full-text (4135 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere–biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation
[...] Read more.
Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere–biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation dynamics and climatic variabilities during the past three decades in the Upper Heihe River Basin (UHRB), a complex multiple use watershed in arid northwestern China. We apply empirical orthogonal function (EOF) and singular value decomposition (SVD) analysis to isolate and identify the spatial patterns of satellite-derived leaf area index (LAI) and their close relationship with the variability of an aridity index (AI = Precipitation/Potential Evapotranspiration). Results show that UHRB has become increasingly warm and wet during the past three decades. In general, the rise of air temperature and precipitation had a positive impact on mean LAI at the annual scale. At the monthly scale, LAI variations had a lagged response to climate. Two major coupled spatial change patterns explained 29% and 41% of the LAI dynamics during 1983–2000 and 2001–2010, respectively. The strongest connections between climate and LAI were found in the southwest part of the basin prior to 2000, but they shifted towards the north central area afterwards, suggesting that the sensitivity of LAI to climate varied over time, and that human disturbances might play an important role in altering LAI patterns. At the basin level, the positive effects of regional climate warming and precipitation increase as well as local ecological restoration efforts overwhelmed the negative effects of overgrazing. The study results offer insights about the coupled effects of climatic variability and grazing on ecosystem structure and functions at a watershed scale. Findings from this study are useful for land managers and policy makers to make better decisions in response to climate change in the study region. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data
Remote Sens. 2016, 8(12), 1026; doi:10.3390/rs8121026
Received: 3 November 2016 / Revised: 28 November 2016 / Accepted: 8 December 2016 / Published: 16 December 2016
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Abstract
Vegetation productivity is an essential variable in ecosystem functioning. Vegetation dynamics of dryland ecosystems are most strongly determined by water availability and consequently by rainfall and there is a need to better understand how water limited ecosystems respond to altered rainfall amounts and
[...] Read more.
Vegetation productivity is an essential variable in ecosystem functioning. Vegetation dynamics of dryland ecosystems are most strongly determined by water availability and consequently by rainfall and there is a need to better understand how water limited ecosystems respond to altered rainfall amounts and variability. This response is partly determined by the vegetation functional response to rainfall (β) approximated by the unit change in annual vegetation productivity per unit change in annual rainfall. Here, we show how this functional response from 1983 to 2011 is affected by below and above average rainfall in two arid to semi-arid subtropical regions in West Africa (WA) and South West Africa (SWA) differing in interannual variability of annual rainfall (higher in SWA, lower in WA). We used a novel approach, shifting linear regression models (SLRs), to estimate gridded time series of β. The SLRs ingest annual satellite based rainfall as the explanatory variable and annual satellite-derived vegetation productivity proxies (NDVI) as the response variable. Gridded β values form unimodal curves along gradients of mean annual precipitation in both regions. β is higher in SWA during periods of below average rainfall (compared to above average) for mean annual precipitation <600 mm. In WA, β is hardly affected by above or below average rainfall conditions. Results suggest that this higher β variability in SWA is related to the higher rainfall variability in this region. Vegetation type-specific β follows observed responses for each region along rainfall gradients leading to region-specific responses for each vegetation type. We conclude that higher interannual rainfall variability might favour a more dynamic vegetation response to rainfall. This in turn may enhance the capability of vegetation productivity of arid and semi-arid regions to better cope with periods of below average rainfall conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Climate-Induced Extreme Hydrologic Events in the Arctic
Remote Sens. 2016, 8(11), 971; doi:10.3390/rs8110971
Received: 30 August 2016 / Revised: 17 November 2016 / Accepted: 21 November 2016 / Published: 23 November 2016
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Abstract
The objectives were (i) to evaluate the relationship between recent climate change and extreme hydrological events and (ii) to characterize the behavior of hydrological events along the Alazeya River. The warming rate of air temperature observed at the meteorological station in Chersky was
[...] Read more.
