R E P O R T S O F T H E N A T IO N A L A C A D E M Y O F S C IE N C E S O F T H E R E P U B L IC O F K A Z A K H S T A N
ISSN 2224-5227 h ttps://d oi.o rg /1 0.3 20 14 /2 02 0.25 18 -1 48 3.3 1
V olum e 2, N um ber 330 (2020), 49 - 57 UDK 504.064
ГРНТИ 36.33.27
G .Y . S a d u o v a 1, G .T . Is s a n o v a 1,2, Y .K h . K a k im z h a n o v 1, J . A b u d u w a ili3
1Al-Farabi Kazakh National University, Faculty of Geography and Environmental Sciences, Almaty, Kazakhstan;
2Research Centre of Ecology and Environment of Central Asia, Almaty, Kazakhstan;
3Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, China.
E-mail: [email protected], [email protected], [email protected], [email protected]
STUDY AND MAPPING OF DEGRADATION OF THE ARALKUM DESERT
A bstract. Desertification is a land degradation in arid, semi-arid and dry sub-humid areas as a result of various factors, including climate change and human activities. The Aral Sea is called the once large, and now almost dry, salt lake, which today looks like a large salt desert. Its drying is considered one of the greatest environmental disasters of our time, because only 50 years ago it was one of the four largest lakes on our planet. In the 1990s, the western world learned about the environmental disaster of what used to be the fourth largest lake in the world - The Aral Sea. The abrupt drying of the Aral Sea led to the intensive development of desertification processes in the region and the formation of the new man-made Aralkum desert.
The main method for determining the soil degradation of the Aralkum desert is the calculation of indices. The selected methods are widely known and repeatedly applied in world practice methods for processing satellite images.
So, all of the listed spectral indices are universal. The uniqueness of this technique lies in the determination of the exact ranges of values for each of the spectral indices in the identification of various indicators of desertification.
Key words: degradation, desertification, satellite images, Landsat-5, NDVI, SI, TCT.
In tro d u c tio n . In the 1990s, the w estern w orld learned about the environm ental disaster th at occurred at the fourth largest lake in the w orld - the A ral Sea. The decline o f the A ral Sea w as called one o f the w orst environm ental disasters o f the 20th century and called “quiet C hernobyl [1]. The sharp drying o f the A ral Sea led to the intensification o f desertification processes in the region and the developm ent o f the new A ralkum desert on the dried up seabed. O ver the p ast few decades, the open bottom has becom e a new “hot spot” o f dust and salt storm s in the region. The arid lands o f South K azakhstan, U zbekistan and T urkm enistan have alw ays been exposed to dangerous dust storm s. H ow ever, in the last thirty years o f the 20th century, dust storm s have show n a significant dow nw ard tren d thro ug ho ut the region [2].
In the arid regions o f C entral A sia, dust storm s are observed w ith frequencies th at are am ong the highest in the w orld [3]. The em ergence o f an additional vast territory subject to w ind erosion, nam ely the anthropogenic created A ralkum desert, inevitably led to the activation o f dust em ission processes [4].
The w ater level in the A ral Sea started drastically decreasing from the 1960s onw ard. The reduction in the volum e and area o f the sea has led to significant changes in the hydrological, chem ical and natural biological structure o f the w ater [5]. The A ral sedim entary basin w ith an area o f 70 thousand k m 2 occupies the north-w estern outskirts o f the T uranPlate [6]. The A ral sedim entary basin extends from the M ugodzhar m ountains in the north to Sultanuizdag in the south fo r m ore th an 440 km . Its w idth from the A ral-K yzylkum shaft in the w est to the A kkyrsko-K um kalinsky shaft in the east is about 210 km [7]. The engineering and geological conditions o f logistic center territories o f A ralsk and T urkistan citie’s determ ined by the developm ent o f loose rocks. They related w ith the m anifistations o f num erous hazardous geological processes like salinization, sw am ping, deflation, linear erosion, slope w ash, the phenom enon o f subsidence, etc. T heir developm ent conditions significantly depend on anthropogenic factor.Sand m assifs are affected by active deflation processes and w ith the developm ent o f the territory can cover large areas [8].
