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10121 KiB

Open AccessArticle

Early Identification of Land Degradation Hotspots in Complex Bio-Geographic Regions

by Maria Lanfredi, Rosa Coppola, Tiziana Simoniello, Rosa Coluzzi, Mariagrazia D'Emilio, Vito Imbrenda and Maria Macchiato

Remote Sens. 2015, 7(6), 8154-8179; https://doi.org/10.3390/rs70608154 - 19 Jun 2015

Cited by 54 | Viewed by 8809

Abstract

The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current [...] Read more.

The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current status of local vegetation and climate shows evident departures from the mean conditions of the investigated region. Our procedure was tested in Basilicata (Italy), which is a typical bio-geographic example of vulnerable Mediterranean landscape. We grouped Landsat TM/ETM+ NDVI and air temperature (T) data by vegetation cover type to estimate the statistical distributions of the departures of NDVI and T from the respective land cover class means. The pixels characterized by contextual left tail NDVI values and right tail T values that persisted in time (2002–2006) were classified as critical to land degradation. According to our results, most of the critical areas (88.6%) corresponded to forests affected by erosion and to riparian buffers that are shaped by fragmentation, as confirmed by aerial and in-situ surveys. Our procedure enables cost-effective screenings of complex areas able to identify raising hotspots that require urgent and deeper investigations. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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6800 KiB

Open AccessArticle

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

by Linlin Lu, Claudia Kuenzer, Cuizhen Wang, Huadong Guo and Qingting Li

Remote Sens. 2015, 7(6), 7597-7614; https://doi.org/10.3390/rs70607597 - 9 Jun 2015

Cited by 73 | Viewed by 11366

Abstract

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution [...] Read more.

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series’ suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001–2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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10993 KiB

Open AccessArticle

SPOT-Based Sub-Field Level Monitoring of Vegetation Cover Dynamics: A Case of Irrigated Croplands

by Olena Dubovyk, Gunter Menz, Alexander Lee, Juergen Schellberg, Frank Thonfeld and Asia Khamzina

Remote Sens. 2015, 7(6), 6763-6783; https://doi.org/10.3390/rs70606763 - 26 May 2015

Cited by 13 | Viewed by 6332

Abstract

Acquiring multi-temporal spatial information on vegetation condition at scales appropriate for site-specific agricultural management is often complicated by the need for meticulous field measurements. Understanding spatial/temporal crop cover heterogeneity within irrigated croplands may support sustainable land use, specifically in areas affected by land [...] Read more.

Acquiring multi-temporal spatial information on vegetation condition at scales appropriate for site-specific agricultural management is often complicated by the need for meticulous field measurements. Understanding spatial/temporal crop cover heterogeneity within irrigated croplands may support sustainable land use, specifically in areas affected by land degradation due to secondary soil salinization. This study demonstrates the use of multi-temporal, high spatial resolution (10 m) SPOT-4/5 image data in an integrated change vector analysis and spectral mixture analysis (CVA-SMA) procedure. This procedure was implemented with the principal objective of mapping sub-field vegetation cover dynamics in irrigated lowland areas within the lowerlands of the Amu Darya River. CVA intensity and direction were calculated separately for the periods of 1998–2006 and 2006–2010. Cumulative change intensity and the overall directional trend were also derived for the entire observation period of 1998–2010. Results show that most of the vector changes were observed between 1998 and 2006; persistent conditions were seen within the study region during the 2006–2010 period. A decreasing vegetation cover trend was identified within 38% of arable land. Areas of decreasing vegetation cover were located principally in the irrigation system periphery where deficient water supply and low soil quality lead to substandard crop development. During the 2006–2010 timeframe, degraded crop cover conditions persisted in 37% of arable land. Vegetation cover increased in 25% of the arable land where irrigation water supply was adequate. This high sub-field crop performance spatial heterogeneity clearly indicates that current land management practices are inefficient. Such information can provide the basis for implementing and adapting irrigation applications and salt leaching techniques to site-specific conditions and thereby make a significant contribution to sustainable regional land management. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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1401 KiB

Open AccessArticle

Land Degradation Assessment Using Residual Trend Analysis of GIMMS NDVI3g, Soil Moisture and Rainfall in Sub-Saharan West Africa from 1982 to 2012

by Yahaya Z. Ibrahim, Heiko Balzter, Jörg Kaduk and Compton J. Tucker

Remote Sens. 2015, 7(5), 5471-5494; https://doi.org/10.3390/rs70505471 - 30 Apr 2015

Cited by 164 | Viewed by 17078

Abstract

Areas affected by land degradation in Sub-Saharan West Africa between 1982 and 2012 are identified using time-series analysis of vegetation index data derived from satellites. The residual trend (RESTREND) of a Normalized Difference Vegetation Index (NDVI) time-series is defined as the fraction of [...] Read more.