The objectives were (i) to evaluate the relationship between recent climate change and extreme hydrological events and (ii) to characterize the behavior of hydrological events along the Alazeya River. The warming rate of air temperature observed at the meteorological station in Chersky was 0.0472 °C·year−1, and an extraordinary increase in air temperatures was observed in 2007. However, data from meteorological stations are somewhat limited in sparsely populated regions. Therefore, this study employed historical remote sensing data for supplementary information. The time-series analysis of the area-averaged Global Precipitation Climatology Project (GPCP) precipitation showed a positive trend because warming leads to an increase in the water vapor content in the atmosphere. In particular, heavy precipitation of 459 ± 113 mm was observed in 2006. On the other hand, the second-highest summer National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution radiometer (AVHRR) brightness temperature (BT) was observed in 2007 when the highest air temperature was observed in Chersky, and the anomaly from normal revealed that the summer AVHRR BTs showed mostly positive values. Conversely, riverbank, lakeshore and seashore areas were much cooler due to the formation, expansion and drainage of lakes and/or the increase in water level by heavy precipitation and melting of frozen ground. The large lake drainage resulted in a flood. Although the flooding was triggered by the thermal erosion along the riverbanks and lakeshores—itself induced by the heat wave in 2007—the increase in soil water content due to the heavy precipitation in 2006 appeared to contribute the magnitude of flood. The flood was characterized by the low streamflow velocity because the Kolyma Lowlands had a very gentle gradient. Therefore, the flood continued for a long time over large areas. Information based on remote sensing data gave basic insights for understanding the mechanism and behavior of climate-induced extreme hydrologic events. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle The Effects of Spatiotemporal Changes in Land Degradation on Ecosystem Services Values in Sanjiang Plain, China
Remote Sens. 2016, 8(11), 917; doi:10.3390/rs8110917
Received: 31 August 2016 / Revised: 7 October 2016 / Accepted: 24 October 2016 / Published: 4 November 2016
Cited by 2 | PDF Full-text (5228 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Sanjiang Plain has undergone dramatic land degradation since the 1950s, which has caused negative effects on ecosystems services and sustainability. In this study, we used trajectory analysis as well as the Lorenz curve, Gini coefficient and relative land use suitability index (R) to
[...] Read more.
Sanjiang Plain has undergone dramatic land degradation since the 1950s, which has caused negative effects on ecosystems services and sustainability. In this study, we used trajectory analysis as well as the Lorenz curve, Gini coefficient and relative land use suitability index (R) to analyze spatiotemporal changes of land degradation from 1954 to 2013 and to make a preliminary estimation of the role of human activities in observed environmental changes using a five-stage LULC data. This study also explored the effect of land degradation on the values and structure of ecosystem services. Our results indicated that more than 70% of marsh area originally present in the study area has been lost, whereas less than 30% was preserved. Dry farmland and paddy increased rapidly at the expense of marsh, forest and grassland. Land use structure became more unsuitable during the past 60 years. Compared with natural factors, human activities played a dominant role (89.67%) in these changes. This dramatic land degradation caused the significant loss of ecosystem services values and the changes in the structure of ecosystem services. These results confirmed the effectiveness of combining temporal trajectory analysis, the Lorenz curve/Gini coefficient and the R index in analyzing spatiotemporal changes in progressive land degradation. Also, these findings highlight the necessity of separating dry farmland from paddy when studying land degradation changes and the effects on ecosystem services in regions where dry farmland has often been converted to paddy. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Vegetation Responses to Climate Variability in the Northern Arid to Sub-Humid Zones of Sub-Saharan Africa
Remote Sens. 2016, 8(11), 910; doi:10.3390/rs8110910
Received: 3 June 2016 / Revised: 12 October 2016 / Accepted: 18 October 2016 / Published: 2 November 2016
Cited by 2 | PDF Full-text (14903 KB) | HTML Full-text | XML Full-text
Abstract
In water limited environments precipitation is often considered the key factor influencing vegetation growth and rates of development. However; other climate variables including temperature; humidity; the frequency and intensity of precipitation events are also known to affect productivity; either directly by changing photosynthesis
[...] Read more.