P u rp o s e o f th e re s e a rc h . The A ral Sea is a term inal salt lake in C entral A sia, on the border o f K azakhstan and U zbekistan. Since the 1960s o f X X century, sea level has been rapidly dropping due to
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w ater abstraction from the m ain supply rivers: A m udarya and Syrdarya. B efore the shallow ing, the A ral Sea w as the fourth largest lake in the w orld. O ver-abstraction o f w ater fo r irrigation o f agricultural land has turned the fourth largest lake-sea in the w orld, form erly rich in life, into a barren desert [9]. W ater is a trem endous value, a national treasure. The decision o f all the m ost actual ecological and hydrogeoecological problem s depends on the state o f w ater resources [10].
D rying o f the A ral Sea led to tw o different types o f desertification. R ecently dried seabed and artificial cutting o f irrigated lands. A s a result, a new A ralkum desert appeared in the center o f the great deserts. The sandy-salt w aste lies on the territory o f U zbekistan and K azakhstan, on the northw estern extrem ity o f the K arakum and K yzylkum deserts. A ralkum covers an area o f m ore th an 38 thousand km 2 and is a pow erful source o f w ind loss (figure 1) [11].
Figure 1 - Studyarea- Aralkum desert
The A ral Sea and the A ralkum are located w ithin the A siatic desert b elt [12]. The clim ate o f the surroundings o f A ralkum is characterized by very cold w inters and very ho t sum m ers. It is assum ed that the effects o f the drying o f the A ral Sea w ere associated w ith clim atic conditions. C urrent clim ate data show only a slight shift tow ards a m ore em phasized continentality [13]. M eteorological data show th at in the M iddle E ast region, annual rainfall has increased over the p ast 50 years [14]. This is due to all parts o f the T uran Plain, as w ell as to high m ountains on the outskirts [15]. D irectly at the rem nants o f the A ral
Sea, and at the A ralkum , at the stations on the form er islands (B arsa-K elm es, Lazarev, V ozrozhdeniya, T igrovni) this trend cannot be traced significantly. H ow ever, those stations m ostly have been closed since the m iddle or the end o f the 1980s.Thus, only the last 15 o r 20 years o f observations w ould have been especially w orthw hile. O nly the A ral station clearly indicates an increase in precipitation w ithin the 1950s, bu t w ithin the last 40 years the m ean precipitation seem s to be stable [16].
M a te ria ls a n d m eth o d s. C urrently, the quantitative characteristics o f m odels o f tem porary and spatial plants w ith rem ote sensing data are o f great interest for the study o f Earth sciences and global changes. Spectral m odels and indicators are being developed to increase sensitivity to plants, taking into account the effects o f the atm osphere and soil.
In the table below (table 1), a list o f desertification indicators is given and the possibility o f determ ining these indicators using rem ote sensing data is noted.
Table 1 - Spectral Indices
№ Desertification Indicators Spectral Indices
1 Vegetation cover NDVI
2 Soilsalinization SI
3 Water ТСТ
The N orm alized D ifference V egetation Index (N D V I) is one o f the m ost w idely used plant indexes, and its usefulness for satellite-based assessm ent and m onitoring o f global vegetation has been well dem onstrated o ver the past tw o decades [17].
IE E E T ransactions on G eoscience and R em ote Sensing, 32, 8 97 -9 0 5 .
N orm alized V egetation Index N D V I is a standardized index show ing the presence and condition o f vegetation (relative biom ass). This index uses the contrast o f the characteristics o f tw o channels from a set o f m ultispectral raster data - the absorption o f chlorophyll by the pigm ent in the red channel and the high reflectivity o f plant m aterials in the infrared channel (NIR).
N D V I isoften used around the w orld to m on itor drought, m onitor and forecast agricultural production, assist in forecasting fire hazard zones, and desert attack m aps. N D V I is preferred for global m onitoring o f vegetation, as it helps to com pensate for changes in lighting conditions, surface slope, exposure and other external factors. N D V I indices range from -1.0 to 1.0, w here higher values relate to green vegetation and tiny ones relate to other w idespread surface resources. N aked soil is indicated by N D V I values th at are in contact w ith 0, and w ater bodies are characterized by negative N D V I values [18].