Areas affected by land degradation in Sub-Saharan West Africa between 1982 and 2012 are identified using time-series analysis of vegetation index data derived from satellites. The residual trend (RESTREND) of a Normalized Difference Vegetation Index (NDVI) time-series is defined as the fraction of the difference between the observed NDVI and the NDVI predicted from climate data. It has been widely used to study desertification and other forms of land degradation in drylands. The method works on the assumption that a negative trend of vegetation photosynthetic capacity is an indication of land degradation if it is independent from climate variability. In the past, many scientists depended on rainfall data as the major climatic factor controlling vegetation productivity in drylands when applying the RESTREND method. However, the water that is directly available to vegetation is stored as soil moisture, which is a function of cumulative rainfall, surface runoff, infiltration and evapotranspiration. In this study, the new NDVI third generation (NDVI3g), which was generated by the National Aeronautics and Space Administration-Goddard Space Flight Center Global Inventory Modeling and Mapping Studies (NASA-GSFC GIMMS) group, was used as a satellite-derived proxy of vegetation productivity, together with the soil moisture index product from the Climate Prediction Center (CPC) and rainfall data from the Climate Research Unit (CRU). The results show that the soil moisture/NDVI pixel-wise residual trend indicates land degraded areas more clearly than rainfall/NDVI. The spatial and temporal trends of the RESTREND in the region follow the patterns of drought episodes, reaffirming the difficulties in separating the impacts of drought and land degradation on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification in drylands should go beyond using rainfall as a sole predictor of vegetation condition, and include soil moisture index datasets in the analysis. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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21160 KiB

Open AccessArticle

Drought Variability and Land Degradation in Semiarid Regions: Assessment Using Remote Sensing Data and Drought Indices (1982–2011)

by Sergio M. Vicente-Serrano, Daniel Cabello, Miquel Tomás-Burguera, Natalia Martín-Hernández, Santiago Beguería, Cesar Azorin-Molina and Ahmed El Kenawy

Remote Sens. 2015, 7(4), 4391-4423; https://doi.org/10.3390/rs70404391 - 14 Apr 2015

Cited by 121 | Viewed by 13749

Abstract

We analyzed potential land degradation processes in semiarid regions worldwide using long time series of remote sensing images and the Normalized Difference Vegetation Index (NDVI) for the period 1981 to 2011. The objectives of the study were to identify semiarid regions showing a [...] Read more.

We analyzed potential land degradation processes in semiarid regions worldwide using long time series of remote sensing images and the Normalized Difference Vegetation Index (NDVI) for the period 1981 to 2011. The objectives of the study were to identify semiarid regions showing a marked decrease in potential vegetation activity, indicative of the occurrence of land degradation processes, and to assess the possible influence of the observed drought trends quantified using the Standardized Precipitation Evapotranspiration Index (SPEI). We found that the NDVI values recorded during the period of maximum vegetation activity (NDVI_max) predominantly showed a positive evolution in the majority of the semiarid regions assessed, but NDVI_max was highly correlated with drought variability, and the trends of drought events influenced trends in NDVI_maxat the global scale. The semiarid regions that showed most increase in NDVI_max (the Sahel, northern Australia, South Africa) were characterized by a clear positive trend in the SPEI values, indicative of conditions of greater humidity and lesser drought conditions. While changes in drought severity may be an important driver of NDVI trends and land degradation processes in semiarid regions worldwide, drought did not apparently explain some of the observed changes in NDVI_max. This reflects the complexity of vegetation activity processes in the world’s semiarid regions, and the difficulty of defining a universal response to drought in these regions, where a number of factors (natural and anthropogenic) may also affect on land degradation. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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8585 KiB

Open AccessArticle

Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data

by Lijuan Miao, Peilong Ye, Bin He, Lizi Chen and Xuefeng Cui

Remote Sens. 2015, 7(4), 3863-3877; https://doi.org/10.3390/rs70403863 - 1 Apr 2015

Cited by 53 | Viewed by 10424

Abstract

Dry Land Asia is the largest arid and semi-arid region in the northern hemisphere that suffers from land desertification. Over the period 1982–2011, there were both overall improvement and regional degeneration in the vegetation NDVI. We analyze future climate changes in these area [...] Read more.