In water limited environments precipitation is often considered the key factor influencing vegetation growth and rates of development. However; other climate variables including temperature; humidity; the frequency and intensity of precipitation events are also known to affect productivity; either directly by changing photosynthesis and transpiration rates or indirectly by influencing water availability and plant physiology. The aim here is to quantify the spatiotemporal patterns of vegetation responses to precipitation and to additional; relevant; meteorological variables. First; an empirical; statistical analysis of the relationship between precipitation and the additional meteorological variables and a proxy of vegetation productivity (the Normalized Difference Vegetation Index; NDVI) is reported and; second; a process-oriented modeling approach to explore the hydrologic and biophysical mechanisms to which the significant empirical relationships might be attributed. The analysis was conducted in Sub-Saharan Africa; between 5 and 18°N; for a 25-year period 1982–2006; and used a new quasi-daily Advanced Very High Resolution Radiometer (AVHRR) dataset. The results suggest that vegetation; particularly in the wetter areas; does not always respond directly and proportionately to precipitation variation; either because of the non-linearity of soil moisture recharge in response to increases in precipitation; or because variations in temperature and humidity attenuate the vegetation responses to changes in water availability. We also find that productivity; independent of changes in total precipitation; is responsive to intra-annual precipitation variation. A significant consequence is that the degree of correlation of all the meteorological variables with productivity varies geographically; so no one formulation is adequate for the entire region. Put together; these results demonstrate that vegetation responses to meteorological variation are more complex than an equilibrium relationship between precipitation and productivity. In addition to their intrinsic interest; the findings have important implications for detection of anthropogenic dryland degradation (desertification); for which the effects of natural fluctuations in meteorological variables must be controlled in order to reveal non-meteorological; including anthropogenic; degradation. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessFeature PaperArticle The Influences of Climate Change and Human Activities on Vegetation Dynamics in the Qinghai-Tibet Plateau
Remote Sens. 2016, 8(10), 876; doi:10.3390/rs8100876
Received: 30 June 2016 / Revised: 28 September 2016 / Accepted: 18 October 2016 / Published: 23 October 2016
Cited by 4 | PDF Full-text (10856 KB) | HTML Full-text | XML Full-text
Abstract
Grasslands occupy nearly three quarters of the land surface of the Qinghai-Tibet plateau (QTP) and play a critical role in regulating the ecological functions of the QTP. Ongoing climate change and human interference have greatly affected grasslands on the QTP. Differentiating human-induced and
[...] Read more.
Grasslands occupy nearly three quarters of the land surface of the Qinghai-Tibet plateau (QTP) and play a critical role in regulating the ecological functions of the QTP. Ongoing climate change and human interference have greatly affected grasslands on the QTP. Differentiating human-induced and climate-driven vegetation changes is vital for both ecological understanding and the management of husbandry. In this study, we employed statistical analysis of annual records, various sources of remote sensing data, and an ecosystem process model to calculate the relative contribution of climate and human activities to vegetation vigor on the QTP. The temperature, precipitation and the intensity and spatial pattern of livestock grazing differed between the periods prior to and after the year 2000, which led to different vegetation dynamics. Overall, increased temperature and enhanced precipitation favored vegetation growth. However, their combined effects exhibited strong spatial heterogeneity. Specifically, increased temperature restrained vegetation growth in dry steppe regions during a period of slightly increasing precipitation from 1986 to 2000 and in meadow regions during a period of precipitation decline during 2000–2011, thereby making precipitation a dominant factor. An increase in precipitation tended to enhance vegetation growth in wet meadow regions during warm periods, and temperature was the limiting factor in Tibet during dry periods. The dominant role played by climate and human activities differed with location and targeted time period. Areas dominated by human activities are much smaller than those dominated by climate. The effects of grazing on grassland pasture were more obvious under unfavorable climate conditions than under suitable ones. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Degradation of Non-Photosynthetic Vegetation in a Semi-Arid Rangeland
Remote Sens. 2016, 8(8), 692; doi:10.3390/rs8080692
Received: 1 June 2016 / Revised: 28 July 2016 / Accepted: 10 August 2016 / Published: 24 August 2016
Cited by 2 | PDF Full-text (4177 KB) | HTML Full-text | XML Full-text
Abstract
Land degradation in drylands is the process in which undesirable conditions emerge due to human and natural causes. Despite the particularly deleterious effects of degradation, and it’s potentially irreversible nature, regional assessments have provided conflicting extents, rates, and severities of degradation, both globally
[...] Read more.