D ifferent reflection in the red and infrared (IR) channels allow s you to control the density and grow th rate o f green vegetation using the spectral reflection o f solar radiation. G reen leaves usually show better reflection in the n ear infrared w avelength range than in visible w avelength ranges. I f the leaves are suppressed by w ater, fading or dead, they becom e m ore yellow and reflect m uch less in the near infrared range. C louds, w ater and snow give a b etter reflection in the visible range than in the n ear infrared range, w hile the difference is alm ost zero for rocks and bare soil. N D V I processing creates a single-channel dataset th at basically represents greenery. N egative values represent clouds, w ater and snow, and values close to zero represent rocks and bare soil.
The default docum ented N D V I equation:
NDVI = (NIR-Red) (1),
(NIR+Red) v
w here, N IR is reflection in the near infrared region o f the spectrum , R ED is reflection in the red region o f the spectrum . The m ain research m ethod w as the analysis o f the norm alized relative vegetation index (N D V I - N orm alized D ifference V egetation Index), w hich w as first described by B.J. R ouse in 1973 [19].
N D V I provides accessible constructive inform ation for distinguishing and interpreting vegetation cover th at is w idely used in rem ote sensing studies [20].
Salinization o f the soil is the process o f enriching the soil w ith soluble salts, w hich leads to obtaining inform ation about the soil exposed to salt. Soil salinization in irrigated areas is becom ing a serious problem fo r agriculture. Saline soil conditions have reduced the value and productivity o f large areas o f land around the w orld [21].Salinity usually occurs in irrigated soils due to the accum ulation o f soluble salts as a result o f continuous use o f irrigation w ater containing large or m edium am ounts o f dissolved salts [22]. The m ain problem s associated w ith arid and sem i-arid areas are salinization and desertification.
Soil salinization is the m ain form o f land degradation in agricultural areas w here inform ation is needed on the degree and m agnitude o f soil salinity for b etter planning and im plem entation o f effective soil reclam ation program s.Irrigational evaporation o f m oisture from the surface o r shallow depths w ithin the profile and insufficient annual precipitation to leach salts from the rooting zone o f plants contribute to the excessive accum ulation o f soluble salts in soils o f arid and sem i-arid regions, w hich m akes such lands w ith m inim al success [23].
Estim ation o f soil salinity can be applied using digital indices extracted from satellite im ages w ith different spectral bands [24]. The digital elevation m odel (D EM ) can also be used to predict soil salinization based on the variographic m orphology o f the earth ’s surface in order to increase the accuracy o f its prediction [25]. The rapid spread o f salinization o f the soil m ainly depends on altitude, because the terrain controls the rate o f salt transfer through different layers o f soil [26].
It has been proven th at rem ote sensing has advantages in predicting soil salinity. M eanw hile, the spatial distribution o f soil salinity is apparently associated w ith one o r m ore variables at the sam e tim e, depending on the characteristics o f the study area [27]. R esearchers have developed m any approaches to studying and predicting soil salinity w ith m ultiple variables using statistical analysis [28].
Sixteen different spectral salinity indices developed in num erous studies related to salt detection and soil salinity m apping w ere studied for all L andsat im ages and seven salinity indices w ere m ost com m only used (ND SI, SI 1, SI 2, SI 4, SI 9, SI 10 , SI 14) taken into account in this study are given in table 2 [29].
5 1
Table 2 - The most common salinity indicators (A. Azabdaftari a, F. Sunarb, 2016)
Salinity indices Equation Reference
Normalized Differential Salinity Index n d s i=(r-nir)(R+NIR) (Khan, Rastoskuev et al. 2001)
Salinity Index 1 SI1=VB x R (Khan, Rastoskuev et al. 2001)
Salinity Index 2 SI2=V G x R (Douaoui, 2006)
Salinity Index 4 SI4=VG2 + R2 (Douaoui, 2006)
Salinity Index 9 SI9=(B5 XB6 B6 XB6
B5 (Bannari, Guedon et al. 2008)
Salinity Index 10 SI10=BR (Abbas, 2007)
Salinity Index 14 SI14=RxNIRG (Abbas, 2007)
W e have used the form ula from the indices th at determ ine the salinization o f the soil, as follows:
S I2 = V G reen x R ed (2)
V arious analyzes, including rem ote sensing and spatial m odeling using GIS, a regression m odel, and m ethod validation, w ere used to determ ine the feasibility o f rem ote sensing and a geographic inform ation system fo r m apping salinity o f the soil directly from the soil and indirectly from the vegetation.