Dry Land Asia is the largest arid and semi-arid region in the northern hemisphere that suffers from land desertification. Over the period 1982–2011, there were both overall improvement and regional degeneration in the vegetation NDVI. We analyze future climate changes in these area using two ensemble-average methods from CMIP5 data. Bayesian Model Averaging shows a better capability to represent the future climate and less uncertainty represented by the 22-model ensemble than does the Simple Model Average. From 2006 to 2100, the average growing season temperature value will increase by 2.9 °C, from 14.4 °C to 17.3 °C under three climate scenarios (RCP 26, RCP 45 and RCP 85). We then conduct multiple regression analysis between climate changes compiled from the Climate Research Unit database and vegetation greenness from the GIMMS NDVI3g dataset. There is a general acceleration in the desertification trend under the RCP 85 scenario in middle and northern part of Middle Asia, northwestern China except Xinjiang and the Mongolian Plateau (except the middle part). The RCP 85 scenario shows a more severe desertification trend than does RCP 26. Desertification in dry land Asia, particularly in the regions highlighted in this study, calls for further investigation into climate change impacts and adaptations. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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14189 KiB

Open AccessArticle

Climate Contributions to Vegetation Variations in Central Asian Drylands: Pre- and Post-USSR Collapse

by Yu Zhou, Li Zhang, Rasmus Fensholt, Kun Wang, Irina Vitkovskaya and Feng Tian

Remote Sens. 2015, 7(3), 2449-2470; https://doi.org/10.3390/rs70302449 - 2 Mar 2015

Cited by 116 | Viewed by 14958

Abstract

Central Asia comprises a large fraction of the world’s drylands, known to be vulnerable to climate change. We analyzed the inter-annual trends and the impact of climate variability in the vegetation greenness for Central Asia from 1982 to 2011 using GIMMS3g normalized difference [...] Read more.

Central Asia comprises a large fraction of the world’s drylands, known to be vulnerable to climate change. We analyzed the inter-annual trends and the impact of climate variability in the vegetation greenness for Central Asia from 1982 to 2011 using GIMMS3g normalized difference vegetation index (NDVI) data. In our study, most areas showed an increasing trend during 1982–1991, but experienced a significantly decreasing trend for 1992–2011. Vegetation changes were closely coupled to climate variables (precipitation and temperature) during 1982–1991 and 1992–2011, but the response trajectories differed between these two periods. The warming trend in Central Asia initially enhanced the vegetation greenness before 1991, but the continued warming trend subsequently became a suppressant of further gains in greenness afterwards. Precipitation expanded its influence on larger vegetated areas in 1992–2011 when compared to 1982–1991. Moreover, the time-lag response of plants to rainfall tended to increase after 1992 compared to the pre-1992 period, indicating that plants might have experienced functional transformations to adapt the climate change during the study period. The impact of climate on vegetation was significantly different for the different sub-regions before and after 1992, coinciding with the collapse of the Union of Soviet Socialist Republics (USSR). It was suggested that these spatio-temporal patterns in greenness change and their relationship with climate change for some regions could be explained by the changes in the socio-economic structure resulted from the USSR collapse in late 1991. Our results clearly illustrate the combined influence of climatic/anthropogenic contributions on vegetation growth in Central Asian drylands. Due to the USSR collapse, this region represents a unique case study of the vegetation response to climate changes under different climatic and socio-economic conditions. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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35218 KiB

Open AccessArticle

Remote Sensing of Shrubland Drying in the South-East Mediterranean, 1995–2010: Water-Use-Efficiency-Based Mapping of Biomass Change

by Maxim Shoshany and Lev Karnibad

Remote Sens. 2015, 7(3), 2283-2301; https://doi.org/10.3390/rs70302283 - 26 Feb 2015

Cited by 20 | Viewed by 6690

Abstract

Recent climate studies of the South-Eastern Mediterranean indicate an increase in drought frequencies and decreasing water resources since the turn of the century. A four-phase methodology was developed for assessing above-ground biomass changes in shrublands caused by these recent trends. Firstly, we generalized [...] Read more.