Land degradation in drylands is the process in which undesirable conditions emerge due to human and natural causes. Despite the particularly deleterious effects of degradation, and it’s potentially irreversible nature, regional assessments have provided conflicting extents, rates, and severities of degradation, both globally and regionally. Current monitoring of degradation relies upon the detection of green, photosynthetically active parts of vegetation (e.g., leaves). Less is known, however, about the effect of degradation on the non-photosynthetic components of vegetation (e.g., wood, stems, leaf litter) and the relationship between photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil under degraded conditions (BS). The major objective of the study was to evaluate regional patterns of fractional cover (i.e., PV, NPV, BS) under degraded and non-degraded NPP conditions in a managed rangeland in north Queensland, Australia. Homogenous environmental conditions were identified and each of NPP, PV, NPV, and BS were scaled according to their potential, reference values. We found a strong spatial and temporal correlation between scaled NPP with both scaled PV and scaled BS. Drastic differences were also found for PV and BS between degraded and non-degraded conditions. NPV displayed similarity to both PV and BS, however no clear relationship was found for NPV in all areas, irrespective of degradation conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Utilizing Multiple Lines of Evidence to Determine Landscape Degradation within Protected Area Landscapes: A Case Study of Chobe National Park, Botswana from 1982 to 2011
Remote Sens. 2016, 8(8), 623; doi:10.3390/rs8080623
Received: 28 May 2016 / Revised: 21 July 2016 / Accepted: 23 July 2016 / Published: 28 July 2016
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Abstract
The savannas of Southern Africa are an important dryland ecosystem as they cover up to 54% of the landscape and support a rich variety of biodiversity. This paper evaluates landscape change in savanna vegetation along Chobe Riverfront within Chobe National Park Botswana, from
[...] Read more.
The savannas of Southern Africa are an important dryland ecosystem as they cover up to 54% of the landscape and support a rich variety of biodiversity. This paper evaluates landscape change in savanna vegetation along Chobe Riverfront within Chobe National Park Botswana, from 1982 to 2011 to understand what change may be occurring in land cover. Classifying land cover in savanna environments is challenging because the vegetation spectral signatures are similar across distinct vegetation covers. With vegetation species and even structural groups having similar signatures in multispectral imagery difficulties exist in making discrete classifications in such landscapes. To address this issue, a Random Forest classification algorithm was applied to predict land-cover classes. Additionally, time series vegetation indices were used to support the findings of the discrete land cover classification. Results indicate that a landscape level vegetation shift has occurred across the Chobe Riverfront, with results highlighting a shift in land cover towards more woody vegetation. This represents a degradation of vegetation cover within this savanna landscape environment, largely due to an increasing number of elephants and other herbivores utilizing the Riverfront. The forested area along roads at a further distance from the River has also had a loss of percent cover. The continuous analysis during 1982–2011, utilizing monthly AVHRR (Advanced Very High Resolution Radiometer) NDVI (Normalized Difference Vegetation Index) values, also verifies this change in amount of vegetation is a continuous and ongoing process in this region. This study provides land use planners and managers with a more reliable, efficient and relatively inexpensive tool for analyzing land-cover change across these highly sensitive regions, and highlights the usefulness of a Random Forest classification in conjunction with time series analysis for monitoring savanna landscapes. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China
Remote Sens. 2016, 8(5), 357; doi:10.3390/rs8050357
Received: 14 March 2016 / Revised: 14 April 2016 / Accepted: 17 April 2016 / Published: 27 April 2016
Cited by 4 | PDF Full-text (3690 KB) | HTML Full-text | XML Full-text
Abstract
To alleviate the severe rocky desertification and improve the ecological conditions in Southwest China, the national and local Chinese governments have implemented a series of Ecological Restoration Projects since the late 1990s. In this context, remote sensing can be a valuable tool for
[...] Read more.