T asseled Cap Transform ation (TCT) is calculated using the form ula (table 3).
Table 3 - Tasseled Cap Transformation (TCT) calculation formula (Healey S.P., et al, 2005) Tasseled
CapT ransformation
Formula
Brightness 0.3037 x (b1) + 0.2793 x (b2) + 0.4743 x (b3) + 0.5585 x (b4) + 0.5082 x (b5) + 0.1863 x (b7) Green 0.2848 x (b1) - 0.2435x (b2) - 0.5436 x (b3) + 0.7243 x (b4) + 0.0840 x (b5) - 0.1800 x (b7) Humidity 0.1509 x (b1) + 0.1973 x (b2) + 0.3279 x (b3) + 0.3406 x (b4) - 0.7112 x (b5) - 0.4572 x (b7)
T C T coefficients are used in the w idest range o f problem s solved using Earth rem ote sensing data:
from recognition o f the coastline o f w ater bodies to determ ination o f forest disturbances [30]. This technique uses the B rightness coefficient TCT.
R e su lts a n d D iscussion. The selected m ethods are w idely know n and repeatedly applied in w orld practice m ethods fo r processing satellite im ages. So, all the listed spectral indices are universal, how ever, th ey are relative. The uniqueness o f this technique lies in the determ ination o f the exact ranges o f values for each o f the spectral indices in the identification o f various indicators o f desertification. This refinem ent w as m ade possible thanks to field survey data. H ow ever, ground-based data w ere taken for one field season, therefore, the inclusion o f m ore scientists in the calculation o f ground-based survey data w ill allow us to m ore accurately determ ine the ranges o f index values fo r each o f the desertification indicators.
L andsat TM data for 2000 and 2008 years w ere taken for this w ork (table 4). The im age has seven bands w ith a resolution o f 30 m pixels. Im age analysis w as perform ed using A rcG IS 10.3.
Table 4 - Space imagery selection
Image type Shooting date
Landsdat TM5 09.16.2000; 09.16.2000; 09.07.2000; 09.07.2000 Landsdat TM5 07.11.2008; 07.11.2008; 07.27.2008; 07.27.2008
Since all the data received from the satellite is nothing m ore than m ultispectral im ages, to obtain the inform ation contained in them , it is necessary to interpret the d ata obtained and reveal th eir physical m eaning.
The stage o f E arth rem ote sensing data analysis, the m ain task o f w hich is the recognition and identification o f objects detected in the im age, is called im age decryption.
F o r Landsat-5 im ages, in accordance w ith the process o f soil degradation, salinization, and desertification, the interpretation o f these channel com binations w as chosen, i.e., the color com bination
7.5.3. This com bination gives an im age close to natural colors, bu t at the sam e tim e allow s you to analyze the state o f the atm osphere and smoke. H ealthy vegetation looks brig ht green, grassy com m unities look green, brigh t pink areas detect open soil, brow n and orange tones are typical for sparse vegetation. D ead standing vegetation looks orange, w ater - blue. Sand, soil and m inerals can be represented by a very large nu m ber o f colors and shades. This com bination gives an excellent result in the analysis o f deserts and desertified territories (figures 2, 3) [31].
Figure 2 - Satellite image based on a combination of 7.5.3 colors in the Study area, 2000
Figure 3 - Satellite image based on a combination of 7.5.3 colors in the Study area, 2008
F or Landsat-5 im ages, the norm alized differential vegetation index (ND VI) w as calculated as follows:
(band4-band3)
NDVI = .