Recent climate studies of the South-Eastern Mediterranean indicate an increase in drought frequencies and decreasing water resources since the turn of the century. A four-phase methodology was developed for assessing above-ground biomass changes in shrublands caused by these recent trends. Firstly, we generalized the function SB = 0.008MAP^1.54 describing the shrublands above-ground biomass (SB) dependence on mean annual precipitation (MAP) for areas of full shrub cover. Secondly, relationships between MAP and NDVI were formalized, allowing an estimation of precipitation levels from observed NDVI values (MAP_NDVI). Thirdly, relative water-use efficiency (RWUE) was defined as the ratio between MAP_NDVI and MAP. Finally, the function SB_RWUE = 0.008MAP^0.54^{+ RWUE} was formalized, utilizing RWUE in estimating shrublands biomass. This methodology was implemented using Landsat TM images (1994 to 2011) for an area between the Judean Mountains and the deserts bordering them to the east and south. More than 50% of the study area revealed low biomass change (±0.2 kg/m²), compared with 30% of the woodlands of the Jerusalem Mountains, where biomass increased between 0.2 and 1.4 kg/m² and with 50% of the semi-arid shrublands, where it decreased between 0.2 and 1.4 kg/m². These results suggest that aridity lines in southern Israel are migrating northwards. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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939 KiB

Open AccessArticle

Contraction of the Gobi Desert, 2000–2012

by Troy Sternberg, Henri Rueff and Nick Middleton

Remote Sens. 2015, 7(2), 1346-1358; https://doi.org/10.3390/rs70201346 - 26 Jan 2015

Cited by 39 | Viewed by 23904

Abstract

Deserts are critical environments because they cover 41% of the world’s land surface and are home to 2 billion residents. As highly dynamic biomes desert expansion and contraction is influenced by climate and anthropogenic factors with variability being a key part of the [...] Read more.

Deserts are critical environments because they cover 41% of the world’s land surface and are home to 2 billion residents. As highly dynamic biomes desert expansion and contraction is influenced by climate and anthropogenic factors with variability being a key part of the desertification debate across dryland regions. Evaluating a major world desert, the Gobi in East Asia, with high resolution satellite data and the meteorologically-derived Aridity Index from 2000 to 2012 identified a recent contraction of the Gobi. The fluctuation in area, primarily driven by precipitation, is at odds with numerous reports of human-induced desertification in Mongolia and China. There are striking parallels between the vagueness in defining the Gobi and the imprecision and controversy surrounding the Sahara desert’s southern boundary in the 1980s and 1990s. Improved boundary definition has implications fGobi; desert boundary; expansion and contraction; Aridity Index; NDVI; Mongolia; China or understanding desert “greening” and “browning”, human action and land use, ecological productivity and changing climate parameters in the region. The Gobi’s average area of 2.3 million km² in the 21st century places it behind only the Sahara and Arabian deserts in size. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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12955 KiB

Open AccessArticle

GRACE Gravity Satellite Observations of Terrestrial Water Storage Changes for Drought Characterization in the Arid Land of Northwestern China

by Yanping Cao, Zhuotong Nan and Guodong Cheng

Remote Sens. 2015, 7(1), 1021-1047; https://doi.org/10.3390/rs70101021 - 16 Jan 2015

Cited by 104 | Viewed by 13015

Abstract

Drought is a complex natural hazard which can have negative effects on agriculture, economy, and human life. In this paper, the primary goal is to explore the application of the Gravity Recovery and Climate Experiment (GRACE) gravity satellite data for the quantitative investigation [...] Read more.

Drought is a complex natural hazard which can have negative effects on agriculture, economy, and human life. In this paper, the primary goal is to explore the application of the Gravity Recovery and Climate Experiment (GRACE) gravity satellite data for the quantitative investigation of the recent drought dynamic over the arid land of northwestern China, a region with scarce hydrological and meteorological observation datasets. The spatiotemporal characteristics of terrestrial water storage changes (TWSC) were first evaluated based on the GRACE satellite data, and then validated against hydrological model simulations and precipitation data. A drought index, the total storage deficit index (TSDI), was derived on the basis of GRACE-recovered TWSC. The spatiotemporal distributions of drought events from 2003 to 2012 in the study region were obtained using the GRACE-derived TSDI. Results derived from TSDI time series indicated that, apart from four short-term (three months) drought events, the study region experienced a severe long-term drought from May 2008 to December 2009. As shown in the spatial distribution of TSDI-derived drought conditions, this long-term drought mainly concentrated in the northwestern area of the entire region, where the terrestrial water storage was in heavy deficit. These drought characteristics, which were detected by TSDI, were consistent with local news reports and other researchers’ results. Furthermore, a comparison between TSDI and Standardized Precipitation Index (SPI) implied that GRACE TSDI was a more reliable integrated drought indicator (monitoring agricultural and hydrological drought) in terms of considering total terrestrial water storages for large regions. The GRACE-derived TSDI can therefore be used to characterize and monitor large-scale droughts in the arid regions, being of special value for areas with scarce observations. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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20665 KiB