To alleviate the severe rocky desertification and improve the ecological conditions in Southwest China, the national and local Chinese governments have implemented a series of Ecological Restoration Projects since the late 1990s. In this context, remote sensing can be a valuable tool for conservation management by monitoring vegetation dynamics, projecting the persistence of vegetation trends and identifying areas of interest for upcoming restoration measures. In this study, we use MODIS satellite time series (2001–2013) and the Hurst exponent to classify the study area (Guizhou and Guangxi Provinces) according to the persistence of future vegetation trends (positive, anti-persistent positive, negative, anti-persistent negative, stable or uncertain). The persistence of trends is interrelated with terrain conditions (elevation and slope angle) and results in an index providing information on the restoration prospects and associated uncertainty of different terrain classes found in the study area. The results show that 69% of the observed trends are persistent beyond 2013, with 57% being stable, 10% positive, 5% anti-persistent positive, 3% negative, 1% anti-persistent negative and 24% uncertain. Most negative development is found in areas of high anthropogenic influence (low elevation and slope), as compared to areas of rough terrain. We further show that the uncertainty increases with the elevation and slope angle, and areas characterized by both high elevation and slope angle need special attention to prevent degradation. Whereas areas with a low elevation and slope angle appear to be less susceptible and relevant for restoration efforts (also having a high uncertainty), we identify large areas of medium elevation and slope where positive future trends are likely to happen if adequate measures are utilized. The proposed framework of this analysis has been proven to work well for assessing restoration prospects in the study area, and due to the generic design, the method is expected to be applicable for other areas of complex landscapes in the world to explore future trends of vegetation. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Other

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Open AccessTechnical Note Mapping Deforestation and Forest Degradation Patterns in Western Himalaya, Pakistan
Remote Sens. 2016, 8(5), 385; doi:10.3390/rs8050385
Received: 31 December 2015 / Revised: 12 April 2016 / Accepted: 12 April 2016 / Published: 6 May 2016
Cited by 4 | PDF Full-text (5948 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Himalayan mountain forest ecosystem has been degrading since the British ruled the area in the 1850s. Local understanding of the patterns and processes of degradation is desperately required to devise management strategies to halt this degradation and provide long-term sustainability. This work
[...] Read more.
The Himalayan mountain forest ecosystem has been degrading since the British ruled the area in the 1850s. Local understanding of the patterns and processes of degradation is desperately required to devise management strategies to halt this degradation and provide long-term sustainability. This work comprises a satellite image based study in combination with national expert validation to generate sub-district level statistics for forest cover over the Western Himalaya, Pakistan, which accounts for approximately 67% of the total forest cover of the country. The time series of forest cover maps (1990, 2000, 2010) reveal extensive deforestation in the area. Indeed, approximately 170,684 ha of forest has been lost, which amounts to 0.38% per year clear cut or severely degraded during the last 20 years. A significant increase in the rate of deforestation is observed in the second half of the study period, where much of the loss occurs at the western borders along with Afghanistan. The current study is the first systematic and comprehensive effort to map changes to forest cover in Northern Pakistan. Deforestation hotspots identified at the sub-district level provide important insight into deforestation patterns, which may facilitate the development of appropriate forest conservation and management strategies in the country. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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