(band4+band3) A lso, the SI form ula (salinity index) for Landsat-5 im ages:
(3)
S I2 = V b a n d 2 x b a n d 3 (4)
The change in sea w ater w as calculated by the form ula T C T (Tasseled Cap Transform ation):
T C T = 0,304 x (b1) + 0,279 x (b2) + 0,474 x (b3) + 0,559 x (b4) + 0,508 x (b5) + 0,186 x (b7), (5) w here, b1, b2, b3, b4, b5, b7 are the corresponding L andsat channels.
A s a result, the Soil D egradation m aps for 2000 and 2008w ere com piled (1:2500 000) (figures 4, 5).
" IHgradMlio
1---
,*** fir
Map Of Ihc Aralkum D ttrrt. 2008 Scale 1:2 5001100
f ) $ !
1,1
Figure 4 - Soil Degradation Map Figure 5 - Soil Degradation Map
of the Aralkum Desert, 2000 of the Aralkum Desert, 2008
The results o f the calculation o f the tw o-year index are presented in the follow ing table. A s a result, the desertification process in 2008 increased by 2 6 ,87 1k m 2 desert area com pared to 2000, the bottom area w as 13 8 49k m 2, and the vegetation cover w as reduced to 1175 k m 2 . A s a result, the drying o f the A ral Sea, the A ralkum desert increased, and the soil degraded significantly (table 5). The quantitative indicator for 2000 and 2008 obtained from the results o f calculating the index is show n in diagram (figures 6, 7).
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Table 5 - Dynamics of changes in objects of the Aral Sea and its region in 2000 and 2008
Calculated objects Area
2000 year 2008 year
Desert 33 354km2 60 225km2
Vegetation 7 375km2 6 200km2
Water 24 428km2 10 579 km2
d e se rt vegetation water d e sert vegetation water
Figure 6 - Calculatedindexes, 2000. Figure 7 - Calculated indexes, 2008.
C o n clu sio n . D esertification, a phenom enon related to land degradation in arid, sem i-arid and arid sub-hum id regions as a result o f clim ate change and hum an activities, is considered one o f the m ost serious environm ental and socio-econom ic problem s o f ou r tim e. The study o f its dynam ics should be accom panied by a full-scale com prehensive study and ground-based route observation data. H ow ever, the observation o f the underlying surface, th at is, vegetation and open soil cover, as one o f the m ain com ponents o f this com plex process, is to som e extent possible w hen using Earth rem ote sensing data.
This study perform ed several tasks related to desertification degradation. The m ain objectives are to increase the area o f the A ralkum desert due to changes in the volum e o f the A ral Sea from y ear to y ear and determ ine the reduction o f vegetation cover. B ased on tw o-year satellite im ages, it is clear th at the distribution o f vegetation in the A ralkum desert is relatively sm all.For exam ple, in 2008, the area o f vegetation w as reduced to 1175 км 2 com pared w ith estim ates o f 2000. The area o f sea w ater decreased by 13 849 км 2 com pared w ith 2000. The current state o f the youngest desert in the w orld o f A ralkum is in the w orst condition. Due to quantitative indicators o f the process, salinization o f the A ralkum desert m akes it clear th at the area o f deserts is increasing. This is due to the fact th at in 2000 the desert area was 33 354 км 2, and in 2008 60 225 км 2, th at is, it increased to 26871 км 2. Due to the drying o f sea w ater, som e areas have degraded, and the risk o f salinization is increasing. A s a result o f the research, the distribution o f vegetation cover, the risk o f degradation and the dynam ics o f the developm ent o f sea w ater w ere determ ined. A nd also the dynam ics o f changes in the objects o f research is show n in the diagram and com piled, and m aps o f the degradation o f desertification o f the A ralkum in 2000 and 2008, at a scale o f
1:2 500 000.