Open AccessArticle

Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression

by Xingwang Fan, Yuanbo Liu, Jinmei Tao and Yongling Weng

Remote Sens. 2015, 7(1), 488-511; https://doi.org/10.3390/rs70100488 - 6 Jan 2015

Cited by 97 | Viewed by 11643

Abstract

Improper use of land resources may result in severe soil salinization. Timely monitoring and early warning of soil salinity is in urgent need for sustainable development. This paper addresses the possibility and potential of Advanced Land Imager (ALI) for mapping soil salinity. In [...] Read more.

Improper use of land resources may result in severe soil salinization. Timely monitoring and early warning of soil salinity is in urgent need for sustainable development. This paper addresses the possibility and potential of Advanced Land Imager (ALI) for mapping soil salinity. In situ field spectra and soil salinity data were collected in the Yellow River Delta, China. Statistical analysis demonstrated the importance of ALI blue and near infrared (NIR) bands for soil salinity. A partial least square regression (PLSR) model was established between soil salinity and ALI-convolved field spectra. The model estimated soil salinity with a R² (coefficient of determination), RPD (ratio of prediction to deviation), bias, standard deviation (SD) and root mean square error (RMSE) of 0.749, 3.584, 0.036 g∙kg⁻¹, 0.778 g∙kg⁻¹ and 0.779 g∙kg⁻¹. The model was then applied to atmospherically corrected ALI data. Soil salinity was underestimated for moderately (soil salinity within 2–4 g∙kg⁻¹) and highly saline (soil salinity >4 g∙kg⁻¹) soils. The underestimates increased with the degree of soil salinization, with a maximum value of ~4 g∙kg⁻¹. The major contribution for the underestimation (>80%) may result from data inaccuracy other than model ineffectiveness. Uncertainty analysis confirmed that improper atmospheric correction contributed to a very conservative uncertainty of 1.3 g∙kg⁻¹. Field sampling within remote sensing pixels was probably the major source responsible for the underestimation. Our study demonstrates the effectiveness of PLSR model in retrieving soil salinity from new-generation multi-spectral sensors. This is very valuable for achieving worldwide soil salinity mapping with low cost and considerable accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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2285 KiB

Open AccessArticle

Remote Sensing of Sonoran Desert Vegetation Structure and Phenology with Ground-Based LiDAR

by Joel B. Sankey, Seth M. Munson, Robert H. Webb, Cynthia S. A. Wallace and Cesar M. Duran

Remote Sens. 2015, 7(1), 342-359; https://doi.org/10.3390/rs70100342 - 30 Dec 2014

Cited by 17 | Viewed by 7701

Abstract

Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics [...] Read more.

Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics at high spatial and temporal resolution. We determined the effectiveness of LiDAR to detect intra-annual variability in vegetation structure at a long-term Sonoran Desert monitoring plot dominated by cacti, deciduous and evergreen shrubs. Monthly repeat LiDAR scans of perennial plant canopies over the course of one year had high precision. LiDAR measurements of canopy height and area were accurate with respect to total station survey measurements of individual plants. We found an increase in the number of LiDAR vegetation returns following the wet North American Monsoon season. This intra-annual variability in vegetation structure detected by LiDAR was attributable to a drought deciduous shrub Ambrosia deltoidea, whereas the evergreen shrub Larrea tridentata and cactus Opuntia engelmannii had low variability. Benefits of using LiDAR over traditional methods to census desert plants are more rapid, consistent, and cost-effective data acquisition in a high-resolution, 3-dimensional context. We conclude that repeat LiDAR measurements can be an effective method for documenting ecosystem response to desert climatology and drought over short time intervals and at detailed-local spatial scale. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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1296 KiB

Open AccessArticle

Spectral Slope as an Indicator of Pasture Quality

by Rachel Lugassi, Alexandra Chudnovsky, Eli Zaady, Levana Dvash and Naftaly Goldshleger

Remote Sens. 2015, 7(1), 256-274; https://doi.org/10.3390/rs70100256 - 25 Dec 2014

Cited by 19 | Viewed by 7813

Abstract

In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. First, differences in spectral behavior were identified across the near infrared–shortwave infrared spectral range that were indicative [...] Read more.

In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. First, differences in spectral behavior were identified across the near infrared–shortwave infrared spectral range that were indicative of changes in chemical properties. Then, slopes across different spectral ranges were calculated and correlated with the changes in crude protein (CP), neutral detergent fiber (NDF) and metabolic energy concentration (MEC). Finally, partial least squares (PLS) regression analysis was applied to identify the optimal spectral ranges for accurate assessment of CP, NDF and MEC. Six spectral domains and a set of slope criteria for real-time evaluation of pasture quality were suggested. The evaluation of three level categories (low, medium, high) for these three parameters showed a success rate of: 73%–96% for CP, 72%–87% for NDF and 60%–85% for MEC. Moreover, only one spectral range, 1748–1764 nm, was needed to provide a good estimation of CP, NDF and MEC. Importantly, five of the six selected spectral regions were not affected by water absorbance. With some modifications, this rationale can be applied to further analyses of pasture quality from airborne sensors. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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6664 KiB

Open AccessArticle

Spatio-Temporal Dynamics of Land-Use and Land-Cover in the Mu Us Sandy Land, China, Using the Change Vector Analysis Technique

by Arnon Karnieli, Zhihao Qin, Bo Wu, Natalya Panov and Feng Yan

Remote Sens. 2014, 6(10), 9316-9339; https://doi.org/10.3390/rs6109316 - 29 Sep 2014

Cited by 73 | Viewed by 9848

Abstract

The spatial extent of desertified vs. rehabilitated areas in the Mu Us Sandy Land, China, was explored. The area is characterized by complex landscape changes that were caused by different drivers, either natural or anthropogenic, interacting with each other, and resulting in multiple [...] Read more.

The spatial extent of desertified vs. rehabilitated areas in the Mu Us Sandy Land, China, was explored. The area is characterized by complex landscape changes that were caused by different drivers, either natural or anthropogenic, interacting with each other, and resulting in multiple consequences. Two biophysical variables, NDVI, positively correlated with vegetation cover, and albedo, positively correlated with cover of exposed sands, were computed from a time series of merged NOAA-AVHRR and MODIS images (1981 to 2010). Generally, throughout the study period, NDVI increased and albedo decreased. Improved understanding of spatial and temporal dynamics of these environmental processes was achieved by using the Change Vector Analysis (CVA) technique applied to NDVI and albedo data extracted from four sets of consecutive Landsat images, several years apart. Changes were detected for each time step, as well as over the entire period (1978 to 2007). Four categories of land cover were created—vegetation, exposed sands, water bodies and wetlands. The CVA’s direction and magnitude enable detecting and quantifying finer changes compared to separate NDVI or albedo difference/ratio images and result in pixel-based maps of the change. Each of the four categories has a biophysical meaning that was validated in selected hot-spots, employing very high spatial resolution images (e.g., Ikonos). Selection of images, taking into account inter and intra annual variability of rainfall, enables differentiating between short-term conservancies (e.g., drought) and long-term alterations. NDVI and albedo, although comparable to tasseled cap’s brightness and greenness indices, have the advantage of being computed using reflectance values extracted from various Landsat platforms since the early 1970s. It is shown that, over the entire study period, the majority of the Mu Us Sandy Land area remained unchanged. Part of the area (6%), mainly in the east, was under human-induced rehabilitation processes, in terms of increasing vegetation cover. In other areas (5.1%), bare sands were found to expand to the central-north and the southwest of the area. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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2233 KiB

Open AccessArticle

Changes in Spring Phenology in the Three-Rivers Headwater Region from 1999 to 2013

by Xianfeng Liu, Xiufang Zhu, Wenquan Zhu, Yaozhong Pan, Chong Zhang and Donghai Zhang

Remote Sens. 2014, 6(9), 9130-9144; https://doi.org/10.3390/rs6099130 - 24 Sep 2014

Cited by 26 | Viewed by 6422

Abstract

Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and [...] Read more.

Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and characterized their driving forces using climatic data sets. A significant advancement trend was observed throughout the entire study area from 1999 to 2013 with a linear tendency of 6.3 days/decade (p < 0.01); the largest advancement trend was over the Yellow River source region (8.6 days/decade, p < 0.01). Spatially, the green-up date increased from the southeast to the northwest, and the green-up date of 87.4% of pixels fell between the 130th and 150th Julian day. Additionally, about 91.5% of the study area experienced advancement in the green-up date, of which 80.2%, mainly distributed in areas of vegetation coverage increase, experienced a significant advance. Moreover, it was found that the green-up date and its trend were significantly correlated with altitude. Statistical analyses showed that a 1-°C increase in spring temperature would induce an advancement in the green-up date of 4.2 days. We suggest that the advancement of the green-up date in the TRHR might be attributable principally to warmer and wetter springs. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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9698 KiB

Open AccessArticle

Structural Changes of Desertified and Managed Shrubland Landscapes in Response to Drought: Spectral, Spatial and Temporal Analyses

by Tarin Paz-Kagan, Natalya Panov, Moshe Shachak, Eli Zaady and Arnon Karnieli

Remote Sens. 2014, 6(9), 8134-8164; https://doi.org/10.3390/rs6098134 - 28 Aug 2014

Cited by 20 | Viewed by 7128

Abstract

Drought events cause changes in ecosystem function and structure by reducing the shrub abundance and expanding the biological soil crusts (biocrusts). This change increases the leakage of nutrient resources and water into the river streams in semi-arid areas. A common management solution for [...] Read more.

Drought events cause changes in ecosystem function and structure by reducing the shrub abundance and expanding the biological soil crusts (biocrusts). This change increases the leakage of nutrient resources and water into the river streams in semi-arid areas. A common management solution for decreasing this loss of resources is to create a runoff-harvesting system (RHS). The objective of the current research is to apply geo-information techniques, including remote sensing and geographic information systems (GIS), on the watershed scale, to monitor and analyze the spatial and temporal changes in response to drought of two source-sink systems, the natural shrubland and the human-made RHSs in the semi-arid area of the northern Negev Desert, Israel. This was done by evaluating the changes in soil, vegetation and landscape cover. The spatial changes were evaluated by three spectral indices: Normalized Difference Vegetation Index (NDVI), Crust Index (CI) and landscape classification change between 2003 and 2010. In addition, we examined the effects of environmental factors on NDVI, CI and their clustering after successive drought years. The results show that vegetation cover indicates a negative ∆NDVI change due to a reduction in the abundance of woody vegetation. On the other hand, the soil cover change data indicate a positive ∆CI change due to the expansion of the biocrusts. These two trends are evidence for degradation processes in terms of resource conservation and bio-production. A considerable part of the changed area (39%) represents transitions between redistribution processes of resources, such as water, sediments, nutrients and seeds, on the watershed scale. In the pre-drought period, resource redistribution mainly occurred on the slope scale, while in the post-drought period, resource redistribution occurred on the whole watershed scale. However, the RHS management is effective in reducing leakage, since these systems are located on the slopes where the magnitude of runoff pulses is low. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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3281 KiB

Open AccessArticle

Soil Surface Sealing Effect on Soil Moisture at a Semiarid Hillslope: Implications for Remote Sensing Estimation

by Shai Sela, Tal Svoray and Shmuel Assouline

Remote Sens. 2014, 6(8), 7469-7490; https://doi.org/10.3390/rs6087469 - 13 Aug 2014

Cited by 9 | Viewed by 6566

Abstract

Robust estimation of soil moisture using microwave remote sensing depends on extensive ground sampling for calibration and validation of the data. Soil surface sealing is a frequent phenomenon in dry environments. It modulates soil moisture close to the soil surface and, thus, has [...] Read more.

Robust estimation of soil moisture using microwave remote sensing depends on extensive ground sampling for calibration and validation of the data. Soil surface sealing is a frequent phenomenon in dry environments. It modulates soil moisture close to the soil surface and, thus, has the potential to affect the retrieval of soil moisture from microwave remote sensing and the validation of these data based on ground observations. We addressed this issue using a physically-based modeling approach that accounts explicitly for surface sealing at the hillslope scale. Simulated mean soil moisture at the respective layers corresponding to both the ground validation probe and the radar beam’s typical effective penetration depth were considered. A cyclic pattern was found in which, as compared to an unsealed profile, the seal layer intensifies the bias in validation during rainfall events and substantially reduces it during subsequent drying periods. The analysis of this cyclic pattern showed that, accounting for soil moisture dynamics at the soil surface, the optimal time for soil sampling following a rainfall event is a few hours in the case of an unsealed system and a few days in the case of a sealed one. Surface sealing was found to increase the temporal stability of soil moisture. In both sealed and unsealed systems, the greatest temporal stability was observed at positions with moderate slope inclination. Soil porosity was the best predictor of soil moisture temporal stability, indicating that prior knowledge regarding the soil texture distribution is crucial for the application of remote sensing validation schemes. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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695 KiB