Г.Е. Сэдуова1, Г.Т. И санова1,2, Е.Х. К аким ж анов1, Дж. Абудувайли3 1 Эл-Фараби атындагы казак ^лттык университет^
Г еография жэне табигатты пайдалану факультет^ Алматы, Казакстан;
2 Орталык Азия экология жэне коршаган орта гылыми-зерттеу орталыгы, Алматы, Казакстан;
3 Синьцзян экология жэне география институты, Кытай гылым академиясы, Кытай А РА Л Ц ¥М Ш в Л Ш Щ д е г р а д а ц и я с ы н з е р т т е у ж э н е к а р т о г р а ф и я л а у
Аннотация. Шелейттену - эртYрлi факторлардыц, соныц ш в д е климаттыц eзгеруi мен адамныц ю- эрекет нэтижеанде к¥ргак, шелейт жэне к¥ргак субгумидл жерлердеп алкаптардыц деградациясы. Б^л жагдайда «жер» термиш топырактан, судан, еамджтерден, баска да биомассадан т^ратын биопродуктивп жYЙенi, сондай-ак жYЙенщ iшiндегi экологиялык жэне гидрологиялык процестердi бiлдiредi. Жердщ
деградациясы - жердi пайдалану нэтижесiнде еп с п к жерлердщ немесе жайылымдардын биологиялык жэне экономикальщ eнiмдiлiгiнiн тeмeндeуi немесе жогалуы. Ол аз мелшерде жермен, eciмдiктeрдiн батпак- тануымен, топырак бiртeктiлiгiнiн тeмeндeуiмeн сипатталады, нэтижeciндe жылдам жел эрозиясы болуы мумк1н.
Арал тeнiзi - 6ip кeздeрi ен ipi кел деп аталса, казiр тартылган, тузды кел, ол бупнде улкен тузды шелге уксайды. Онын тартылуы бiздiн заманымыздагы орасан курдел1 экологиялык апаттардын бiрi болып сана- лады, eйткeнi осыдан 50 жыл бурын ол бiздiн планетамыздагы ен iрi терт келдщ бiрi болган. 1990 жылдары батыс элeмi экологиялык апат туралы элемдеп тeртiншi iрi кел - Арал тeнiзi туралы бiлдi. Арал тещзшщ курт тартылуы аймакта шелейттену процестершщ каркынды дамуына жэне жана антропогендж Аралкум шeлiнiн пайда болуына экелдг
Шелейттену процecтeрiн жэне жeрдiн деградация процестерш аныктау мониторингiнiн нагыз тэсш не мыналар жатады: кашыктыктан зондтау деректерше нeгiздeлгeн бакылау байкауынын орындылыгын айкындайтын табиги объeктiлeрдiн, шелейттену процecтeрiнiн немесе кубылыстарынын (индикаторлары- нын) тiзбeci; осы ecкeртулeрдi жYргiзу кeзeцдeрi мен мeрзiмдeрi; жYЙeлi турде жэне кепжылдык непзде ерк1н таратылатын гарыштык кeрiнicтeрдiн турлер^ оларга бакылау жYргiзу усынылады; бакылаудын нeгiзгi кeзeндeрi, эрбiр байкалган индикатор бойынша шыгыс нэтижeлeрiнiн геоакпараттык нысаны жэне eзгeрicтeр динамикасын багалау.
Аралкум шелшщ деградациясын аныктайтын нeгiзгi эдю - индeкcтeрдi есептеу. Тандалган эдютер кен турде танымал жэне элемдш тэж1рибеде бiрнeшe рет cпутниктiк cурeттeрдi ендеуде колданылады. Сонымен, аталган барлык cпeктрлiк керсетк1штер эмбебап болып келедг Бул эдicтiн бiрeгeйлiгi шeлeйттeнудiн эртурл1 кeрceткiштeрiн аныктауда cпeктрлiк кeрceткiштeрдiн эркайсысы Yшiн накты мэндер аукымын аныктауда жатыр. Аралкум шeлiнiн шeлeйттeнуi мен деградациялануынын гарыштык мониторинг эдicтeмeciндeгi орындалган шeшушi кадамдар: cпутниктiк суретке тапсырыс беру, оларды алдын ала ендеу; cпeктрлiк кeрceткiштeрдi есептеу; визуалды бажайлау; индекс мэндeрiнiн кажетп диапазонын белу; шелдену процeciнiн индeкcтiк мэндeрi непзщде карта курастыру; жeрдeгi мэлiмeттeрдi пайдаланып, карталарды тексеру; алынган нэтижeлeрдi талдау.
Тушн сездер: деградация, шелдену, гарыштык суреттер, Landsat-5, NDVI,SI, TCT.
Г.Е. Садуова1, Г.Т. И санова12, Е.Х. К аким ж анов1, Дж. Абудувайли3 1 Казахский национальный университет им. аль-Фараби,
Факультет географии и природопользования, Алматы, Казахстан;
2 Научно-исследовательский центр экологии и окружающей среды Центральной Азии, Алматы, Казахстан;
3 Синьцзянский институт экологии и географии Китайской академии наук, Китай КАРТОГРАФ ИРОВАНИЕ И ИССЛЕДОВАНИЕ ДЕГРАДАЦИИ
П У СТЫ Н И АРАЛКУМ
Аннотация. Опустынивание - деградация земель в засушливых, полузасушливых и сухих субгумидных районах в результате действия различных факторов, включая изменение климата и деятельность человека.
Понятие «земля» в данном случае означает биопродуктивную систему, состоящую из почвы, воды, растительности, прочей биомассы, а также экологические и гидрологические процессы внутри системы.
Деградация земель - снижение или потеря биологической и экономической продуктивности пахотных земель или пастбищ в результате землепользования. Характеризуется маленьким количеством земли, увяданием растительности, снижением связанности почвы, в результате чего становится возможной быстрая ветровая эрозия.
Аральским морем называют некогда большое, а сейчас практически высохшее, соленое озеро, которое сегодня выглядит как большая соляная пустыня. Его пересыхание считается одной из самых катастрофических экологических проблем современности, хотя 50 лет назад оно входило в четверку самых больших озер нашей планеты. В 1990-х годах западный мир узнал об экологической катастрофе того, что когда-то было четвертым по величине озером в мире - Аральским морем. Резкое высыхание Аральского моря привело к интенсивному развитию процессов опустынивания в регионе и формированию новой антропогенной пустыни Аралкум.
Реальным способом мониторинга процессов опустынивания и процессов деградации земель является перечень природных объектов, процессов или явлений опустынивания (индикаторов), которые определяют осуществимость контрольных наблюдений на основе данных дистанционного зондирования; сроки и сроки этих замечаний; типы космических снимков, которые распространяются свободно на регулярной и долгосрочной основе, рекомендуется их мониторинг; основные этапы контроля, геоинформационная форма результатов расходов по каждому наблюдаемому показателю и динамика изменений.
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Основным методом определения деградации пустыни Аралкум является расчет индексов. Выбранные методы являются широко известными и неоднократно примененными в мировой практике обработки космических снимков. Так, все перечисленные спектральные индексы являются универсальными.
Уникальность же данной методики состоит в определении точных диапазонов значений по каждому из спектральных индексов при идентификации различных индикаторов опустынивания. Выполнены ключевые этапы методики космического мониторинга процессов опустынивания и деградации пустыни Аралкума:
заказ космических снимков, их предварительная обработка; расчет спектральных индексов; визуальное дешифрирование; выделение необходимых диапазонов значений индексов; 1составление на основе значений индексов карт процессов опустынивания; верификация карт по наземным данным; анализ полученных результатов.
К лю чевы е слова: деградация, опустынивание, космические снимки, Landsat-5, NDVI,SI, TCT.
Information about authors:
SaduovaGulmira, master student at the Al-Farabi Kazakh National University, Faculty of Geography and Environment, [email protected], https://orcid.org/0000-0002-8479-8026;
Issanova Gulnura, PhD in Natural Sciences (Physical Geography), scientific secretary at the Research Centerfor Ecology and Environment of Central Asia (Almaty) and PostDoc at the Al-Farabi Kazakh National University, [email protected];
https://orcid.org/0000-0002-4496-0463;
KakimzhanovYerkin is a PhD, lecturer at the Al-Farabi Kazakh National University; [email protected];
https://orcid.org/0000-0001-6454-681X;
Jilili Abuduwailiis a Doctor in Geography, Professor, Deputy Director at the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, an Executive Director at the Research Center for Ecology and Environment of Central Asia (Almaty), and a foreign academician of the Academy of Agriculture of Kazakhstan, [email protected]; https://orcid.org/0000- 0001-8483-1554
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