Open AccessArticle

Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China

by Sawaid Abbas, Janet E. Nichol, Faisal M. Qamer and Jianchu Xu

Remote Sens. 2014, 6(6), 4998-5018; https://doi.org/10.3390/rs6064998 - 30 May 2014

Cited by 89 | Viewed by 12284

Abstract

This study assesses the applicability of remote sensing data for retrieval of key drought indicators including the degree of moisture deficiency, drought duration and areal extent of drought within different land cover types across the landscape. A Normalized Vegetation Supply Water Index (NVSWI) [...] Read more.

This study assesses the applicability of remote sensing data for retrieval of key drought indicators including the degree of moisture deficiency, drought duration and areal extent of drought within different land cover types across the landscape. A Normalized Vegetation Supply Water Index (NVSWI) is devised, combining remotely sensed climate data to retrieve key drought indicators over different vegetation cover types and a lag-time relationship is established based on preceding rainfall. The results indicate that during the major drought event of spring 2010, Evergreen Forest (EF) experienced severe dry conditions for 48 days fewer than Cropland (CL) and Shrubland (SL). Testing of vegetation response to drought conditions with different lag-time periods since the last rainfall indicated a highest correlation for CL and SL with the 4th lag period (i.e., 64 days) whereas EF exhibited maximum correlation with the 5th lag period (i.e., 80 days). Evergreen Forest, which includes tree crops, appears to act as a green reservoir of water, and is more resistant than CL and SL to drought due to its water retention capacity with deeper roots to tap sub-surface water. Identifying differences in rainfall lag-time relationships among land cover types using a remote sensing-based integrated drought index enables more accurate drought prediction, and can thus assist in the development of more specific drought adaptation strategies. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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813 KiB

Open AccessReview

What Four Decades of Earth Observation Tell Us about Land Degradation in the Sahel?

by Cheikh Mbow, Martin Brandt, Issa Ouedraogo, Jan De Leeuw and Michael Marshall

Remote Sens. 2015, 7(4), 4048-4067; https://doi.org/10.3390/rs70404048 - 2 Apr 2015

Cited by 72 | Viewed by 15570

Abstract

The assessment of land degradation and the quantification of its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation (EO) applications, little agreement has been gained on the magnitude and direction of land degradation [...] Read more.

The assessment of land degradation and the quantification of its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation (EO) applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The large number of EO datasets and methods associated with the complex interactions among biophysical and social drivers of ecosystem changes make it difficult to apply aggregated EO indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends. Consequently, many uncertainties exist in regression models between rainfall, biomass and various indices that limit the ability of EO science to adequately assess and develop a consistent message on the magnitude of land degradation. We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term field-surveys and (7) consider local perceptions and social dynamics. To allow multiple perspectives and avoid erroneous interpretations, we underline that EO results should not be interpreted without contextual knowledge. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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1063 KiB

Open AccessReview

Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions

by Thomas P. Higginbottom and Elias Symeonakis

Remote Sens. 2014, 6(10), 9552-9575; https://doi.org/10.3390/rs6109552 - 10 Oct 2014

Cited by 237 | Viewed by 22322

Abstract

Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being [...] Read more.

Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being a candidate choice for the development of monitoring systems. This paper reviews the statistical and ecological frameworks of assessing land degradation and desertification using vegetation index data. The development of multi-temporal analysis as a desertification assessment technique is reviewed, with a focus on how current practice has been shaped by controversy and dispute within the literature. The statistical techniques commonly employed are examined from both a statistical as well as ecological point of view, and recommendations are made for future research directions. The scientific requirements for degradation and desertification monitoring systems identified here are: (I) the validation of methodologies in a robust and comparable manner; and (II) the detection of degradation at minor intensities and magnitudes. It is also established that the multi-temporal analysis of vegetation index data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 30 years, considerable progress has been made in the respective research. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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